WorldWideScience

Sample records for prices forecast comparison aeo

  1. Average regional end-use energy price projections to the year 2030

    International Nuclear Information System (INIS)

    1991-01-01

    The energy prices shown in this report cover the period from 1991 through 2030. These prices reflect sector/fuel price projections from the Annual Energy Outlook 1991 (AEO) base case, developed using the Energy Information Administration's (EIA) Intermediate Future Forecasting System (IFFS) forecasting model. Projections through 2010 are AEO base case forecasts. Projections for the period from 2011 through 2030 were developed separately from the AEO for this report, and the basis for these projections is described in Chapter 3. Projections in this report include average energy prices for each of four Census Regions for the residential, commercial, industrial, and transportation end-use sectors. Energy sources include electricity, distillate fuel oil, liquefied petroleum gas, motor gasoline, residual fuel oil, natural gas, and steam coal. (VC)

  2. Issues in midterm analysis and forecasting 1998

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1998-07-01

    Issues in Midterm Analysis and Forecasting 1998 (Issues) presents a series of nine papers covering topics in analysis and modeling that underlie the Annual Energy Outlook 1998 (AEO98), as well as other significant issues in midterm energy markets. AEO98, DOE/EIA-0383(98), published in December 1997, presents national forecasts of energy production, demand, imports, and prices through the year 2020 for five cases -- a reference case and four additional cases that assume higher and lower economic growth and higher and lower world oil prices than in the reference case. The forecasts were prepared by the Energy Information Administration (EIA), using EIA`s National Energy Modeling System (NEMS). The papers included in Issues describe underlying analyses for the projections in AEO98 and the forthcoming Annual Energy Outlook 1999 and for other products of EIA`s Office of Integrated Analysis and Forecasting. Their purpose is to provide public access to analytical work done in preparation for the midterm projections and other unpublished analyses. Specific topics were chosen for their relevance to current energy issues or to highlight modeling activities in NEMS. 59 figs., 44 tabs.

  3. Forecasting metal prices: Do forecasters herd?

    DEFF Research Database (Denmark)

    Pierdzioch, C.; Rulke, J. C.; Stadtmann, G.

    2013-01-01

    We analyze more than 20,000 forecasts of nine metal prices at four different forecast horizons. We document that forecasts are heterogeneous and report that anti-herding appears to be a source of this heterogeneity. Forecaster anti-herding reflects strategic interactions among forecasters...

  4. Annual energy outlook 1994: With projections to 2010

    Energy Technology Data Exchange (ETDEWEB)

    1994-01-01

    The Annual Energy Outlook 1994 (AEO94) presents the midterm energy forecasts of the Energy Information Administration (EIA). This year`s report presents projects and analyses of energy supply, demand, and prices through 2010, based for the first time on results from the National Energy Modeling System (NEMS). NEMS is the latest in a series of computer-based energy modeling systems used over the past 2 decades by EIA and its predecessor organization, the Federal Energy Administration, to analyze and forecast energy consumption and supply in the midterm period (about 20 years). Quarterly forecasts of energy supply and demand for 1994 and 1995 are published in the Short-Term Energy Outlook (February 1994). Forecast tables for 2000, 2005, and 2010 for each of the five scenarios examined in the AEO94 are provided in Appendices A through E. The five scenarios include a reference case and four additional cases that assume higher and lower economic growth and higher and lower world oil prices. Appendix F provides detailed comparisons of the AEO94 forecasts with those of other organizations. Appendix G briefly described the NEMS and the major AEO94 forecast assumptions. Appendix H summarizes the key results for the five scenarios.

  5. Assumptions for the Annual Energy Outlook 1992

    International Nuclear Information System (INIS)

    1992-01-01

    This report serves a auxiliary document to the Energy Information Administration (EIA) publication Annual Energy Outlook 1992 (AEO) (DOE/EIA-0383(92)), released in January 1992. The AEO forecasts were developed for five alternative cases and consist of energy supply, consumption, and price projections by major fuel and end-use sector, which are published at a national level of aggregation. The purpose of this report is to present important quantitative assumptions, including world oil prices and macroeconomic growth, underlying the AEO forecasts. The report has been prepared in response to external requests, as well as analyst requirements for background information on the AEO and studies based on the AEO forecasts

  6. An empirical comparison of alternative schemes for combining electricity spot price forecasts

    International Nuclear Information System (INIS)

    Nowotarski, Jakub; Raviv, Eran; Trück, Stefan; Weron, Rafał

    2014-01-01

    In this comprehensive empirical study we critically evaluate the use of forecast averaging in the context of electricity prices. We apply seven averaging and one selection scheme and perform a backtesting analysis on day-ahead electricity prices in three major European and US markets. Our findings support the additional benefit of combining forecasts of individual methods for deriving more accurate predictions, however, the performance is not uniform across the considered markets and periods. In particular, equally weighted pooling of forecasts emerges as a simple, yet powerful technique compared with other schemes that rely on estimated combination weights, but only when there is no individual predictor that consistently outperforms its competitors. Constrained least squares regression (CLS) offers a balance between robustness against such well performing individual methods and relatively accurate forecasts, on average better than those of the individual predictors. Finally, some popular forecast averaging schemes – like ordinary least squares regression (OLS) and Bayesian Model Averaging (BMA) – turn out to be unsuitable for predicting day-ahead electricity prices. - Highlights: • So far the most extensive study on combining forecasts for electricity spot prices • 12 stochastic models, 8 forecast combination schemes and 3 markets considered • Our findings support the additional benefit of combining forecasts for deriving more accurate predictions • Methods that allow for unconstrained weights, such as OLS averaging, should be avoided • We recommend a backtesting exercise to identify the preferred forecast averaging method for the data at hand

  7. Accounting for fuel price risk: Using forward natural gas prices instead of gas price forecasts to compare renewable to natural gas-fired generation

    Energy Technology Data Exchange (ETDEWEB)

    Bolinger, Mark; Wiser, Ryan; Golove, William

    2003-08-13

    Against the backdrop of increasingly volatile natural gas prices, renewable energy resources, which by their nature are immune to natural gas fuel price risk, provide a real economic benefit. Unlike many contracts for natural gas-fired generation, renewable generation is typically sold under fixed-price contracts. Assuming that electricity consumers value long-term price stability, a utility or other retail electricity supplier that is looking to expand its resource portfolio (or a policymaker interested in evaluating different resource options) should therefore compare the cost of fixed-price renewable generation to the hedged or guaranteed cost of new natural gas-fired generation, rather than to projected costs based on uncertain gas price forecasts. To do otherwise would be to compare apples to oranges: by their nature, renewable resources carry no natural gas fuel price risk, and if the market values that attribute, then the most appropriate comparison is to the hedged cost of natural gas-fired generation. Nonetheless, utilities and others often compare the costs of renewable to gas-fired generation using as their fuel price input long-term gas price forecasts that are inherently uncertain, rather than long-term natural gas forward prices that can actually be locked in. This practice raises the critical question of how these two price streams compare. If they are similar, then one might conclude that forecast-based modeling and planning exercises are in fact approximating an apples-to-apples comparison, and no further consideration is necessary. If, however, natural gas forward prices systematically differ from price forecasts, then the use of such forecasts in planning and modeling exercises will yield results that are biased in favor of either renewable (if forwards < forecasts) or natural gas-fired generation (if forwards > forecasts). In this report we compare the cost of hedging natural gas price risk through traditional gas-based hedging instruments (e

  8. The Combination Forecasting of Electricity Price Based on Price Spikes Processing: A Case Study in South Australia

    Directory of Open Access Journals (Sweden)

    Jianzhou Wang

    2014-01-01

    Full Text Available Electricity price forecasting holds very important position in the electricity market. Inaccurate price forecasting may cause energy waste and management chaos in the electricity market. However, electricity price forecasting has always been regarded as one of the largest challenges in the electricity market because it shows high volatility, which makes electricity price forecasting difficult. This paper proposes the use of artificial intelligence optimization combination forecasting models based on preprocessing data, called “chaos particles optimization (CPSO weight-determined combination models.” These models allow for the weight of the combined model to take values of [-1,1]. In the proposed models, the density-based spatial clustering of applications with noise (DBSCAN algorithm is used to identify outliers, and the outliers are replaced by a new data-produced linear interpolation function. The proposed CPSO weight-determined combination models are then used to forecast the projected future electricity price. In this case study, the electricity price data of South Australia are simulated. The results indicate that, while the weight of the combined model takes values of [-1,1], the proposed combination model can always provide adaptive, reliable, and comparatively accurate forecast results in comparison to traditional combination models.

  9. House Price Forecasts, Forecaster Herding, and the Recent Crisis

    DEFF Research Database (Denmark)

    Stadtmann, Georg; Pierdzioch; Ruelke

    2013-01-01

    We used the Wall Street Journal survey data for the period 2006–2012 to analyze whether forecasts of house prices and housing starts provide evidence of (anti-)herding of forecasters. Forecasts are consistent with herding (anti-herding) of forecasters if forecasts are biased towards (away from) t......) the consensus forecast. We found that anti-herding is prevalent among forecasters of house prices. We also report that, following the recent crisis, the prevalence of forecaster anti-herding seems to have changed over time....

  10. House Price Forecasts, Forecaster Herding, and the Recent Crisis

    Directory of Open Access Journals (Sweden)

    Christian Pierdzioch

    2012-11-01

    Full Text Available We used the Wall Street Journal survey data for the period 2006–2012 to analyze whether forecasts of house prices and housing starts provide evidence of (anti-herding of forecasters. Forecasts are consistent with herding (anti-herding of forecasters if forecasts are biased towards (away from the consensus forecast. We found that anti-herding is prevalent among forecasters of house prices. We also report that, following the recent crisis, the prevalence of forecaster anti-herding seems to have changed over time.

  11. Short-term electricity price forecast based on the improved hybrid model

    International Nuclear Information System (INIS)

    Dong Yao; Wang Jianzhou; Jiang He; Wu Jie

    2011-01-01

    Highlights: → The proposed models can detach high volatility and daily seasonality of electricity price. → The improved hybrid forecast models can make full use of the advantages of individual models. → The proposed models create commendable improvements that are relatively satisfactorily for current research. → The proposed models do not require making complicated decisions about the explicit form. - Abstract: Half-hourly electricity price in power system are volatile, electricity price forecast is significant information which can help market managers and participants involved in electricity market to prepare their corresponding bidding strategies to maximize their benefits and utilities. However, the fluctuation of electricity price depends on the common effect of many factors and there is a very complicated random in its evolution process. Therefore, it is difficult to forecast half-hourly prices with traditional only one model for different behaviors of half-hourly prices. This paper proposes the improved forecasting model that detaches high volatility and daily seasonality for electricity price of New South Wales in Australia based on Empirical Mode Decomposition, Seasonal Adjustment and Autoregressive Integrated Moving Average. The prediction errors are analyzed and compared with the ones obtained from the traditional Seasonal Autoregressive Integrated Moving Average model. The comparisons demonstrate that the proposed model can improve the prediction accuracy noticeably.

  12. Short-term electricity price forecast based on the improved hybrid model

    Energy Technology Data Exchange (ETDEWEB)

    Dong Yao, E-mail: dongyao20051987@yahoo.cn [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Wang Jianzhou, E-mail: wjz@lzu.edu.cn [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China); Jiang He; Wu Jie [School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000 (China)

    2011-08-15

    Highlights: {yields} The proposed models can detach high volatility and daily seasonality of electricity price. {yields} The improved hybrid forecast models can make full use of the advantages of individual models. {yields} The proposed models create commendable improvements that are relatively satisfactorily for current research. {yields} The proposed models do not require making complicated decisions about the explicit form. - Abstract: Half-hourly electricity price in power system are volatile, electricity price forecast is significant information which can help market managers and participants involved in electricity market to prepare their corresponding bidding strategies to maximize their benefits and utilities. However, the fluctuation of electricity price depends on the common effect of many factors and there is a very complicated random in its evolution process. Therefore, it is difficult to forecast half-hourly prices with traditional only one model for different behaviors of half-hourly prices. This paper proposes the improved forecasting model that detaches high volatility and daily seasonality for electricity price of New South Wales in Australia based on Empirical Mode Decomposition, Seasonal Adjustment and Autoregressive Integrated Moving Average. The prediction errors are analyzed and compared with the ones obtained from the traditional Seasonal Autoregressive Integrated Moving Average model. The comparisons demonstrate that the proposed model can improve the prediction accuracy noticeably.

  13. New evidence of anti-herding of oil-price forecasters

    International Nuclear Information System (INIS)

    Pierdzioch, Christian; Ruelke, Jan Christoph; Stadtmann, Georg

    2010-01-01

    We used the oil-price forecasts of the Survey of Professional Forecasters published by the European Central Bank to analyze whether oil-price forecasters herd or anti-herd. Oil-price forecasts are consistent with herding (anti-herding) of forecasters if forecasts are biased towards (away from) the consensus forecast. Based on a new empirical test developed by Bernhardt et al. (J. Financ. Econ. 80: 657-675, 2006), we found strong evidence of anti-herding among oil-price forecasters. (author)

  14. Evaluating information in multiple horizon forecasts. The DOE's energy price forecasts

    International Nuclear Information System (INIS)

    Sanders, Dwight R.; Manfredo, Mark R.; Boris, Keith

    2009-01-01

    The United States Department of Energy's (DOE) quarterly price forecasts for energy commodities are examined to determine the incremental information provided at the one-through four-quarter forecast horizons. A direct test for determining information content at alternative forecast horizons, developed by Vuchelen and Gutierrez [Vuchelen, J. and Gutierrez, M.-I. 'A Direct Test of the Information Content of the OECD Growth Forecasts.' International Journal of Forecasting. 21(2005):103-117.], is used. The results suggest that the DOE's price forecasts for crude oil, gasoline, and diesel fuel do indeed provide incremental information out to three-quarters ahead, while natural gas and electricity forecasts are informative out to the four-quarter horizon. In contrast, the DOE's coal price forecasts at two-, three-, and four-quarters ahead provide no incremental information beyond that provided for the one-quarter horizon. Recommendations of how to use these results for making forecast adjustments is also provided. (author)

  15. Supplement to the annual energy outlook 1994

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1994-03-01

    This report is a companion document to the Annual Energy Outlook 1994 (AEO94), (DOE/EIA-0383(94)), released in Jan. 1994. Part I of the Supplement presents the key quantitative assumptions underlying the AEO94 projections, responding to requests by energy analysts for additional information on the forecasts. In Part II, the Supplement provides regional projections and other underlying details of the reference case projections in the AEO94. The AEO94 presents national forecasts of energy production, demand and prices through 2010 for five scenarios, including a reference case and four additional cases that assume higher and lower economic growth and higher and lower world oil prices. These forecasts are used by Federal, State, and local governments, trade associations, and other planners and decisionmakers in the public and private sectors.

  16. Electricity price forecasting in deregulated markets: A review and evaluation

    Energy Technology Data Exchange (ETDEWEB)

    Aggarwal, Sanjeev Kumar; Saini, Lalit Mohan; Kumar, Ashwani [Department of Electrical Engineering, National Institute of Technology, Kurukshetra, Haryana (India)

    2009-01-15

    The main methodologies used in electricity price forecasting have been reviewed in this paper. The following price-forecasting techniques have been covered: (i) stochastic time series, (ii) causal models, and (iii) artificial intelligence based models. The quantitative analysis of the work done by various authors has been presented based on (a) time horizon for prediction, (b) input variables, (c) output variables, (d) results, (e) data points used for analysis, (f) preprocessing technique employed, and (g) architecture of the model. The results have been presented in the form of tables for ease of comparison. Classification of various price-influencing factors used by different researchers has been done and put for reference. Application of various models as applied to different electricity markets is also presented for consideration. (author)

  17. Electricity price forecasting in deregulated markets: A review and evaluation

    International Nuclear Information System (INIS)

    Aggarwal, Sanjeev Kumar; Saini, Lalit Mohan; Kumar, Ashwani

    2009-01-01

    The main methodologies used in electricity price forecasting have been reviewed in this paper. The following price-forecasting techniques have been covered: (i) stochastic time series, (ii) causal models, and (iii) artificial intelligence based models. The quantitative analysis of the work done by various authors has been presented based on (a) time horizon for prediction, (b) input variables, (c) output variables, (d) results, (e) data points used for analysis, (f) preprocessing technique employed, and (g) architecture of the model. The results have been presented in the form of tables for ease of comparison. Classification of various price-influencing factors used by different researchers has been done and put for reference. Application of various models as applied to different electricity markets is also presented for consideration. (author)

  18. Density Forecasts of Crude-Oil Prices Using Option-Implied and ARCH-Type Models

    DEFF Research Database (Denmark)

    Tsiaras, Leonidas; Høg, Esben

      The predictive accuracy of competing crude-oil price forecast densities is investigated for the 1994-2006 period. Moving beyond standard ARCH models that rely exclusively on past returns, we examine the benefits of utilizing the forward-looking information that is embedded in the prices...... as for regions and intervals that are of special interest for the economic agent. We find that non-parametric adjustments of risk-neutral density forecasts perform significantly better than their parametric counterparts. Goodness-of-fit tests and out-of-sample likelihood comparisons favor forecast densities...

  19. Electricity price forecast using Combinatorial Neural Network trained by a new stochastic search method

    International Nuclear Information System (INIS)

    Abedinia, O.; Amjady, N.; Shafie-khah, M.; Catalão, J.P.S.

    2015-01-01

    Highlights: • Presenting a Combinatorial Neural Network. • Suggesting a new stochastic search method. • Adapting the suggested method as a training mechanism. • Proposing a new forecast strategy. • Testing the proposed strategy on real-world electricity markets. - Abstract: Electricity price forecast is key information for successful operation of electricity market participants. However, the time series of electricity price has nonlinear, non-stationary and volatile behaviour and so its forecast method should have high learning capability to extract the complex input/output mapping function of electricity price. In this paper, a Combinatorial Neural Network (CNN) based forecasting engine is proposed to predict the future values of price data. The CNN-based forecasting engine is equipped with a new training mechanism for optimizing the weights of the CNN. This training mechanism is based on an efficient stochastic search method, which is a modified version of chemical reaction optimization algorithm, giving high learning ability to the CNN. The proposed price forecast strategy is tested on the real-world electricity markets of Pennsylvania–New Jersey–Maryland (PJM) and mainland Spain and its obtained results are extensively compared with the results obtained from several other forecast methods. These comparisons illustrate effectiveness of the proposed strategy.

  20. Effect of the accuracy of price forecasting on profit in a Price Based Unit Commitment

    International Nuclear Information System (INIS)

    Delarue, Erik; Van Den Bosch, Pieterjan; D'haeseleer, William

    2010-01-01

    This paper discusses and quantifies the so-called loss of profit (i.e., the sub-optimality of profit) that can be expected in a Price Based Unit Commitment (PBUC), when incorrect price forecasts are used. For this purpose, a PBUC model has been developed and utilized, using Mixed Integer Linear Programming (MILP). Simulations are used to determine the relationship between the Mean Absolute Percentage Error (MAPE) of a certain price forecast and the loss of profit, for four different types of power plants. A Combined Cycle (CC) power plant and a pumped storage unit show highest sensitivity to incorrect forecasts. A price forecast with a MAPE of 15%, on average, yields 13.8% and 12.1% profit loss, respectively. A classic thermal power plant (coal fired) and cascade hydro unit are less affected by incorrect forecasts, with only 2.4% and 2.0% profit loss, respectively, at the same price forecast MAPE. This paper further demonstrates that if price forecasts show an average bias (upward or downward), using the MAPE as measure of the price forecast might not be sufficient to quantify profit loss properly. Profit loss in this case has been determined as a function of both shift and MAPE of the price forecast. (author)

  1. A Statistical Approach for Interval Forecasting of the Electricity Price

    DEFF Research Database (Denmark)

    Zhao, Jun Hua; Dong, Zhao Yang; Xu, Zhao

    2008-01-01

    the prediction interval is essential for estimating the uncertainty involved in the price and thus is highly useful for making generation bidding strategies and investment decisions. In this paper, a novel data mining-based approach is proposed to achieve two major objectives: 1) to accurately forecast the value......Electricity price forecasting is a difficult yet essential task for market participants in a deregulated electricity market. Rather than forecasting the value, market participants are sometimes more interested in forecasting the prediction interval of the electricity price. Forecasting...... of the electricity price series, which is widely accepted as a nonlinear time series; 2) to accurately estimate the prediction interval of the electricity price series. In the proposed approach, support vector machine (SVM) is employed to forecast the value of the price. To forecast the prediction interval, we...

  2. Price forecasting of day-ahead electricity markets using a hybrid forecast method

    International Nuclear Information System (INIS)

    Shafie-khah, M.; Moghaddam, M. Parsa; Sheikh-El-Eslami, M.K.

    2011-01-01

    Research highlights: → A hybrid method is proposed to forecast the day-ahead prices in electricity market. → The method combines Wavelet-ARIMA and RBFN network models. → PSO method is applied to obtain optimum RBFN structure for avoiding over fitting. → One of the merits of the proposed method is lower need to the input data. → The proposed method has more accurate behavior in compare with previous methods. -- Abstract: Energy price forecasting in a competitive electricity market is crucial for the market participants in planning their operations and managing their risk, and it is also the key information in the economic optimization of the electric power industry. However, price series usually have a complex behavior due to their nonlinearity, nonstationarity, and time variancy. In this paper, a novel hybrid method to forecast day-ahead electricity price is proposed. This hybrid method is based on wavelet transform, Auto-Regressive Integrated Moving Average (ARIMA) models and Radial Basis Function Neural Networks (RBFN). The wavelet transform provides a set of better-behaved constitutive series than price series for prediction. ARIMA model is used to generate a linear forecast, and then RBFN is developed as a tool for nonlinear pattern recognition to correct the estimation error in wavelet-ARIMA forecast. Particle Swarm Optimization (PSO) is used to optimize the network structure which makes the RBFN be adapted to the specified training set, reducing computation complexity and avoiding overfitting. The proposed method is examined on the electricity market of mainland Spain and the results are compared with some of the most recent price forecast methods. The results show that the proposed hybrid method could provide a considerable improvement for the forecasting accuracy.

  3. Price forecasting of day-ahead electricity markets using a hybrid forecast method

    Energy Technology Data Exchange (ETDEWEB)

    Shafie-khah, M., E-mail: miadreza@gmail.co [Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Moghaddam, M. Parsa, E-mail: parsa@modares.ac.i [Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Sheikh-El-Eslami, M.K., E-mail: aleslam@modares.ac.i [Tarbiat Modares University, Tehran (Iran, Islamic Republic of)

    2011-05-15

    Research highlights: {yields} A hybrid method is proposed to forecast the day-ahead prices in electricity market. {yields} The method combines Wavelet-ARIMA and RBFN network models. {yields} PSO method is applied to obtain optimum RBFN structure for avoiding over fitting. {yields} One of the merits of the proposed method is lower need to the input data. {yields} The proposed method has more accurate behavior in compare with previous methods. -- Abstract: Energy price forecasting in a competitive electricity market is crucial for the market participants in planning their operations and managing their risk, and it is also the key information in the economic optimization of the electric power industry. However, price series usually have a complex behavior due to their nonlinearity, nonstationarity, and time variancy. In this paper, a novel hybrid method to forecast day-ahead electricity price is proposed. This hybrid method is based on wavelet transform, Auto-Regressive Integrated Moving Average (ARIMA) models and Radial Basis Function Neural Networks (RBFN). The wavelet transform provides a set of better-behaved constitutive series than price series for prediction. ARIMA model is used to generate a linear forecast, and then RBFN is developed as a tool for nonlinear pattern recognition to correct the estimation error in wavelet-ARIMA forecast. Particle Swarm Optimization (PSO) is used to optimize the network structure which makes the RBFN be adapted to the specified training set, reducing computation complexity and avoiding overfitting. The proposed method is examined on the electricity market of mainland Spain and the results are compared with some of the most recent price forecast methods. The results show that the proposed hybrid method could provide a considerable improvement for the forecasting accuracy.

  4. Forecasting short-term power prices in the Ontario Electricity Market (OEM) with a fuzzy logic based inference system

    International Nuclear Information System (INIS)

    Arciniegas, Alvaro I.; Arciniegas Rueda, Ismael E.

    2008-01-01

    The Ontario Electricity Market (OEM), which opened in May 2002, is relatively new and is still under change. In addition, the bidding strategies of the participants are such that the relationships between price and fundamentals are non-linear and dynamic. The lack of market maturity and high complexity hinders the use of traditional statistical methodologies (e.g., regression analysis) for price forecasting. Therefore, a flexible model is needed to achieve good forecasting in OEM. This paper uses a Takagi-Sugeno-Kang (TSK) fuzzy inference system in forecasting the one-day-ahead real-time peak price of the OEM. The forecasting results of TSK are compared with those obtained by traditional statistical and neural network based forecasting. The comparison suggests that TSK has considerable value in forecasting one-day-ahead peak price in OEM. (author)

  5. Application of Markov Model in Crude Oil Price Forecasting

    Directory of Open Access Journals (Sweden)

    Nuhu Isah

    2017-08-01

    Full Text Available Crude oil is an important energy commodity to mankind. Several causes have made crude oil prices to be volatile. The fluctuation of crude oil prices has affected many related sectors and stock market indices. Hence, forecasting the crude oil prices is essential to avoid the future prices of the non-renewable natural resources to rise. In this study, daily crude oil prices data was obtained from WTI dated 2 January to 29 May 2015. We used Markov Model (MM approach in forecasting the crude oil prices. In this study, the analyses were done using EViews and Maple software where the potential of this software in forecasting daily crude oil prices time series data was explored. Based on the study, we concluded that MM model is able to produce accurate forecast based on a description of history patterns in crude oil prices.

  6. Electricity Price Forecasting Based on AOSVR and Outlier Detection

    Institute of Scientific and Technical Information of China (English)

    Zhou Dianmin; Gao Lin; Gao Feng

    2005-01-01

    Electricity price is of the first consideration for all the participants in electric power market and its characteristics are related to both market mechanism and variation in the behaviors of market participants. It is necessary to build a real-time price forecasting model with adaptive capability; and because there are outliers in the price data, they should be detected and filtrated in training the forecasting model by regression method. In view of these points, this paper presents an electricity price forecasting method based on accurate on-line support vector regression (AOSVR) and outlier detection. Numerical testing results show that the method is effective in forecasting the electricity prices in electric power market.

  7. Forecasting prices and price volatility in the Nordic electricity market

    International Nuclear Information System (INIS)

    2001-01-01

    We develop a stochastic model for long term price forecasting in a competitive electricity market environment. It is demonstrated both theoretically and through model simulations that non-stochastic models may give biased forecasts both with respect to price level and volatility. In the paper, the model concept is applied on the restructured Nordic electricity market. It is specially in peak load hours that a stochastic model formulation provides significantly different results than an expected value model. (author)

  8. Boosting Learning Algorithm for Stock Price Forecasting

    Science.gov (United States)

    Wang, Chengzhang; Bai, Xiaoming

    2018-03-01

    To tackle complexity and uncertainty of stock market behavior, more studies have introduced machine learning algorithms to forecast stock price. ANN (artificial neural network) is one of the most successful and promising applications. We propose a boosting-ANN model in this paper to predict the stock close price. On the basis of boosting theory, multiple weak predicting machines, i.e. ANNs, are assembled to build a stronger predictor, i.e. boosting-ANN model. New error criteria of the weak studying machine and rules of weights updating are adopted in this study. We select technical factors from financial markets as forecasting input variables. Final results demonstrate the boosting-ANN model works better than other ones for stock price forecasting.

  9. Day ahead price forecasting of electricity markets by a mixed data model and hybrid forecast method

    International Nuclear Information System (INIS)

    Amjady, Nima; Keynia, Farshid

    2008-01-01

    In a competitive electricity market, forecast of energy prices is a key information for the market participants. However, price signal usually has a complex behavior due to its nonlinearity, nonstationarity, and time variancy. In spite of all performed researches on this area in the recent years, there is still an essential need for more accurate and robust price forecast methods. In this paper, a combination of wavelet transform (WT) and a hybrid forecast method is proposed for this purpose. The hybrid method is composed of cascaded forecasters where each forecaster consists of a neural network (NN) and an evolutionary algorithms (EA). Both time domain and wavelet domain features are considered in a mixed data model for price forecast, in which the candidate input variables are refined by a feature selection technique. The adjustable parameters of the whole method are fine-tuned by a cross-validation technique. The proposed method is examined on PJM electricity market and compared with some of the most recent price forecast methods. (author)

  10. A method for short term electricity spot price forecasting

    International Nuclear Information System (INIS)

    Koreneff, G.; Seppaelae, A.; Lehtonen, M.; Kekkonen, V.; Laitinen, E.; Haekli, J.; Antila, E.

    1998-01-01

    In Finland, the electricity market was de-regulated in November 1995. For the electricity purchase of power companies this has caused big changes, since the old tariff based contracts of bulk power supply have been replaced by negotiated bilateral short term contracts and by power purchase from the spot market. In the spot market, in turn, there are at the present two strong actors: The electricity exchange of Finland and the Nordic power pool which is run by the Swedish and Norwegian companies. Today, the power companies in Finland have short term trade with both of the electricity exchanges. The aim of this chapter is to present methods for spot price forecasting in the electricity exchange. The main focus is given to the Finnish circumstances. In the beginning of the presentation, the practices of the electricity exchange of Finland are described, and a brief presentation is given on the different contracts, or electricity products, available in the spot market. For comparison, the practices of the Nordic electricity exchange are also outlined. A time series technique for spot price forecasting is presented. The structure of the model is presented, and its validity is tested using real case data obtained from the Finnish power market. The spot price forecasting model is a part of a computer system for distribution energy management (DEM) in a de-regulated power market

  11. A method for short term electricity spot price forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Koreneff, G; Seppaelae, A; Lehtonen, M; Kekkonen, V [VTT Energy, Espoo (Finland); Laitinen, E; Haekli, J [Vaasa Univ. (Finland); Antila, E [ABB Transmit Oy (Finland)

    1998-08-01

    In Finland, the electricity market was de-regulated in November 1995. For the electricity purchase of power companies this has caused big changes, since the old tariff based contracts of bulk power supply have been replaced by negotiated bilateral short term contracts and by power purchase from the spot market. In the spot market, in turn, there are at the present two strong actors: The electricity exchange of Finland and the Nordic power pool which is run by the Swedish and Norwegian companies. Today, the power companies in Finland have short term trade with both of the electricity exchanges. The aim of this chapter is to present methods for spot price forecasting in the electricity exchange. The main focus is given to the Finnish circumstances. In the beginning of the presentation, the practices of the electricity exchange of Finland are described, and a brief presentation is given on the different contracts, or electricity products, available in the spot market. For comparison, the practices of the Nordic electricity exchange are also outlined. A time series technique for spot price forecasting is presented. The structure of the model is presented, and its validity is tested using real case data obtained from the Finnish power market. The spot price forecasting model is a part of a computer system for distribution energy management (DEM) in a de-regulated power market

  12. Formation of an Integrated Stock Price Forecast Model in Lithuania

    Directory of Open Access Journals (Sweden)

    Audrius Dzikevičius

    2016-12-01

    Full Text Available Technical and fundamental analyses are widely used to forecast stock prices due to lack of knowledge of other modern models and methods such as Residual Income Model, ANN-APGARCH, Support Vector Machine, Probabilistic Neural Network and Genetic Fuzzy Systems. Although stock price forecast models integrating both technical and fundamental analyses are currently used widely, their integration is not justified comprehensively enough. This paper discusses theoretical one-factor and multi-factor stock price forecast models already applied by investors at a global level and determines possibility to create and apply practically a stock price forecast model which integrates fundamental and technical analysis with the reference to the Lithuanian stock market. The research is aimed to determine the relationship between stock prices of the 14 Lithuanian companies listed in the Main List by the Nasdaq OMX Baltic and various fundamental variables. Based on correlation and regression analysis results and application of c-Squared Test, ANOVA method, a general stock price forecast model is generated. This paper discusses practical implications how the developed model can be used to forecast stock prices by individual investors and suggests additional check measures.

  13. Forecasting of electricity prices with neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Gareta, Raquel [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain); Romeo, Luis M. [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain)]. E-mail: luismi@unizar.es; Gil, Antonia [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain)

    2006-08-15

    During recent years, the electricity energy market deregulation has led to a new free competition situation in Europe and other countries worldwide. Generators, distributors and qualified clients have some uncertainties about the future evolution of electricity markets. In consequence, feasibility studies of new generation plants, design of new systems and energy management optimization are frequently postponed. The ability of forecasting energy prices, for instance the electricity prices, would be highly appreciated in order to improve the profitability of utility investments. The development of new simulation techniques, such as Artificial Intelligence (AI), has provided a good tool to forecast time series. In this paper, it is demonstrated that the Neural Network (NN) approach can be used to forecast short term hourly electricity pool prices (for the next day and two or three days after). The NN architecture and design for prices forecasting are described in this paper. The results are tested with extensive data sets, and good agreement is found between actual data and NN results. This methodology could help to improve power plant generation capacity management and, certainly, more profitable operation in daily energy pools.

  14. Forecasting of electricity prices with neural networks

    International Nuclear Information System (INIS)

    Gareta, Raquel; Romeo, Luis M.; Gil, Antonia

    2006-01-01

    During recent years, the electricity energy market deregulation has led to a new free competition situation in Europe and other countries worldwide. Generators, distributors and qualified clients have some uncertainties about the future evolution of electricity markets. In consequence, feasibility studies of new generation plants, design of new systems and energy management optimization are frequently postponed. The ability of forecasting energy prices, for instance the electricity prices, would be highly appreciated in order to improve the profitability of utility investments. The development of new simulation techniques, such as Artificial Intelligence (AI), has provided a good tool to forecast time series. In this paper, it is demonstrated that the Neural Network (NN) approach can be used to forecast short term hourly electricity pool prices (for the next day and two or three days after). The NN architecture and design for prices forecasting are described in this paper. The results are tested with extensive data sets, and good agreement is found between actual data and NN results. This methodology could help to improve power plant generation capacity management and, certainly, more profitable operation in daily energy pools

  15. Short-Term Price Forecasting Models Based on Artificial Neural Networks for Intraday Sessions in the Iberian Electricity Market

    Directory of Open Access Journals (Sweden)

    Claudio Monteiro

    2016-09-01

    Full Text Available This paper presents novel intraday session models for price forecasts (ISMPF models for hourly price forecasting in the six intraday sessions of the Iberian electricity market (MIBEL and the analysis of mean absolute percentage errors (MAPEs obtained with suitable combinations of their input variables in order to find the best ISMPF models. Comparisons of errors from different ISMPF models identified the most important variables for forecasting purposes. Similar analyses were applied to determine the best daily session models for price forecasts (DSMPF models for the day-ahead price forecasting in the daily session of the MIBEL, considering as input variables extensive hourly time series records of recent prices, power demands and power generations in the previous day, forecasts of demand, wind power generation and weather for the day-ahead, and chronological variables. ISMPF models include the input variables of DSMPF models as well as the daily session prices and prices of preceding intraday sessions. The best ISMPF models achieved lower MAPEs for most of the intraday sessions compared to the error of the best DSMPF model; furthermore, such DSMPF error was very close to the lowest limit error for the daily session. The best ISMPF models can be useful for MIBEL agents of the electricity intraday market and the electric energy industry.

  16. Structural change and forecasting long-run energy prices

    International Nuclear Information System (INIS)

    Bernard, J.T.; Khalaf, L.

    2004-01-01

    Fluctuating energy prices have a significant impact on the economies of industrialized nations. A recent study has shown a strong non-linear relationship between changes in oil prices and growth in gross domestic product (GDP). In order to forecast the behaviour of energy prices, a complete model must take into account domestic and international supply and demand conditions, market regulations, technological advances and geopolitics. In 1999, Pindyck suggested that for long-term forecasting, a simple model should be adopted where prices grow in real terms and at a fixed rate. This paper tests the statistical significance of Pindyck's suggested class of econometric equations that model the behaviour of long-run real energy prices. The models assume mean-reverting prices with continuous and random changes in their level and trend. They are estimated using Kalman filtering. The authors used simulation-based procedures to address the issue of non-standard test statistics and nuisance parameters. Results were reported for a standard Monte Carlo test and a maximized Monte Carlo test. Results shown statistically significant instabilities for coal and natural gas prices, but not for crude oil prices. Various models were differentiated using out-of-sample forecasting exercises. 25 refs., 3 tabs

  17. Supplement to the Annual Energy Outlook 1993

    International Nuclear Information System (INIS)

    1993-01-01

    The Supplement to the Annual Energy Outlook 1993 is a companion document to the Energy Information Administration's (EIA) Annual Energy Outlook 1993 (AEO). Supplement tables provide the regional projections underlying the national data and projections in the AEO. The domestic coal, electric power, commercial nuclear power, end-use consumption, and end-use price tables present AEO forecasts at the 10 Federal Region level. World coal tables provide data and projections on international flows of steam coal and metallurgical coal, and the oil and gas tables provide the AEO oil and gas supply forecasts by Oil and Gas Supply Regions and by source of supply. All tables refer to cases presented in the AEO, which provides a range of projections for energy markets through 2010

  18. Palm oil price forecasting model: An autoregressive distributed lag (ARDL) approach

    Science.gov (United States)

    Hamid, Mohd Fahmi Abdul; Shabri, Ani

    2017-05-01

    Palm oil price fluctuated without any clear trend or cyclical pattern in the last few decades. The instability of food commodities price causes it to change rapidly over time. This paper attempts to develop Autoregressive Distributed Lag (ARDL) model in modeling and forecasting the price of palm oil. In order to use ARDL as a forecasting model, this paper modifies the data structure where we only consider lagged explanatory variables to explain the variation in palm oil price. We then compare the performance of this ARDL model with a benchmark model namely ARIMA in term of their comparative forecasting accuracy. This paper also utilize ARDL bound testing approach to co-integration in examining the short run and long run relationship between palm oil price and its determinant; production, stock, and price of soybean as the substitute of palm oil and price of crude oil. The comparative forecasting accuracy suggests that ARDL model has a better forecasting accuracy compared to ARIMA.

  19. Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

    Directory of Open Access Journals (Sweden)

    Bijay Neupane

    2017-01-01

    Full Text Available Forecasting of electricity prices is important in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards the aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participates in forecasting 1-h ahead the price for each hour of a day. We propose two different strategies, namely, the Fixed Weight Method (FWM and the Varying Weight Method (VWM, for selecting each hour’s expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features extracted from the past electricity price data, weather data and calendar data. The proposed ensemble model offers better results than the Autoregressive Integrated Moving Average (ARIMA method, the Pattern Sequence-based Forecasting (PSF method and our previous work using Artificial Neural Networks (ANN alone on the datasets for New York, Australian and Spanish electricity markets.

  20. Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors

    Directory of Open Access Journals (Sweden)

    Claudio Monteiro

    2018-04-01

    Full Text Available This article presents original probabilistic price forecasting meta-models (PPFMCP models, by aggregation of competitive predictors, for day-ahead hourly probabilistic price forecasting. The best twenty predictors of the EEM2016 EPF competition are used to create ensembles of hourly spot price forecasts. For each hour, the parameter values of the probability density function (PDF of a Beta distribution for the output variable (hourly price can be directly obtained from the expected and variance values associated to the ensemble for such hour, using three aggregation strategies of predictor forecasts corresponding to three PPFMCP models. A Reliability Indicator (RI and a Loss function Indicator (LI are also introduced to give a measure of uncertainty of probabilistic price forecasts. The three PPFMCP models were satisfactorily applied to the real-world case study of the Iberian Electricity Market (MIBEL. Results from PPFMCP models showed that PPFMCP model 2, which uses aggregation by weight values according to daily ranks of predictors, was the best probabilistic meta-model from a point of view of mean absolute errors, as well as of RI and LI. PPFMCP model 1, which uses the averaging of predictor forecasts, was the second best meta-model. PPFMCP models allow evaluations of risk decisions based on the price to be made.

  1. A hybrid approach for probabilistic forecasting of electricity price

    DEFF Research Database (Denmark)

    Wan, Can; Xu, Zhao; Wang, Yelei

    2014-01-01

    to the nonstationarities involved in market clearing prices (MCPs), it is rather difficult to accurately predict MCPs in advance. The challenge is getting intensified as more and more renewable energy and other new technologies emerged in smart grids. Therefore transformation from traditional point forecasts...... electricity price forecasting is proposed in this paper. The effectiveness of the proposed hybrid method has been validated through comprehensive tests using real price data from Australian electricity market.......The electricity market plays a key role in realizing the economic prophecy of smart grids. Accurate and reliable electricity market price forecasting is essential to facilitate various decision making activities of market participants in the future smart grid environment. However, due...

  2. Probabilistic electricity price forecasting with variational heteroscedastic Gaussian process and active learning

    International Nuclear Information System (INIS)

    Kou, Peng; Liang, Deliang; Gao, Lin; Lou, Jianyong

    2015-01-01

    Highlights: • A novel active learning model for the probabilistic electricity price forecasting. • Heteroscedastic Gaussian process that captures the local volatility of the electricity price. • Variational Bayesian learning that avoids over-fitting. • Active learning algorithm that reduces the computational efforts. - Abstract: Electricity price forecasting is essential for the market participants in their decision making. Nevertheless, the accuracy of such forecasting cannot be guaranteed due to the high variability of the price data. For this reason, in many cases, rather than merely point forecasting results, market participants are more interested in the probabilistic price forecasting results, i.e., the prediction intervals of the electricity price. Focusing on this issue, this paper proposes a new model for the probabilistic electricity price forecasting. This model is based on the active learning technique and the variational heteroscedastic Gaussian process (VHGP). It provides the heteroscedastic Gaussian prediction intervals, which effectively quantify the heteroscedastic uncertainties associated with the price data. Because the high computational effort of VHGP hinders its application to the large-scale electricity price forecasting tasks, we design an active learning algorithm to select a most informative training subset from the whole available training set. By constructing the forecasting model on this smaller subset, the computational efforts can be significantly reduced. In this way, the practical applicability of the proposed model is enhanced. The forecasting performance and the computational time of the proposed model are evaluated using the real-world electricity price data, which is obtained from the ANEM, PJM, and New England ISO

  3. Electricity demand and spot price forecasting using evolutionary computation combined with chaotic nonlinear dynamic model

    International Nuclear Information System (INIS)

    Unsihuay-Vila, C.; Zambroni de Souza, A.C.; Marangon-Lima, J.W.; Balestrassi, P.P.

    2010-01-01

    This paper proposes a new hybrid approach based on nonlinear chaotic dynamics and evolutionary strategy to forecast electricity loads and prices. The main idea is to develop a new training or identification stage in a nonlinear chaotic dynamic based predictor. In the training stage five optimal parameters for a chaotic based predictor are searched through an optimization model based on evolutionary strategy. The objective function of the optimization model is the mismatch minimization between the multi-step-ahead forecasting of predictor and observed data such as it is done in identification problems. The first contribution of this paper is that the proposed approach is capable of capturing the complex dynamic of demand and price time series considered resulting in a more accuracy forecasting. The second contribution is that the proposed approach run on-line manner, i.e. the optimal set of parameters and prediction is executed automatically which can be used to prediction in real-time, it is an advantage in comparison with other models, where the choice of their input parameters are carried out off-line, following qualitative/experience-based recipes. A case study of load and price forecasting is presented using data from New England, Alberta, and Spain. A comparison with other methods such as autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) is shown. The results show that the proposed approach provides a more accurate and effective forecasting than ARIMA and ANN methods. (author)

  4. Forecasting Long-Run Electricity Prices

    International Nuclear Information System (INIS)

    Hamm, Gregory; Borison, Adam

    2006-01-01

    Estimation of long-run electricity prices is extremely important but it is also very difficult because of the many uncertainties that will determine future prices, and because of the lack of sufficient historical and forwards data. The difficulty is compounded when forecasters ignore part of the available information or unnecessarily limit their thinking about the future. The authors present a practical approach that addresses these problems. (author)

  5. Improving the Forecasting Accuracy of Crude Oil Prices

    Directory of Open Access Journals (Sweden)

    Xuluo Yin

    2018-02-01

    Full Text Available Currently, oil is the key element of energy sustainability, and its prices and economy have a strong mutual influence. Modeling a good method to accurately predict oil prices over long future horizons is challenging and of great interest to investors and policymakers. This paper forecasts oil prices using many predictor variables with a new time-varying weight combination approach. In doing so, we first use five single-variable time-varying parameter models to predict crude oil prices separately. Second, every special model is assigned a time-varying weight by the new combination approach. Finally, the forecasting results of oil prices are calculated. The results show that the paper’s method is robust and performs well compared to random walk.

  6. Forecasting electricity market pricing using artificial neural networks

    International Nuclear Information System (INIS)

    Pao, Hsiao-Tien

    2007-01-01

    Electricity price forecasting is extremely important for all market players, in particular for generating companies: in the short term, they must set up bids for the spot market; in the medium term, they have to define contract policies; and in the long term, they must define their expansion plans. For forecasting long-term electricity market pricing, in order to avoid excessive round-off and prediction errors, this paper proposes a new artificial neural network (ANN) with single output node structure by using direct forecasting approach. The potentials of ANNs are investigated by employing a rolling cross validation scheme. Out of sample performance evaluated with three criteria across five forecasting horizons shows that the proposed ANNs are a more robust multi-step ahead forecasting method than autoregressive error models. Moreover, ANN predictions are quite accurate even when the length of the forecast horizon is relatively short or long

  7. Forecasting Natural Rubber Price In Malaysia Using Arima

    Science.gov (United States)

    Zahari, Fatin Z.; Khalid, Kamil; Roslan, Rozaini; Sufahani, Suliadi; Mohamad, Mahathir; Saifullah Rusiman, Mohd; Ali, Maselan

    2018-04-01

    This paper contains introduction, materials and methods, results and discussions, conclusions and references. Based on the title mentioned, high volatility of the price of natural rubber nowadays will give the significant risk to the producers, traders, consumers, and others parties involved in the production of natural rubber. To help them in making decisions, forecasting is needed to predict the price of natural rubber. The main objective of the research is to forecast the upcoming price of natural rubber by using the reliable statistical method. The data are gathered from Malaysia Rubber Board which the data are from January 2000 until December 2015. In this research, average monthly price of Standard Malaysia Rubber 20 (SMR20) will be forecast by using Box-Jenkins approach. Time series plot is used to determine the pattern of the data. The data have trend pattern which indicates the data is non-stationary data and the data need to be transformed. By using the Box-Jenkins method, the best fit model for the time series data is ARIMA (1, 1, 0) which this model satisfy all the criteria needed. Hence, ARIMA (1, 1, 0) is the best fitted model and the model will be used to forecast the average monthly price of Standard Malaysia Rubber 20 (SMR20) for twelve months ahead.

  8. Short-term electricity prices forecasting in a competitive market: A neural network approach

    International Nuclear Information System (INIS)

    Catalao, J.P.S.; Mariano, S.J.P.S.; Mendes, V.M.F.; Ferreira, L.A.F.M.

    2007-01-01

    This paper proposes a neural network approach for forecasting short-term electricity prices. Almost until the end of last century, electricity supply was considered a public service and any price forecasting which was undertaken tended to be over the longer term, concerning future fuel prices and technical improvements. Nowadays, short-term forecasts have become increasingly important since the rise of the competitive electricity markets. In this new competitive framework, short-term price forecasting is required by producers and consumers to derive their bidding strategies to the electricity market. Accurate forecasting tools are essential for producers to maximize their profits, avowing profit losses over the misjudgement of future price movements, and for consumers to maximize their utilities. A three-layered feedforward neural network, trained by the Levenberg-Marquardt algorithm, is used for forecasting next-week electricity prices. We evaluate the accuracy of the price forecasting attained with the proposed neural network approach, reporting the results from the electricity markets of mainland Spain and California. (author)

  9. Forecasting spot electricity prices : Deep learning approaches and empirical comparison of traditional algorithms

    NARCIS (Netherlands)

    Lago Garcia, J.; De Ridder, Fjo; De Schutter, B.H.K.

    2018-01-01

    In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning

  10. Design and implementation of ticket price forecasting system

    Science.gov (United States)

    Li, Yuling; Li, Zhichao

    2018-05-01

    With the advent of the aviation travel industry, a large number of data mining technologies have been developed to increase profits for airlines in the past two decades. The implementation of the digital optimization strategy leads to price discrimination, for example, similar seats on the same flight are purchased at different prices, depending on the time of purchase, the supplier, and so on. Price fluctuations make the prediction of ticket prices have application value. In this paper, a combination of ARMA algorithm and random forest algorithm is proposed to predict the price of air ticket. The experimental results show that the model is more reliable by comparing the forecasting results with the actual results of each price model. The model is helpful for passengers to buy tickets and to save money. Based on the proposed model, using Python language and SQL Server database, we design and implement the ticket price forecasting system.

  11. Forecasting electricity spot-prices using linear univariate time-series models

    International Nuclear Information System (INIS)

    Cuaresma, Jesus Crespo; Hlouskova, Jaroslava; Kossmeier, Stephan; Obersteiner, Michael

    2004-01-01

    This paper studies the forecasting abilities of a battery of univariate models on hourly electricity spot prices, using data from the Leipzig Power Exchange. The specifications studied include autoregressive models, autoregressive-moving average models and unobserved component models. The results show that specifications, where each hour of the day is modelled separately present uniformly better forecasting properties than specifications for the whole time-series, and that the inclusion of simple probabilistic processes for the arrival of extreme price events can lead to improvements in the forecasting abilities of univariate models for electricity spot prices. (Author)

  12. Day-ahead price forecasting in restructured power systems using artificial neural networks

    International Nuclear Information System (INIS)

    Vahidinasab, V.; Jadid, S.; Kazemi, A.

    2008-01-01

    Over the past 15 years most electricity supply companies around the world have been restructured from monopoly utilities to deregulated competitive electricity markets. Market participants in the restructured electricity markets find short-term electricity price forecasting (STPF) crucial in formulating their risk management strategies. They need to know future electricity prices as their profitability depends on them. This research project classifies and compares different techniques of electricity price forecasting in the literature and selects artificial neural networks (ANN) as a suitable method for price forecasting. To perform this task, market knowledge should be used to optimize the selection of input data for an electricity price forecasting tool. Then sensitivity analysis is used in this research to aid in the selection of the optimum inputs of the ANN and fuzzy c-mean (FCM) algorithm is used for daily load pattern clustering. Finally, ANN with a modified Levenberg-Marquardt (LM) learning algorithm are implemented for forecasting prices in Pennsylvania-New Jersey-Maryland (PJM) market. The forecasting results were compared with the previous works and showed that the results are reasonable and accurate. (author)

  13. Crop Insurance Inaccurate FCIC Price Forecasts Increase Program Costs

    National Research Council Canada - National Science Library

    1991-01-01

    ...) how FCIC can improve its forecast accuracy. We found that FCIC's corn, wheat, and soybeans price forecasts exhibit large bias errors that exceed those of other available alternative forecasts and that FCIC would have spent...

  14. Forecasting electricity spot market prices with a k-factor GIGARCH process

    International Nuclear Information System (INIS)

    Diongue, Abdou Ka; Guegan, Dominique; Vignal, Bertrand

    2009-01-01

    In this article, we investigate conditional mean and conditional variance forecasts using a dynamic model following a k-factor GIGARCH process. Particularly, we provide the analytical expression of the conditional variance of the prediction error. We apply this method to the German electricity price market for the period August 15, 2000-December 31, 2002 and we test spot prices forecasts until one-month ahead forecast. The forecasting performance of the model is compared with a SARIMA-GARCH benchmark model using the year 2003 as the out-of-sample. The proposed model outperforms clearly the benchmark model. We conclude that the k-factor GIGARCH process is a suitable tool to forecast spot prices, using the classical RMSE criteria. (author)

  15. The long-run forecasting of energy prices using the model of shifting trend

    International Nuclear Information System (INIS)

    Radchenko, Stanislav

    2005-01-01

    Developing models for accurate long-term energy price forecasting is an important problem because these forecasts should be useful in determining both supply and demand of energy. On the supply side, long-term forecasts determine investment decisions of energy-related companies. On the demand side, investments in physical capital and durable goods depend on price forecasts of a particular energy type. Forecasting long-run rend movements in energy prices is very important on the macroeconomic level for several developing countries because energy prices have large impacts on their real output, the balance of payments, fiscal policy, etc. Pindyck (1999) argues that the dynamics of real energy prices is mean-reverting to trend lines with slopes and levels that are shifting unpredictably over time. The hypothesis of shifting long-term trend lines was statistically tested by Benard et al. (2004). The authors find statistically significant instabilities for coal and natural gas prices. I continue the research of energy prices in the framework of continuously shifting levels and slopes of trend lines started by Pindyck (1999). The examined model offers both parsimonious approach and perspective on the developments in energy markets. Using the model of depletable resource production, Pindyck (1999) argued that the forecast of energy prices in the model is based on the long-run total marginal cost. Because the model of a shifting trend is based on the competitive behavior, one may examine deviations of oil producers from the competitive behavior by studying the difference between actual prices and long-term forecasts. To construct the long-run forecasts (10-year-ahead and 15-year-ahead) of energy prices, I modify the univariate shifting trends model of Pindyck (1999). I relax some assumptions on model parameters, the assumption of white noise error term, and propose a new Bayesian approach utilizing a Gibbs sampling algorithm to estimate the model with autocorrelation. To

  16. Forecasting oil price trends using wavelets and hidden Markov models

    International Nuclear Information System (INIS)

    Souza e Silva, Edmundo G. de; Souza e Silva, Edmundo A. de; Legey, Luiz F.L.

    2010-01-01

    The crude oil price is influenced by a great number of factors, most of which interact in very complex ways. For this reason, forecasting it through a fundamentalist approach is a difficult task. An alternative is to use time series methodologies, with which the price's past behavior is conveniently analyzed, and used to predict future movements. In this paper, we investigate the usefulness of a nonlinear time series model, known as hidden Markov model (HMM), to predict future crude oil price movements. Using an HMM, we develop a forecasting methodology that consists of, basically, three steps. First, we employ wavelet analysis to remove high frequency price movements, which can be assumed as noise. Then, the HMM is used to forecast the probability distribution of the price return accumulated over the next F days. Finally, from this distribution, we infer future price trends. Our results indicate that the proposed methodology might be a useful decision support tool for agents participating in the crude oil market. (author)

  17. Oil prices. Brownian motion or mean reversion? A study using a one year ahead density forecast criterion

    International Nuclear Information System (INIS)

    Meade, Nigel

    2010-01-01

    For oil related investment appraisal, an accurate description of the evolving uncertainty in the oil price is essential. For example, when using real option theory to value an investment, a density function for the future price of oil is central to the option valuation. The literature on oil pricing offers two views. The arbitrage pricing theory literature for oil suggests geometric Brownian motion and mean reversion models. Empirically driven literature suggests ARMA-GARCH models. In addition to reflecting the volatility of the market, the density function of future prices should also incorporate the uncertainty due to price jumps, a common occurrence in the oil market. In this study, the accuracy of density forecasts for up to a year ahead is the major criterion for a comparison of a range of models of oil price behaviour, both those proposed in the literature and following from data analysis. The Kullbach Leibler information criterion is used to measure the accuracy of density forecasts. Using two crude oil price series, Brent and West Texas Intermediate (WTI) representing the US market, we demonstrate that accurate density forecasts are achievable for up to nearly two years ahead using a mixture of two Gaussians innovation processes with GARCH and no mean reversion. (author)

  18. Oil prices. Brownian motion or mean reversion? A study using a one year ahead density forecast criterion

    Energy Technology Data Exchange (ETDEWEB)

    Meade, Nigel [Imperial College, Business School London (United Kingdom)

    2010-11-15

    For oil related investment appraisal, an accurate description of the evolving uncertainty in the oil price is essential. For example, when using real option theory to value an investment, a density function for the future price of oil is central to the option valuation. The literature on oil pricing offers two views. The arbitrage pricing theory literature for oil suggests geometric Brownian motion and mean reversion models. Empirically driven literature suggests ARMA-GARCH models. In addition to reflecting the volatility of the market, the density function of future prices should also incorporate the uncertainty due to price jumps, a common occurrence in the oil market. In this study, the accuracy of density forecasts for up to a year ahead is the major criterion for a comparison of a range of models of oil price behaviour, both those proposed in the literature and following from data analysis. The Kullbach Leibler information criterion is used to measure the accuracy of density forecasts. Using two crude oil price series, Brent and West Texas Intermediate (WTI) representing the US market, we demonstrate that accurate density forecasts are achievable for up to nearly two years ahead using a mixture of two Gaussians innovation processes with GARCH and no mean reversion. (author)

  19. Electricity price forecasting using Enhanced Probability Neural Network

    International Nuclear Information System (INIS)

    Lin, Whei-Min; Gow, Hong-Jey; Tsai, Ming-Tang

    2010-01-01

    This paper proposes a price forecasting system for electric market participants to reduce the risk of price volatility. Combining the Probability Neural Network (PNN) and Orthogonal Experimental Design (OED), an Enhanced Probability Neural Network (EPNN) is proposed in the solving process. In this paper, the Locational Marginal Price (LMP), system load and temperature of PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday, and weekend. With the OED to smooth parameters in the EPNN, the forecasting error can be improved during the training process to promote the accuracy and reliability where even the ''spikes'' can be tracked closely. Simulation results show the effectiveness of the proposed EPNN to provide quality information in a price volatile environment. (author)

  20. Canadian natural gas price forecast

    International Nuclear Information System (INIS)

    Jones, D.

    1998-01-01

    The basic factors that influenced NYMEX gas prices during the winter of 1997/1998 - warm temperatures, low fuel prices, new production in the Gulf of Mexico, and the fact that forecasters had predicted a mild spring due to El Nino - were reviewed. However, it was noted that for the last 18 months the basic factors had less of an impact on market direction because of an increase in Fund and technical trader participation. Overall, gas prices were strong through most of the year. For the winter of 1998-1999 the prediction was that NYMEX gas prices will remain below $2.00 through to the end of October 1998 because of high U.S. storage levels and moderate temperatures. NYMEX gas prices are expected to peak in January 1999 at $3.25. AECO natural gas prices were predicted to decrease in the short term because of increasing levels of Canadian storage, and because of delays in Northern Border pipeline expansions. It was also predicted that AECO prices will peak in January 1999 and will remain relatively strong through the summer of 1999. tabs., figs

  1. Housing price forecastability: A factor analysis

    DEFF Research Database (Denmark)

    Møller, Stig Vinther; Bork, Lasse

    2017-01-01

    We examine U.S. housing price forecastability using principal component analysis (PCA), partial least squares (PLS), and sparse PLS (SPLS). We incorporate information from a large panel of 128 economic time series and show that macroeconomic fundamentals have strong predictive power for future...... movements in housing prices. We find that (S)PLS models systematically dominate PCA models. (S)PLS models also generate significant out-of-sample predictive power over and above the predictive power contained by the price-rent ratio, autoregressive benchmarks, and regression models based on small datasets....

  2. Stock price forecasting based on time series analysis

    Science.gov (United States)

    Chi, Wan Le

    2018-05-01

    Using the historical stock price data to set up a sequence model to explain the intrinsic relationship of data, the future stock price can forecasted. The used models are auto-regressive model, moving-average model and autoregressive-movingaverage model. The original data sequence of unit root test was used to judge whether the original data sequence was stationary. The non-stationary original sequence as a first order difference needed further processing. Then the stability of the sequence difference was re-inspected. If it is still non-stationary, the second order differential processing of the sequence is carried out. Autocorrelation diagram and partial correlation diagram were used to evaluate the parameters of the identified ARMA model, including coefficients of the model and model order. Finally, the model was used to forecast the fitting of the shanghai composite index daily closing price with precision. Results showed that the non-stationary original data series was stationary after the second order difference. The forecast value of shanghai composite index daily closing price was closer to actual value, indicating that the ARMA model in the paper was a certain accuracy.

  3. Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market

    Energy Technology Data Exchange (ETDEWEB)

    Mandal, Paras; Senjyu, Tomonobu [Department of Electrical and Electronics, University of the Ryukyus, 1 Senbaru, Nagakami Nishihara, Okinawa 903-0213 (Japan); Funabashi, Toshihisa [Meidensha Corporation, Tokyo 103-8515 (Japan)

    2006-09-15

    In daily power markets, forecasting electricity prices and loads are the most essential task and the basis for any decision making. An approach to predict the market behaviors is to use the historical prices, loads and other required information to forecast the future prices and loads. This paper introduces an approach for several hour ahead (1-6h) electricity price and load forecasting using an artificial intelligence method, such as a neural network model, which uses publicly available data from the NEMMCO web site to forecast electricity prices and loads for the Victorian electricity market. An approach of selection of similar days is proposed according to which the load and price curves are forecasted by using the information of the days being similar to that of the forecast day. A Euclidean norm with weighted factors is used for the selection of the similar days. Two different ANN models, one for one to six hour ahead load forecasting and another for one to six hour ahead price forecasting have been proposed. The MAPE (mean absolute percentage error) results show a clear increasing trend with the increase in hour ahead load and price forecasting. The sample average of MAPEs for one hour ahead price forecasts is 9.75%. This figure increases to only 20.03% for six hour ahead predictions. Similarly, the one to six hour ahead load forecast errors (MAPE) range from 0.56% to 1.30% only. MAPE results show that several hour ahead electricity prices and loads in the deregulated Victorian market can be forecasted with reasonable accuracy. (author)

  4. Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market

    International Nuclear Information System (INIS)

    Mandal, Paras; Senjyu, Tomonobu; Funabashi, Toshihisa

    2006-01-01

    In daily power markets, forecasting electricity prices and loads are the most essential task and the basis for any decision making. An approach to predict the market behaviors is to use the historical prices, loads and other required information to forecast the future prices and loads. This paper introduces an approach for several hour ahead (1-6 h) electricity price and load forecasting using an artificial intelligence method, such as a neural network model, which uses publicly available data from the NEMMCO web site to forecast electricity prices and loads for the Victorian electricity market. An approach of selection of similar days is proposed according to which the load and price curves are forecasted by using the information of the days being similar to that of the forecast day. A Euclidean norm with weighted factors is used for the selection of the similar days. Two different ANN models, one for one to six hour ahead load forecasting and another for one to six hour ahead price forecasting have been proposed. The MAPE (mean absolute percentage error) results show a clear increasing trend with the increase in hour ahead load and price forecasting. The sample average of MAPEs for one hour ahead price forecasts is 9.75%. This figure increases to only 20.03% for six hour ahead predictions. Similarly, the one to six hour ahead load forecast errors (MAPE) range from 0.56% to 1.30% only. MAPE results show that several hour ahead electricity prices and loads in the deregulated Victorian market can be forecasted with reasonable accuracy

  5. Mixed price and load forecasting of electricity markets by a new iterative prediction method

    International Nuclear Information System (INIS)

    Amjady, Nima; Daraeepour, Ali

    2009-01-01

    Load and price forecasting are the two key issues for the participants of current electricity markets. However, load and price of electricity markets have complex characteristics such as nonlinearity, non-stationarity and multiple seasonality, to name a few (usually, more volatility is seen in the behavior of electricity price signal). For these reasons, much research has been devoted to load and price forecast, especially in the recent years. However, previous research works in the area separately predict load and price signals. In this paper, a mixed model for load and price forecasting is presented, which can consider interactions of these two forecast processes. The mixed model is based on an iterative neural network based prediction technique. It is shown that the proposed model can present lower forecast errors for both load and price compared with the previous separate frameworks. Another advantage of the mixed model is that all required forecast features (from load or price) are predicted within the model without assuming known values for these features. So, the proposed model can better be adapted to real conditions of an electricity market. The forecast accuracy of the proposed mixed method is evaluated by means of real data from the New York and Spanish electricity markets. The method is also compared with some of the most recent load and price forecast techniques. (author)

  6. Annual energy outlook 1999, with projections to 2020

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1998-12-01

    The Annual Energy Outlook 1999 (AEO99) presents midterm forecasts of energy supply, demand, and prices through 2020 prepared by the Energy Information Administration (EIA). The projections are based on results from EIA`s National Energy Modeling System (NEMS). The report begins with an Overview summarizing the AEO99 reference case. The next section, Legislation and Regulations, describes the assumptions made with regard to laws that affect energy markets and discusses evolving legislative and regulatory issues. Issues in Focus discusses current energy issues--the economic decline in East Asia, growth in demand for natural gas, vehicle emissions standards, competitive electricity pricing, renewable portfolio standards, and carbon emissions. It is followed by the analysis of energy market trends. The analysis in AEO99 focuses primarily on a reference case and four other cases that assume higher and lower economic growth and higher and lower world oil prices than in the reference case. Forecast tables for these cases are provided in Appendixes A through C. Appendixes D and E present a summary of the reference case forecasts in units of oil equivalence and household energy expenditures. The AEO99 projections are based on Federal, State, and local laws and regulations in effect on July 1, 1998. Pending legislation and sections of existing legislation for which funds have not been appropriated are not reflected in the forecasts. Historical data used for the AEOI99 projections were the most current available as of July 31, 1998, when most 1997 data but only partial 1998 data were available.

  7. Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model

    Directory of Open Access Journals (Sweden)

    Ani Shabri

    2014-01-01

    Full Text Available A new method based on integrating discrete wavelet transform and artificial neural networks (WANN model for daily crude oil price forecasting is proposed. The discrete Mallat wavelet transform is used to decompose the crude price series into one approximation series and some details series (DS. The new series obtained by adding the effective one approximation series and DS component is then used as input into the ANN model to forecast crude oil price. The relative performance of WANN model was compared to regular ANN model for crude oil forecasting at lead times of 1 day for two main crude oil price series, West Texas Intermediate (WTI and Brent crude oil spot prices. In both cases, WANN model was found to provide more accurate crude oil prices forecasts than individual ANN model.

  8. Advances in electric power and energy systems load and price forecasting

    CERN Document Server

    2017-01-01

    A comprehensive review of state-of-the-art approaches to power systems forecasting from the most respected names in the field, internationally. Advances in Electric Power and Energy Systems is the first book devoted exclusively to a subject of increasing urgency to power systems planning and operations. Written for practicing engineers, researchers, and post-grads concerned with power systems planning and forecasting, this book brings together contributions from many of the world’s foremost names in the field who address a range of critical issues, from forecasting power system load to power system pricing to post-storm service restoration times, river flow forecasting, and more. In a time of ever-increasing energy demands, mounting concerns over the environmental impacts of power generation, and the emergence of new, smart-grid technologies, electricity price forecasting has assumed a prominent role within both the academic and industrial ar nas. Short-run forecasting of electricity prices has become nece...

  9. Assumptions for the Annual Energy Outlook 1993

    International Nuclear Information System (INIS)

    1993-01-01

    This report is an auxiliary document to the Annual Energy Outlook 1993 (AEO) (DOE/EIA-0383(93)). It presents a detailed discussion of the assumptions underlying the forecasts in the AEO. The energy modeling system is an economic equilibrium system, with component demand modules representing end-use energy consumption by major end-use sector. Another set of modules represents petroleum, natural gas, coal, and electricity supply patterns and pricing. A separate module generates annual forecasts of important macroeconomic and industrial output variables. Interactions among these components of energy markets generate projections of prices and quantities for which energy supply equals energy demand. This equilibrium modeling system is referred to as the Intermediate Future Forecasting System (IFFS). The supply models in IFFS for oil, coal, natural gas, and electricity determine supply and price for each fuel depending upon consumption levels, while the demand models determine consumption depending upon end-use price. IFFS solves for market equilibrium for each fuel by balancing supply and demand to produce an energy balance in each forecast year

  10. Probabilistic Price Forecasting for Day-Ahead and Intraday Markets: Beyond the Statistical Model

    Directory of Open Access Journals (Sweden)

    José R. Andrade

    2017-10-01

    Full Text Available Forecasting the hourly spot price of day-ahead and intraday markets is particularly challenging in electric power systems characterized by high installed capacity of renewable energy technologies. In particular, periods with low and high price levels are difficult to predict due to a limited number of representative cases in the historical dataset, which leads to forecast bias problems and wide forecast intervals. Moreover, these markets also require the inclusion of multiple explanatory variables, which increases the complexity of the model without guaranteeing a forecasting skill improvement. This paper explores information from daily futures contract trading and forecast of the daily average spot price to correct point and probabilistic forecasting bias. It also shows that an adequate choice of explanatory variables and use of simple models like linear quantile regression can lead to highly accurate spot price point and probabilistic forecasts. In terms of point forecast, the mean absolute error was 3.03 €/MWh for day-ahead market and a maximum value of 2.53 €/MWh was obtained for intraday session 6. The probabilistic forecast results show sharp forecast intervals and deviations from perfect calibration below 7% for all market sessions.

  11. Oil price assumptions in macroeconomic forecasts: should we follow future market expectations?

    International Nuclear Information System (INIS)

    Coimbra, C.; Esteves, P.S.

    2004-01-01

    In macroeconomic forecasting, in spite of its important role in price and activity developments, oil prices are usually taken as an exogenous variable, for which assumptions have to be made. This paper evaluates the forecasting performance of futures market prices against the other popular technical procedure, the carry-over assumption. The results suggest that there is almost no difference between opting for futures market prices or using the carry-over assumption for short-term forecasting horizons (up to 12 months), while, for longer-term horizons, they favour the use of futures market prices. However, as futures market prices reflect market expectations for world economic activity, futures oil prices should be adjusted whenever market expectations for world economic growth are different to the values underlying the macroeconomic scenarios, in order to fully ensure the internal consistency of those scenarios. (Author)

  12. Price Forecasting of Electricity Markets in the Presence of a High Penetration of Wind Power Generators

    OpenAIRE

    Saber Talari; Miadreza Shafie-khah; Gerardo J. Osório; Fei Wang; Alireza Heidari; João P. S. Catalão

    2017-01-01

    Price forecasting plays a vital role in the day-ahead markets. Once sellers and buyers access an accurate price forecasting, managing the economic risk can be conducted appropriately through offering or bidding suitable prices. In networks with high wind power penetration, the electricity price is influenced by wind energy; therefore, price forecasting can be more complicated. This paper proposes a novel hybrid approach for price forecasting of day-ahead markets, with high penetration of wind...

  13. Forecasting the price of gold: An error correction approach

    Directory of Open Access Journals (Sweden)

    Kausik Gangopadhyay

    2016-03-01

    Full Text Available Gold prices in the Indian market may be influenced by a multitude of factors such as the value of gold in investment decisions, as an inflation hedge, and in consumption motives. We develop a model to explain and forecast gold prices in India, using a vector error correction model. We identify investment decision and inflation hedge as prime movers of the data. We also present out-of-sample forecasts of our model and the related properties.

  14. Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets

    International Nuclear Information System (INIS)

    Yamin, H.Y.; Shahidehpour, S.M.; Li, Z.

    2004-01-01

    This paper proposes a comprehensive model for the adaptive short-term electricity price forecasting using Artificial Neural Networks (ANN) in the restructured power markets. The model consists: price simulation, price forecasting, and performance analysis. The factors impacting the electricity price forecasting, including time factors, load factors, reserve factors, and historical price factor are discussed. We adopted ANN and proposed a new definition for the MAPE using the median to study the relationship between these factors and market price as well as the performance of the electricity price forecasting. The reserve factors are included to enhance the performance of the forecasting process. The proposed model handles the price spikes more efficiently due to considering the median instead of the average. The IEEE 118-bus system and California practical system are used to demonstrate the superiority of the proposed model. (author)

  15. AN EVALUATION OF POINT AND DENSITY FORECASTS FOR SELECTED EU FARM GATE MILK PRICES

    Directory of Open Access Journals (Sweden)

    Dennis Bergmann

    2018-01-01

    Full Text Available Fundamental changes to the common agricultural policy (CAP have led to greater market orientation which in turn has resulted in sharply increased variability of EU farm gate milk prices and thus farmers’ income. In this market environment reliable forecasts of farm gate milk prices are extremely important as farmers can make improved decisions with regards to cash flow management and budget preparation. In addition these forecasts may be used in setting fixed priced contracts between dairy farmers and processors thus providing certainty and reducing risk. In this study both point and density forecasts from various time series models for farm gate milk prices in Germany, Ireland and for an average EU price series are evaluated using a rolling window framework. Additionally forecasts of the individual models are combined using different combination schemes. The results of the out of sample evaluation show that ARIMA type models perform well on short forecast horizons (1 to 3 month while the structural time series approach performs well on longer forecast horizons (12 month. Finally combining individual forecasts of different models significantly improves the forecast performance for all forecast horizons.

  16. Uranium price forecasting methods

    International Nuclear Information System (INIS)

    Fuller, D.M.

    1994-01-01

    This article reviews a number of forecasting methods that have been applied to uranium prices and compares their relative strengths and weaknesses. The methods reviewed are: (1) judgemental methods, (2) technical analysis, (3) time-series methods, (4) fundamental analysis, and (5) econometric methods. Historically, none of these methods has performed very well, but a well-thought-out model is still useful as a basis from which to adjust to new circumstances and try again

  17. Simultaneous day-ahead forecasting of electricity price and load in smart grids

    International Nuclear Information System (INIS)

    Shayeghi, H.; Ghasemi, A.; Moradzadeh, M.; Nooshyar, M.

    2015-01-01

    Highlights: • This paper presents a novel MIMO-based support vector machine for forecasting. • Considered uncertainties for better simulation for filtering in input data. • Used LSSVM technique for learning. • Proposed a new modification for standard artificial bee colony algorithm to optimize LSSVM engine. - Abstract: In smart grids, customers are promoted to change their energy consumption patterns by electricity prices. In fact, in this environment, the electricity price and load consumption are highly corrected such that the market participants will have complex model in their decisions to maximize their profit. Although the available forecasting mythologies perform well in electricity market by way of little or no load and price interdependencies, but cannot capture load and price dynamics if they exist. To overcome this shortage, a Multi-Input Multi-Output (MIMO) model is presented which can consider the correlation between electricity price and load. The proposed model consists of three components known as a Wavelet Packet Transform (WPT) to make valuable subsets, Generalized Mutual Information (GMI) to select best input candidate and Least Squares Support Vector Machine (LSSVM) based on MIMO model, called LSSVM-MIMO, to make simultaneous load and price forecasts. Moreover, the LSSVM-MIMO parameters are optimized by a novel Quasi-Oppositional Artificial Bee Colony (QOABC) algorithm. Some forecasting indices based on error factor are considered to evaluate the forecasting accuracy. Simulations carried out on New York Independent System Operator, New South Wales (NSW) and PJM electricity markets data, and showing that the proposed hybrid algorithm has good potential for simultaneous forecasting of electricity price and load

  18. Annual energy outlook 1998 with projections to 2020

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1997-12-01

    The Annual Energy Outlook 1998 (AEO98) is the first AEO with projections to 2020. Key issues for the forecast extension are trends in energy efficiency improvements, the effects of increasing production and productivity improvements on energy prices, and the reduction in nuclear generating capacity. Projections in AEO98 also reflect a greater shift to electricity market restructuring. Restructuring is addressed through several changes that are assumed to occur in the industry, including a shorter capital recovery period for capacity expansion decisions and a revised financial structure that features a higher cost of capital as the result of higher competitive risk. Both assumptions tend to favor less capital-intensive generation technologies, such as natural gas, over coal or baseload renewable technologies. The forecasts include specific restructuring plans in those regions that have announced plans. California, New York, and New England are assumed to begin competitive pricing in 1998. The provisions of the California legislation for stranded cost recovery and price caps are incorporated. In New York and New England, stranded cost recovery is assumed to be phased out by 2008.

  19. Modeling and forecasting electricity price jumps in the Nord Pool power market

    DEFF Research Database (Denmark)

    Knapik, Oskar

    extreme prices and forecasting of the price jumps is crucial for risk management and market design. In this paper, we consider the problem of the impact of fundamental price drivers on forecasting of price jumps in NordPool intraday market. We develop categorical time series models which take into account......For risk management traders in the electricity market are mainly interested in the risk of negative (drops) or of positive (spikes) price jumps, i.e. the sellers face the risk of negative price jumps while the buyers face the risk of positive price jumps. Understanding the mechanism that drive...

  20. Forecasting regional house price inflation: a comparison between dynamic factor models and vector autoregressive models

    CSIR Research Space (South Africa)

    Das, Sonali

    2010-01-01

    Full Text Available and Gertler, 1995), but also because changes in house prices tend to have important wealth effects on consumption (International Monetary Fund, 2000) and investment (Topel and Rosen, 1988), the importance of forecasting house price infl ation is vital, since... sections, respectively, lay out the DFM and outline the basics of the VAR, the Minnesota-type BVARs, and the SBVARs based on the fi rst-order spatial contiguity (FOSC) and the random walk averaging (RWA) priors developed by LeSage and Pan (1995) and Le...

  1. Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks.

    Science.gov (United States)

    Jin, Junghwan; Kim, Jinsoo

    2015-01-01

    Following the unconventional gas revolution, the forecasting of natural gas prices has become increasingly important because the association of these prices with those of crude oil has weakened. With this as motivation, we propose some modified hybrid models in which various combinations of the wavelet approximation, detail components, autoregressive integrated moving average, generalized autoregressive conditional heteroskedasticity, and artificial neural network models are employed to predict natural gas prices. We also emphasize the boundary problem in wavelet decomposition, and compare results that consider the boundary problem case with those that do not. The empirical results show that our suggested approach can handle the boundary problem, such that it facilitates the extraction of the appropriate forecasting results. The performance of the wavelet-hybrid approach was superior in all cases, whereas the application of detail components in the forecasting was only able to yield a small improvement in forecasting performance. Therefore, forecasting with only an approximation component would be acceptable, in consideration of forecasting efficiency.

  2. Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks.

    Directory of Open Access Journals (Sweden)

    Junghwan Jin

    Full Text Available Following the unconventional gas revolution, the forecasting of natural gas prices has become increasingly important because the association of these prices with those of crude oil has weakened. With this as motivation, we propose some modified hybrid models in which various combinations of the wavelet approximation, detail components, autoregressive integrated moving average, generalized autoregressive conditional heteroskedasticity, and artificial neural network models are employed to predict natural gas prices. We also emphasize the boundary problem in wavelet decomposition, and compare results that consider the boundary problem case with those that do not. The empirical results show that our suggested approach can handle the boundary problem, such that it facilitates the extraction of the appropriate forecasting results. The performance of the wavelet-hybrid approach was superior in all cases, whereas the application of detail components in the forecasting was only able to yield a small improvement in forecasting performance. Therefore, forecasting with only an approximation component would be acceptable, in consideration of forecasting efficiency.

  3. Price Density Forecasts in the U.S. Hog Market: Composite Procedures

    NARCIS (Netherlands)

    Trujillo Barrera, A.A.; Garcia, P.; Mallory, M.

    2013-01-01

    Abstract We develop and evaluate quarterly out-of-sample individual and composite density forecasts for U.S. hog prices using data from 1975.I to 2010.IV. Individual forecasts are generated from time series models and the implied distribution of USDA outlook forecasts. Composite density forecasts

  4. Accounting for fuel price risk when comparing renewable togas-fired generation: the role of forward natural gas prices

    Energy Technology Data Exchange (ETDEWEB)

    Bolinger, Mark; Wiser, Ryan; Golove, William

    2004-07-17

    Unlike natural gas-fired generation, renewable generation (e.g., from wind, solar, and geothermal power) is largely immune to fuel price risk. If ratepayers are rational and value long-term price stability, then--contrary to common practice--any comparison of the levelized cost of renewable to gas-fired generation should be based on a hedged gas price input, rather than an uncertain gas price forecast. This paper compares natural gas prices that can be locked in through futures, swaps, and physical supply contracts to contemporaneous long-term forecasts of spot gas prices. We find that from 2000-2003, forward gas prices for terms of 2-10 years have been considerably higher than most contemporaneous long-term gas price forecasts. This difference is striking, and implies that comparisons between renewable and gas-fired generation based on these forecasts over this period have arguably yielded results that are biased in favor of gas-fired generation.

  5. Modelling and Forecasting Stock Price Movements with Serially Dependent Determinants

    Directory of Open Access Journals (Sweden)

    Rasika Yatigammana

    2018-05-01

    Full Text Available The direction of price movements are analysed under an ordered probit framework, recognising the importance of accounting for discreteness in price changes. By extending the work of Hausman et al. (1972 and Yang and Parwada (2012,This paper focuses on improving the forecast performance of the model while infusing a more practical perspective by enhancing flexibility. This is achieved by extending the existing framework to generate short term multi period ahead forecasts for better decision making, whilst considering the serial dependence structure. This approach enhances the flexibility and adaptability of the model to future price changes, particularly targeting risk minimisation. Empirical evidence is provided, based on seven stocks listed on the Australian Securities Exchange (ASX. The prediction success varies between 78 and 91 per cent for in-sample and out-of-sample forecasts for both the short term and long term.

  6. Accounting for fuel price risk when comparing renewable to gas-fired generation: the role of forward natural gas prices

    International Nuclear Information System (INIS)

    Bolinger, Mark; Wiser, Ryan; Golove, William

    2006-01-01

    Unlike natural gas-fired generation, renewable generation (e.g., from wind, solar, and geothermal power) is largely immune to fuel price risk. If ratepayers are rational and value long-term price stability, then-contrary to common practice-any comparison of the levelized cost of renewable to gas-fired generation should be based on a hedged gas price input, rather than an uncertain gas price forecast. This paper compares natural gas prices that can be locked in through futures, swaps, and physical supply contracts to contemporaneous long-term forecasts of spot gas prices. We find that from 2000 to 2003, forward gas prices for terms of 2-10 years have been considerably higher than most contemporaneous long-term gas price forecasts. This difference is striking, and implies that comparisons between renewable and gas-fired generation based on these forecasts over this period have arguably yielded results that are biased in favor of gas-fired generation

  7. Forecasting loads and prices in competitive power markets

    International Nuclear Information System (INIS)

    Bunn, D.W.

    2000-01-01

    This paper provides a review of some of the main methodological issues and techniques which have become innovative in addressing the problem of forecasting daily loads and prices in the new competitive power markets. Particular emphasis is placed upon computationally intensive methods, including variable segmentation, multiple modeling, combinations, and neural networks for forecasting the demand side, and strategic simulation using artificial agents for the supply side

  8. Annual energy outlook 1997 with projections to 2015

    International Nuclear Information System (INIS)

    1996-12-01

    The Annual Energy Outlook 1997 (AEO97) presents midterm forecasts of energy supply, demand, and prices through 2015 prepared by the Energy Information Administration (EIA). These projections are based on results of EIA's National Energy Modeling System (NEMS). This report begins with a summary of the reference case, followed by a discussion of the legislative assumptions and evolving legislative and regulatory issues. ''Issues in Focus'' discusses emerging energy issues and other topics of particular interest. It is followed by the analysis of energy market trends. The analysis in AEO97 focuses primarily on a reference case and four other cases that assume higher and lower economic growth and higher and lower world oil prices than in the reference case. Forecast tables for these cases are provided in Appendixes A through C. Appendixes D and E present summaries of the reference case forecasts in units of oil equivalence and household energy expenditures. Twenty-three other cases explore the impacts of varying key assumptions in NEMS--generally, technology penetration, with the major results shown in Appendix F. Appendix G briefly describes NEMS and the major AEO97 assumptions, with a summary table. 114 figs., 22 tabs

  9. Annual energy outlook 1997 with projections to 2015

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1996-12-01

    The Annual Energy Outlook 1997 (AEO97) presents midterm forecasts of energy supply, demand, and prices through 2015 prepared by the Energy Information Administration (EIA). These projections are based on results of EIA`s National Energy Modeling System (NEMS). This report begins with a summary of the reference case, followed by a discussion of the legislative assumptions and evolving legislative and regulatory issues. ``Issues in Focus`` discusses emerging energy issues and other topics of particular interest. It is followed by the analysis of energy market trends. The analysis in AEO97 focuses primarily on a reference case and four other cases that assume higher and lower economic growth and higher and lower world oil prices than in the reference case. Forecast tables for these cases are provided in Appendixes A through C. Appendixes D and E present summaries of the reference case forecasts in units of oil equivalence and household energy expenditures. Twenty-three other cases explore the impacts of varying key assumptions in NEMS--generally, technology penetration, with the major results shown in Appendix F. Appendix G briefly describes NEMS and the major AEO97 assumptions, with a summary table. 114 figs., 22 tabs.

  10. Electricity price forecasting through transfer function models

    International Nuclear Information System (INIS)

    Nogales, F.J.; Conejo, A.J.

    2006-01-01

    Forecasting electricity prices in present day competitive electricity markets is a must for both producers and consumers because both need price estimates to develop their respective market bidding strategies. This paper proposes a transfer function model to predict electricity prices based on both past electricity prices and demands, and discuss the rationale to build it. The importance of electricity demand information is assessed. Appropriate metrics to appraise prediction quality are identified and used. Realistic and extensive simulations based on data from the PJM Interconnection for year 2003 are conducted. The proposed model is compared with naive and other techniques. Journal of the Operational Research Society (2006) 57, 350-356.doi:10.1057/palgrave.jors.2601995; published online 18 May 2005. (author)

  11. Robust forecast comparison

    OpenAIRE

    Jin, Sainan; Corradi, Valentina; Swanson, Norman

    2015-01-01

    Forecast accuracy is typically measured in terms of a given loss function. However, as a consequence of the use of misspecified models in multiple model comparisons, relative forecast rankings are loss function dependent. This paper addresses this issue by using a novel criterion for forecast evaluation which is based on the entire distribution of forecast errors. We introduce the concepts of general-loss (GL) forecast superiority and convex-loss (CL) forecast superiority, and we establish a ...

  12. A regime-switching stochastic volatility model for forecasting electricity prices

    DEFF Research Database (Denmark)

    Exterkate, Peter; Knapik, Oskar

    In a recent review paper, Weron (2014) pinpoints several crucial challenges outstanding in the area of electricity price forecasting. This research attempts to address all of them by i) showing the importance of considering fundamental price drivers in modeling, ii) developing new techniques for ...... on explanatory variables. Bayesian inference is explored in order to obtain predictive densities. The main focus of the paper is on shorttime density forecasting in Nord Pool intraday market. We show that the proposed model outperforms several benchmark models at this task....

  13. OSSIM wave-optics toolbox and its use to simulate AEOS

    Science.gov (United States)

    Smith, Carey A.; Forgham, James L.; Jones, Bruce W.; Jones, Kenneth D.

    2001-12-01

    OSSim (Optical System Simulation) is a simulation toolbox of optical and processing components. By using full wave-optics in the time-domain, OSSim simulates diffractive effects and control loop interactions missed by simpler analyses. OSSim also models the atmosphere, with user customizable turbulence strength, wind, and slew. This paper first presents 2 introductory examples: a simple 2-lens imaging system and a simple tilt-control system. Then it presents a simulation of the 3.67-meter AEOS (Advanced Electro-Optics System) telescope on Maui. The OSSim simulation agrees well with the AEOS experimental results.

  14. Electricity prices forecasting by automatic dynamic harmonic regression models

    International Nuclear Information System (INIS)

    Pedregal, Diego J.; Trapero, Juan R.

    2007-01-01

    The changes experienced by electricity markets in recent years have created the necessity for more accurate forecast tools of electricity prices, both for producers and consumers. Many methodologies have been applied to this aim, but in the view of the authors, state space models are not yet fully exploited. The present paper proposes a univariate dynamic harmonic regression model set up in a state space framework for forecasting prices in these markets. The advantages of the approach are threefold. Firstly, a fast automatic identification and estimation procedure is proposed based on the frequency domain. Secondly, the recursive algorithms applied offer adaptive predictions that compare favourably with respect to other techniques. Finally, since the method is based on unobserved components models, explicit information about trend, seasonal and irregular behaviours of the series can be extracted. This information is of great value to the electricity companies' managers in order to improve their strategies, i.e. it provides management innovations. The good forecast performance and the rapid adaptability of the model to changes in the data are illustrated with actual prices taken from the PJM interconnection in the US and for the Spanish market for the year 2002. (author)

  15. Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis

    Science.gov (United States)

    E, Jianwei; Bao, Yanling; Ye, Jimin

    2017-10-01

    As one of the most vital energy resources in the world, crude oil plays a significant role in international economic market. The fluctuation of crude oil price has attracted academic and commercial attention. There exist many methods in forecasting the trend of crude oil price. However, traditional models failed in predicting accurately. Based on this, a hybrid method will be proposed in this paper, which combines variational mode decomposition (VMD), independent component analysis (ICA) and autoregressive integrated moving average (ARIMA), called VMD-ICA-ARIMA. The purpose of this study is to analyze the influence factors of crude oil price and predict the future crude oil price. Major steps can be concluded as follows: Firstly, applying the VMD model on the original signal (crude oil price), the modes function can be decomposed adaptively. Secondly, independent components are separated by the ICA, and how the independent components affect the crude oil price is analyzed. Finally, forecasting the price of crude oil price by the ARIMA model, the forecasting trend demonstrates that crude oil price declines periodically. Comparing with benchmark ARIMA and EEMD-ICA-ARIMA, VMD-ICA-ARIMA can forecast the crude oil price more accurately.

  16. Day-ahead deregulated electricity market price forecasting using neural network input featured by DCT

    International Nuclear Information System (INIS)

    Anbazhagan, S.; Kumarappan, N.

    2014-01-01

    Highlights: • We presented DCT input featured FFNN model for forecasting in Spain market. • The key factors impacting electricity price forecasting are historical prices. • Past 42 days were trained and the next 7 days were forecasted. • The proposed approach has a simple and better NN structure. • The DCT-FFNN mode is effective and less computation time than the recent models. - Abstract: In a deregulated market, a number of factors determined the outcome of electricity price and displays a perplexed and maverick fluctuation. Both power producers and consumers needs single compact and robust price forecasting tool in order to maximize their profits and utilities. In order to achieve the helter–skelter kind of electricity price, one dimensional discrete cosine transforms (DCT) input featured feed-forward neural network (FFNN) is modeled (DCT-FFNN). The proposed FFNN is a single compact and robust architecture (without hybridizing the various hard and soft computing models). It has been predicted that the DCT-FFNN model is close to the state of the art can be achieved with less computation time. The proposed DCT-FFNN approach is compared with 17 other recent approaches to estimate the market clearing prices of mainland Spain. Finally, the accuracy of the price forecasting is also applied to the electricity market of New York in year 2010 that shows the effectiveness of the proposed DCT-FFNN approach

  17. Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach

    Energy Technology Data Exchange (ETDEWEB)

    Catalao, J.P.S. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Center for Innovation in Electrical and Energy Engineering, Instituto Superior Tecnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001 Lisbon (Portugal); Pousinho, H.M.I. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Mendes, V.M.F. [Department of Electrical Engineering and Automation, Instituto Superior de Engenharia de Lisboa, R. Conselheiro Emidio Navarro, 1950-062 Lisbon (Portugal)

    2011-02-15

    In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. (author)

  18. Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach

    International Nuclear Information System (INIS)

    Catalao, J.P.S.; Pousinho, H.M.I.; Mendes, V.M.F.

    2011-01-01

    In this paper, a hybrid intelligent approach is proposed for short-term electricity prices forecasting in a competitive market. The proposed approach is based on the wavelet transform and a hybrid of neural networks and fuzzy logic. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Conclusions are duly drawn. (author)

  19. Coal Price Forecasting and Structural Analysis in China

    Directory of Open Access Journals (Sweden)

    Xiaopeng Guo

    2016-01-01

    Full Text Available Coal plays an important role in China’s energy structure and its price has been continuously decreasing since the second half of 2012. Constant low price of coal affected the profits of coal enterprises and the coal use of its downstream firms; the precision of coal price provides a reference for these enterprises making their management strategy. Based on the historical data of coal price and related factors such as port stocks, sales volume, futures prices, Producer Price Index (PPI, and crude oil price rate from November 2013 to June 2016, this study aims to forecast coal price using vector autoregression (VAR model and portray the dynamic correlations between coal price and variables by the impulse response function and variance decomposition. Comparing predicted and actual values, the root mean square error (RMSE was small which indicated good precision of this model. Thus this short period prediction can help these enterprises make the right business decisions.

  20. Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection

    DEFF Research Database (Denmark)

    Bork, Lasse; Møller, Stig Vinther

    2015-01-01

    We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves substantia......We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves...

  1. Extension of the new proposed association equation of state (AEOS) to associating fluid mixtures

    International Nuclear Information System (INIS)

    Rezaei, H.; Modarress, H.; Mohsen-Nia, M.

    2010-01-01

    Recently, a new statistical mechanic-based equation of state has been proposed by Mohsen-Nia and Modarress [M. Mohsen-Nia, H. Modarress, Chem. Phys. 336 (2007) 22-26] for associating pure fluids. The new association equation of state (AEOS) was successfully applied to calculate the saturated properties of water, methanol, and ammonia. In this work, the new proposed AEOS is used to evaluate the (vapour + liquid) equilibrium (VLE) of 25 associating pure compounds and the adjusted parameters are reported. The new AEOS is also extended to mixtures containing associating and non-associating compounds. The calculated saturated properties of the pure compounds are compared with those calculated by other AEOSs. The results of VLE calculation for various binary mixtures such as: alcohol/hydrocarbon, alcohol/CO 2 , alcohol/aromatic-hydrocarbons, and the quaternary system (H 2 O/CH 4 /CO 2 /H 2 S) indicate the capability of the new proposed AEOS for associating pure and mixture calculations.

  2. A Comparison of forecasting Volatility startegies into ARCH Class throughPricing

    OpenAIRE

    Marzia Freo

    2003-01-01

    Daily data on the German market index return are used to consider multiple issues in a forecasting comparison of ARCH-type specifications. first, attention is paid to the impact of different sample sizez, different horizons and fitting of historical versus implied data. Secondly, the issue of volatility transmission is addressed by modelling French and Germany market indexes into simultaneous conditionally heteroskedasticity framework. Errors obtained by updating the Black and Scholes formula...

  3. Fuzzy time-series based on Fibonacci sequence for stock price forecasting

    Science.gov (United States)

    Chen, Tai-Liang; Cheng, Ching-Hsue; Jong Teoh, Hia

    2007-07-01

    Time-series models have been utilized to make reasonably accurate predictions in the areas of stock price movements, academic enrollments, weather, etc. For promoting the forecasting performance of fuzzy time-series models, this paper proposes a new model, which incorporates the concept of the Fibonacci sequence, the framework of Song and Chissom's model and the weighted method of Yu's model. This paper employs a 5-year period TSMC (Taiwan Semiconductor Manufacturing Company) stock price data and a 13-year period of TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) stock index data as experimental datasets. By comparing our forecasting performances with Chen's (Forecasting enrollments based on fuzzy time-series. Fuzzy Sets Syst. 81 (1996) 311-319), Yu's (Weighted fuzzy time-series models for TAIEX forecasting. Physica A 349 (2004) 609-624) and Huarng's (The application of neural networks to forecast fuzzy time series. Physica A 336 (2006) 481-491) models, we conclude that the proposed model surpasses in accuracy these conventional fuzzy time-series models.

  4. Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models

    International Nuclear Information System (INIS)

    Tan, Zhongfu; Zhang, Jinliang; Xu, Jun; Wang, Jianhui

    2010-01-01

    This paper proposes a novel price forecasting method based on wavelet transform combined with ARIMA and GARCH models. By wavelet transform, the historical price series is decomposed and reconstructed into one approximation series and some detail series. Then each subseries can be separately predicted by a suitable time series model. The final forecast is obtained by composing the forecasted results of each subseries. This proposed method is examined on Spanish and PJM electricity markets and compared with some other forecasting methods. (author)

  5. Day-ahead price forecasting of electricity markets by a new feature selection algorithm and cascaded neural network technique

    International Nuclear Information System (INIS)

    Amjady, Nima; Keynia, Farshid

    2009-01-01

    With the introduction of restructuring into the electric power industry, the price of electricity has become the focus of all activities in the power market. Electricity price forecast is key information for electricity market managers and participants. However, electricity price is a complex signal due to its non-linear, non-stationary, and time variant behavior. In spite of performed research in this area, more accurate and robust price forecast methods are still required. In this paper, a new forecast strategy is proposed for day-ahead price forecasting of electricity markets. Our forecast strategy is composed of a new two stage feature selection technique and cascaded neural networks. The proposed feature selection technique comprises modified Relief algorithm for the first stage and correlation analysis for the second stage. The modified Relief algorithm selects candidate inputs with maximum relevancy with the target variable. Then among the selected candidates, the correlation analysis eliminates redundant inputs. Selected features by the two stage feature selection technique are used for the forecast engine, which is composed of 24 consecutive forecasters. Each of these 24 forecasters is a neural network allocated to predict the price of 1 h of the next day. The whole proposed forecast strategy is examined on the Spanish and Australia's National Electricity Markets Management Company (NEMMCO) and compared with some of the most recent price forecast methods.

  6. Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based scheme

    International Nuclear Information System (INIS)

    Shayeghi, H.; Ghasemi, A.

    2013-01-01

    Highlights: • Presenting a hybrid CGSA-LSSVM scheme for price forecasting. • Considering uncertainties for filtering in input data and feature selection to improve efficiency. • Using DWT input featured LSSVM approach to classify next-week prices. • Used three real markets to illustrate performance of the proposed price forecasting model. - Abstract: At the present time, day-ahead electricity market is closely associated with other commodity markets such as fuel market and emission market. Under such an environment, day-ahead electricity price forecasting has become necessary for power producers and consumers in the current deregulated electricity markets. Seeking for more accurate price forecasting techniques, this paper proposes a new combination of a Feature Selection (FS) technique based mutual information (MI) technique and Wavelet Transform (WT) in this study. Moreover, in this paper a new modified version of Gravitational Search Algorithm (GSA) optimization based chaos theory, namely Chaotic Gravitational Search Algorithm (CGSA) is developed to find the optimal parameters of Least Square Support Vector Machine (LSSVM) to predict electricity prices. The performance and price forecast accuracy of the proposed technique is assessed by means of real data from Iran’s, Ontario’s and Spain’s price markets. The simulation results from numerical tables and figures in different cases show that the proposed technique increases electricity price market forecasting accuracy than the other classical and heretical methods in the scientific researches

  7. Preliminary analysis on hybrid Box-Jenkins - GARCH modeling in forecasting gold price

    Science.gov (United States)

    Yaziz, Siti Roslindar; Azizan, Noor Azlinna; Ahmad, Maizah Hura; Zakaria, Roslinazairimah; Agrawal, Manju; Boland, John

    2015-02-01

    Gold has been regarded as a valuable precious metal and the most popular commodity as a healthy return investment. Hence, the analysis and prediction of gold price become very significant to investors. This study is a preliminary analysis on gold price and its volatility that focuses on the performance of hybrid Box-Jenkins models together with GARCH in analyzing and forecasting gold price. The Box-Cox formula is used as the data transformation method due to its potential best practice in normalizing data, stabilizing variance and reduces heteroscedasticity using 41-year daily gold price data series starting 2nd January 1973. Our study indicates that the proposed hybrid model ARIMA-GARCH with t-innovation can be a new potential approach in forecasting gold price. This finding proves the strength of GARCH in handling volatility in the gold price as well as overcomes the non-linear limitation in the Box-Jenkins modeling.

  8. Forecasting Nord Pool day-ahead prices with an autoregressive model

    International Nuclear Information System (INIS)

    Kristiansen, Tarjei

    2012-01-01

    This paper presents a model to forecast Nord Pool hourly day-ahead prices. The model is based on but reduced in terms of estimation parameters (from 24 sets to 1) and modified to include Nordic demand and Danish wind power as exogenous variables. We model prices across all hours in the analysis period rather than across each single hour of 24 hours. By applying three model variants on Nord Pool data, we achieve a weekly mean absolute percentage error (WMAE) of around 6–7% and an hourly mean absolute percentage error (MAPE) ranging from 8% to 11%. Out of sample results yields a WMAE and an hourly MAPE of around 5%. The models enable analysts and traders to forecast hourly day-ahead prices accurately. Moreover, the models are relatively straightforward and user-friendly to implement. They can be set up in any trading organization. - Highlights: ► Forecasting Nord Pool day-ahead prices with an autoregressive model. ► The model is based on but with the set of parameters reduced from 24 to 1. ► The model includes Nordic demand and Danish wind power as exogenous variables. ► Hourly mean absolute percentage error ranges from 8% to 11%. ► Out of sample results yields a WMAE and an hourly MAPE of around 5%.

  9. Fünf Jahre Zugelassener Wirtschaftsbeteiligter (AEO)

    OpenAIRE

    Weerth, Carsten

    2014-01-01

    Der Zugelassene Wirtschaftsbeteiligte (AEO) wurde mit Wirkung vom 1. Januar 2008 geschaffen. Vier Jahre sind vergangen, nach denen es nun erneut gilt, eine Zwischenbilanz zu ziehen – wurden die Erwartungen und Antragszahlen erreicht, was gibt es an Neuerungen und welche Tendenzen sind erkennbar? Neue rechtliche Rahmenbedingungen gelten bereits seit dem 1. Januar 2009 – die Kriterien für alle zollrechtlichen Bewilligungen von vereinfachten Anmeldeverfahren und Anschreibeverfahren (Einfuhr und ...

  10. Formation and forecast of the daily price of the electric power in the chain Nare-Guatape-San Carlos

    International Nuclear Information System (INIS)

    Romero, Alejandro; Carvajal, Luis

    2003-01-01

    This work shows three different methodologies for the understanding and forecast of the electric energy prices in the chain Nare - Guatape - San Carlos: lineal multivariate model, autoregressive deterministic model and Fourier series decomposition. The electric energy price depends basically of the reservoir level and river flow, not only its own but the reservoir down and up, waters. About prices forecast, they can be modeled with an autoregressive process. Prices forecast follows the tendency and captures with acceptable precision the maximum prices due especially to the low hydrology and price variability for daily and weekly regulation reservoirs

  11. Price Forecasting of Electricity Markets in the Presence of a High Penetration of Wind Power Generators

    Directory of Open Access Journals (Sweden)

    Saber Talari

    2017-11-01

    Full Text Available Price forecasting plays a vital role in the day-ahead markets. Once sellers and buyers access an accurate price forecasting, managing the economic risk can be conducted appropriately through offering or bidding suitable prices. In networks with high wind power penetration, the electricity price is influenced by wind energy; therefore, price forecasting can be more complicated. This paper proposes a novel hybrid approach for price forecasting of day-ahead markets, with high penetration of wind generators based on Wavelet transform, bivariate Auto-Regressive Integrated Moving Average (ARIMA method and Radial Basis Function Neural Network (RBFN. To this end, a weighted time series for wind dominated power systems is calculated and added to a bivariate ARIMA model along with the price time series. Moreover, RBFN is applied as a tool to correct the estimation error, and particle swarm optimization (PSO is used to optimize the structure and adapt the RBFN to the particular training set. This method is evaluated on the Spanish electricity market, which shows the efficiency of this approach. This method has less error compared with other methods especially when it considers the effects of large-scale wind generators.

  12. Multi-step ahead forecasts for electricity prices using NARX: A new approach, a critical analysis of one-step ahead forecasts

    International Nuclear Information System (INIS)

    Andalib, Arash; Atry, Farid

    2009-01-01

    The prediction of electricity prices is very important to participants of deregulated markets. Among many properties, a successful prediction tool should be able to capture long-term dependencies in market's historical data. A nonlinear autoregressive model with exogenous inputs (NARX) has proven to enjoy a superior performance to capture such dependencies than other learning machines. However, it is not examined for electricity price forecasting so far. In this paper, we have employed a NARX network for forecasting electricity prices. Our prediction model is then compared with two currently used methods, namely the multivariate adaptive regression splines (MARS) and wavelet neural network. All the models are built on the reconstructed state space of market's historical data, which either improves the results or decreases the complexity of learning algorithms. Here, we also criticize the one-step ahead forecasts for electricity price that may suffer a one-term delay and we explain why the mean square error criterion does not guarantee a functional prediction result in this case. To tackle the problem, we pursue multi-step ahead predictions. Results for the Ontario electricity market are presented

  13. Forecasting short-run crude oil price using high- and low-inventory variables

    International Nuclear Information System (INIS)

    Ye, Michael; Zyren, John; Shore, Joanne

    2006-01-01

    Since inventories have a lower bound or a minimum operating level, economic literature suggests a nonlinear relationship between inventory level and commodity prices. This was found to be the case in the short-run crude oil market. In order to explore this inventory-price relationship, two nonlinear inventory variables are defined and derived from the monthly normal level and relative level of OECD crude oil inventories from post 1991 Gulf War to October 2003: one for the low inventory state and another for the high inventory state of the crude oil market. Incorporation of low- and high-inventory variables in a single equation model to forecast short-run WTI crude oil prices enhances the model fit and forecast ability

  14. Forecasting Long-Term Crude Oil Prices Using a Bayesian Model with Informative Priors

    Directory of Open Access Journals (Sweden)

    Chul-Yong Lee

    2017-01-01

    Full Text Available In the long-term, crude oil prices may impact the economic stability and sustainability of many countries, especially those depending on oil imports. This study thus suggests an alternative model for accurately forecasting oil prices while reflecting structural changes in the oil market by using a Bayesian approach. The prior information is derived from the recent and expected structure of the oil market, using a subjective approach, and then updated with available market data. The model includes as independent variables factors affecting oil prices, such as world oil demand and supply, the financial situation, upstream costs, and geopolitical events. To test the model’s forecasting performance, it is compared with other models, including a linear ordinary least squares model and a neural network model. The proposed model outperforms on the forecasting performance test even though the neural network model shows the best results on a goodness-of-fit test. The results show that the crude oil price is estimated to increase to $169.3/Bbl by 2040.

  15. Forecasting inflation based on the consumer price index, taking into account the impact of seasonal factors

    Directory of Open Access Journals (Sweden)

    A. K. Sapova

    2017-01-01

    Full Text Available The consumer price index is a key indicator of the inflation level in Russia. It is important for the Central Bank and Government in decision-making process. There is a strong need for high-quality analysis and accurate forecast of this index. Modelling and forecasting of consumer price index as a key indicator of inflation are relevant issues in current macroeconomic conditions. The article is dedicated to development of quality short-term forecast of consumer inflation level, with the impact of seasonal factor. Two classes of models (vector autoregression and time series models are considered. It was shown that vector autoregression model of the dependency between consumer price index and nominal effective exchange rate is worse for the proposes of inflation forecast then non-linear model with structural components and conventional heteroscedasticity. The practical significance of this work is that the developed approach to the forecasting of the consumer price index adjusted of seasonal factor can be very helpful for the purpose of proper assessment and regulation of inflation.

  16. Forecasting ability of the investor sentiment endurance index: The case of oil service stock returns and crude oil prices

    International Nuclear Information System (INIS)

    He, Ling T.; Casey, K.M.

    2015-01-01

    Using a binomial probability distribution model this paper creates an endurance index of oil service investor sentiment. The index reflects the probability of the high or low stock price being the close price for the PHLX Oil Service Sector Index. Results of this study reveal the substantial forecasting ability of the sentiment endurance index. Monthly and quarterly rolling forecasts of returns of oil service stocks have an overall accuracy as high as 52% to 57%. In addition, the index shows decent forecasting ability on changes in crude oil prices, especially, WTI prices. The accuracy of 6-quarter rolling forecasts is 55%. The sentiment endurance index, along with the procedure of true forecasting and accuracy ratio, applied in this study provides investors and analysts of oil service sector stocks and crude oil prices as well as energy policy-makers with effective analytical tools

  17. U.S. Cotton Prices and the World Cotton Market: Forecasting and Structural Change

    OpenAIRE

    Isengildina-Massa, Olga; MacDonald, Stephen

    2009-01-01

    The purpose of this study was to analyze structural changes that took place in the cotton industry in recent years and develop a statistical model that reflects the current drivers of U.S. cotton prices. Legislative changes authorized the U.S. Department of Agriculture to resume publishing cotton price forecasts for the first time in 79 years. In addition, systematic problems have become apparent in the forecasting models used by USDA and elsewhere, highlighting the need for an updated review...

  18. A Note on Forecasting the Rate of Change of the Price of Oil: Asymmetric Loss and Forecast Rationality

    Directory of Open Access Journals (Sweden)

    Christian Pierdzioch

    2013-03-01

    Full Text Available We study whether forecasts of the rate of change of the price of oil are rational. To this end, we consider a model that allows the shape of forecasters’ loss function to be studied. The shape of forecasters’ loss function may be consistent with a symmetric or an asymmetric loss function. We find that an asymmetric loss function often (but not always makes forecasts look rational, and we also report that forecast rationality may have changed over time.

  19. An enhanced radial basis function network for short-term electricity price forecasting

    International Nuclear Information System (INIS)

    Lin, Whei-Min; Gow, Hong-Jey; Tsai, Ming-Tang

    2010-01-01

    This paper proposed a price forecasting system for electric market participants to reduce the risk of price volatility. Combining the Radial Basis Function Network (RBFN) and Orthogonal Experimental Design (OED), an Enhanced Radial Basis Function Network (ERBFN) has been proposed for the solving process. The Locational Marginal Price (LMP), system load, transmission flow and temperature of the PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday and weekend. With the OED applied to learning rates in the ERBFN, the forecasting error can be reduced during the training process to improve both accuracy and reliability. This would mean that even the ''spikes'' could be tracked closely. The Back-propagation Neural Network (BPN), Probability Neural Network (PNN), other algorithms, and the proposed ERBFN were all developed and compared to check the performance. Simulation results demonstrated the effectiveness of the proposed ERBFN to provide quality information in a price volatile environment. (author)

  20. Forecasting Electricity Spot Prices Accounting for Wind Power Predictions

    DEFF Research Database (Denmark)

    Jónsson, Tryggvi; Pinson, Pierre; Nielsen, Henrik Aalborg

    2013-01-01

    A two-step methodology for forecasting of electricity spot prices is introduced, with focus on the impact of predicted system load and wind power generation. The nonlinear and nonstationary influence of these explanatory variables is accommodated in a first step based on a nonparametric and time...

  1. Wavelet regression model in forecasting crude oil price

    Science.gov (United States)

    Hamid, Mohd Helmie; Shabri, Ani

    2017-05-01

    This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

  2. Oil Price Forecasting Using Crack Spread Futures and Oil Exchange Traded Funds

    Directory of Open Access Journals (Sweden)

    Hankyeung Choi

    2015-04-01

    Full Text Available Given the emerging consensus from previous studies that crude oil and refined product (as well as crack spread prices are cointegrated, this study examines the link between the crude oil spot and crack spread derivatives markets. Specifically, the usefulness of the two crack spread derivatives products (namely, crack spread futures and the ETF crack spread for modeling and forecasting daily OPEC crude oil spot prices is evaluated. Based on the results of a structural break test, the sample is divided into pre-crisis, crisis, and post-crisis periods. We find a unidirectional relationship from the two crack spread derivatives markets to the crude oil spot market during the post-crisis period. In terms of forecasting performance, the forecasting models based on crack spread futures and the ETF crack spread outperform the Random Walk Model (RWM, both in-sample and out-of-sample. In addition, on average, the results suggest that information from the ETF crack spread market contributes more to the forecasting models than information from the crack spread futures market.

  3. On the importance of the long-term seasonal component in day-ahead electricity price forecasting

    International Nuclear Information System (INIS)

    Nowotarski, Jakub; Weron, Rafał

    2016-01-01

    In day-ahead electricity price forecasting (EPF) the daily and weekly seasonalities are always taken into account, but the long-term seasonal component (LTSC) is believed to add unnecessary complexity to the already parameter-rich models and is generally ignored. Conducting an extensive empirical study involving state-of-the-art time series models we show that (i) decomposing a series of electricity prices into a LTSC and a stochastic component, (ii) modeling them independently and (iii) combining their forecasts can bring – contrary to a common belief – an accuracy gain compared to an approach in which a given time series model is calibrated to the prices themselves. - Highlights: • A new class of Seasonal Component AutoRegressive (SCAR) models is introduced. • Electricity prices are decomposed into a trend-seasonal and a stochastic component. • Both components are modeled independently, their forecasts are combined. • Significant accuracy gains can be achieved compared to commonly used approaches.

  4. Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

    International Nuclear Information System (INIS)

    Nguyen, Hang T.; Nabney, Ian T.

    2010-01-01

    This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their NMSEs are 0.02314 and 0.15384 respectively. (author)

  5. Testing the rationality of DOE's energy price forecasts under asymmetric loss preferences

    International Nuclear Information System (INIS)

    Mamatzakis, E.; Koutsomanoli-Filippaki, A.

    2014-01-01

    This paper examines the rationality of the price forecasts for energy commodities of the United States Department of Energy's (DOE), departing from the common assumption in the literature that DOE's forecasts are based on a symmetric underlying loss function with respect to positive vs. negative forecast errors. Instead, we opt for the methodology of Elliott et al. (2005) that allows testing the joint hypothesis of an asymmetric loss function and rationality and reveals the underlying preferences of the forecaster. Results indicate the existence of asymmetries in the shape of the loss function for most energy categories with preferences leaning towards optimism. Moreover, we also examine whether there is a structural break in those preferences over the examined period, 1997–2012. - Highlights: • Examine the rationality of DOE energy forecasts. • Departing from a symmetric underlying loss function. • Asymmetries exist in most energy prices. • Preferences lean towards optimism. • Examine structural breaks in those preferences

  6. Electric power demand forecasting using interval time series. A comparison between VAR and iMLP

    International Nuclear Information System (INIS)

    Garcia-Ascanio, Carolina; Mate, Carlos

    2010-01-01

    Electric power demand forecasts play an essential role in the electric industry, as they provide the basis for making decisions in power system planning and operation. A great variety of mathematical methods have been used for demand forecasting. The development and improvement of appropriate mathematical tools will lead to more accurate demand forecasting techniques. In order to forecast the monthly electric power demand per hour in Spain for 2 years, this paper presents a comparison between a new forecasting approach considering vector autoregressive (VAR) forecasting models applied to interval time series (ITS) and the iMLP, the multi-layer perceptron model adapted to interval data. In the proposed comparison, for the VAR approach two models are fitted per every hour, one composed of the centre (mid-point) and radius (half-range), and another one of the lower and upper bounds according to the interval representation assumed by the ITS in the learning set. In the case of the iMLP, only the model composed of the centre and radius is fitted. The other interval representation composed of the lower and upper bounds is obtained from the linear combination of the two. This novel approach, obtaining two bivariate models each hour, makes possible to establish, for different periods in the day, which interval representation is more accurate. Furthermore, the comparison between two different techniques adapted to interval time series allows us to determine the efficiency of these models in forecasting electric power demand. It is important to note that the iMLP technique has been selected for the comparison, as it has shown its accuracy in forecasting daily electricity price intervals. This work shows the ITS forecasting methods as a potential tool that will lead to a reduction in risk when making power system planning and operational decisions. (author)

  7. Statistical model for forecasting uranium prices to estimate the nuclear fuel cycle cost

    International Nuclear Information System (INIS)

    Kim, Sung Ki; Ko, Won Il; Nam, Hyoon; Kim, Chul Min; Chung, Yang Hon; Bang, Sung Sig

    2017-01-01

    This paper presents a method for forecasting future uranium prices that is used as input data to calculate the uranium cost, which is a rational key cost driver of the nuclear fuel cycle cost. In other words, the statistical autoregressive integrated moving average (ARIMA) model and existing engineering cost estimation method, the so-called escalation rate model, were subjected to a comparative analysis. When the uranium price was forecasted in 2015, the margin of error of the ARIMA model forecasting was calculated and found to be 5.4%, whereas the escalation rate model was found to have a margin of error of 7.32%. Thus, it was verified that the ARIMA model is more suitable than the escalation rate model at decreasing uncertainty in nuclear fuel cycle cost calculation

  8. Statistical model for forecasting uranium prices to estimate the nuclear fuel cycle cost

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Sung Ki; Ko, Won Il; Nam, Hyoon [Nuclear Fuel Cycle Analysis, Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Kim, Chul Min; Chung, Yang Hon; Bang, Sung Sig [Korea Advanced Institute of Science and Technology, Daejeon (Korea, Republic of)

    2017-08-15

    This paper presents a method for forecasting future uranium prices that is used as input data to calculate the uranium cost, which is a rational key cost driver of the nuclear fuel cycle cost. In other words, the statistical autoregressive integrated moving average (ARIMA) model and existing engineering cost estimation method, the so-called escalation rate model, were subjected to a comparative analysis. When the uranium price was forecasted in 2015, the margin of error of the ARIMA model forecasting was calculated and found to be 5.4%, whereas the escalation rate model was found to have a margin of error of 7.32%. Thus, it was verified that the ARIMA model is more suitable than the escalation rate model at decreasing uncertainty in nuclear fuel cycle cost calculation.

  9. Robust estimation and forecasting of the long-term seasonal component of electricity spot prices

    International Nuclear Information System (INIS)

    Nowotarski, Jakub; Tomczyk, Jakub; Weron, Rafał

    2013-01-01

    We present the results of an extensive study on estimation and forecasting of the long-term seasonal component (LTSC) of electricity spot prices. We consider a battery of over 300 models, including monthly dummies and models based on Fourier or wavelet decomposition combined with linear or exponential decay. We find that the considered wavelet-based models are significantly better in terms of forecasting spot prices up to a year ahead than the commonly used monthly dummies and sine-based models. This result questions the validity and usefulness of stochastic models of spot electricity prices built on the latter two types of LTSC models. - Highlights: • First comprehensive study on the forecasting of the long-term seasonal components • Over 300 models examined, including commonly used and new approaches • Wavelet-based models outperform sine-based and monthly dummy models. • Validity of stochastic models built on sines or monthly dummies is questionable

  10. Electricity Price Forecast Using Combined Models with Adaptive Weights Selected and Errors Calibrated by Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Da Liu

    2013-01-01

    Full Text Available A combined forecast with weights adaptively selected and errors calibrated by Hidden Markov model (HMM is proposed to model the day-ahead electricity price. Firstly several single models were built to forecast the electricity price separately. Then the validation errors from every individual model were transformed into two discrete sequences: an emission sequence and a state sequence to build the HMM, obtaining a transmission matrix and an emission matrix, representing the forecasting ability state of the individual models. The combining weights of the individual models were decided by the state transmission matrixes in HMM and the best predict sample ratio of each individual among all the models in the validation set. The individual forecasts were averaged to get the combining forecast with the weights obtained above. The residuals of combining forecast were calibrated by the possible error calculated by the emission matrix of HMM. A case study of day-ahead electricity market of Pennsylvania-New Jersey-Maryland (PJM, USA, suggests that the proposed method outperforms individual techniques of price forecasting, such as support vector machine (SVM, generalized regression neural networks (GRNN, day-ahead modeling, and self-organized map (SOM similar days modeling.

  11. Deterministic Echo State Networks Based Stock Price Forecasting

    Directory of Open Access Journals (Sweden)

    Jingpei Dan

    2014-01-01

    Full Text Available Echo state networks (ESNs, as efficient and powerful computational models for approximating nonlinear dynamical systems, have been successfully applied in financial time series forecasting. Reservoir constructions in standard ESNs rely on trials and errors in real applications due to a series of randomized model building stages. A novel form of ESN with deterministically constructed reservoir is competitive with standard ESN by minimal complexity and possibility of optimizations for ESN specifications. In this paper, forecasting performances of deterministic ESNs are investigated in stock price prediction applications. The experiment results on two benchmark datasets (Shanghai Composite Index and S&P500 demonstrate that deterministic ESNs outperform standard ESN in both accuracy and efficiency, which indicate the prospect of deterministic ESNs for financial prediction.

  12. Distributional modeling and short-term forecasting of electricity prices by Generalized Additive Models for Location, Scale and Shape

    International Nuclear Information System (INIS)

    Serinaldi, Francesco

    2011-01-01

    In the context of the liberalized and deregulated electricity markets, price forecasting has become increasingly important for energy company's plans and market strategies. Within the class of the time series models that are used to perform price forecasting, the subclasses of methods based on stochastic time series and causal models commonly provide point forecasts, whereas the corresponding uncertainty is quantified by approximate or simulation-based confidence intervals. Aiming to improve the uncertainty assessment, this study introduces the Generalized Additive Models for Location, Scale and Shape (GAMLSS) to model the dynamically varying distribution of prices. The GAMLSS allow fitting a variety of distributions whose parameters change according to covariates via a number of linear and nonlinear relationships. In this way, price periodicities, trends and abrupt changes characterizing both the position parameter (linked to the expected value of prices), and the scale and shape parameters (related to price volatility, skewness, and kurtosis) can be explicitly incorporated in the model setup. Relying on the past behavior of the prices and exogenous variables, the GAMLSS enable the short-term (one-day ahead) forecast of the entire distribution of prices. The approach was tested on two datasets from the widely studied California Power Exchange (CalPX) market, and the less mature Italian Power Exchange (IPEX). CalPX data allow comparing the GAMLSS forecasting performance with published results obtained by different models. The study points out that the GAMLSS framework can be a flexible alternative to several linear and nonlinear stochastic models. - Research Highlights: ► Generalized Additive Models for Location, Scale and Shape (GAMLSS) are used to model electricity prices' time series. ► GAMLSS provide the entire dynamicaly varying distribution function of prices resorting to a suitable set of covariates that drive the instantaneous values of the parameters

  13. Synthetic river flow time series generator for dispatch and spot price forecast

    International Nuclear Information System (INIS)

    Flores, R.A.

    2007-01-01

    Decision-making in electricity markets is complicated by uncertainties in demand growth, power supplies and fuel prices. In Peru, where the electrical power system is highly dependent on water resources at dams and river flows, hydrological uncertainties play a primary role in planning, price and dispatch forecast. This paper proposed a signal processing method for generating new synthetic river flow time series as a support for planning and spot market price forecasting. River flow time series are natural phenomena representing a continuous-time domain process. As an alternative synthetic representation of the original river flow time series, this proposed signal processing method preserves correlations, basic statistics and seasonality. It takes into account deterministic, periodic and non periodic components such as those due to the El Nino Southern Oscillation phenomenon. The new synthetic time series has many correlations with the original river flow time series, rendering it suitable for possible replacement of the classical method of sorting historical river flow time series. As a dispatch and planning approach to spot pricing, the proposed method offers higher accuracy modeling by decomposing the signal into deterministic, periodic, non periodic and stochastic sub signals. 4 refs., 4 tabs., 13 figs

  14. Market efficiency, cross hedging and price forecasts: California's natural-gas markets

    International Nuclear Information System (INIS)

    Woo, C.K.; Olson, A.; Horowitz, I.

    2006-01-01

    An extensive North American pipeline grid that physically integrates individual natural-gas markets, in conjunction with economic ties binding the California markets to those at Henry Hub, Louisiana and the New York mercantile exchange via an array of financial instruments, suggests that the spot prices at Henry Hub will impact those in California. We verify the suggestion via a partial-adjustment regression model, thus affirming that California traders can exploit the cross-hedging opportunities made available to them via market integration with Henry Hub, and that they can accurately forecast the price they will have to pay to meet future demand based solely on the price of futures at Henry Hub and the price of a California natural-gas basis swaps contract. (author)

  15. Foreign currency rate forecasting using neural networks

    Science.gov (United States)

    Pandya, Abhijit S.; Kondo, Tadashi; Talati, Amit; Jayadevappa, Suryaprasad

    2000-03-01

    Neural networks are increasingly being used as a forecasting tool in many forecasting problems. This paper discusses the application of neural networks in predicting daily foreign exchange rates between the USD, GBP as well as DEM. We approach the problem from a time-series analysis framework - where future exchange rates are forecasted solely using past exchange rates. This relies on the belief that the past prices and future prices are very close related, and interdependent. We present the result of training a neural network with historical USD-GBP data. The methodology used in explained, as well as the training process. We discuss the selection of inputs to the network, and present a comparison of using the actual exchange rates and the exchange rate differences as inputs. Price and rate differences are the preferred way of training neural network in financial applications. Results of both approaches are present together for comparison. We show that the network is able to learn the trends in the exchange rate movements correctly, and present the results of the prediction over several periods of time.

  16. Entity’s Irregular Demand Scheduling of the Wholesale Electricity Market based on the Forecast of Hourly Price Ratios

    Directory of Open Access Journals (Sweden)

    O. V. Russkov

    2015-01-01

    Full Text Available The article considers a hot issue to forecast electric power demand amounts and prices for the entities of wholesale electricity market (WEM, which are in capacity of a large user with production technology requirements prevailing over hourly energy planning ones. An electric power demand of such entities is on irregular schedule. The article analyses mathematical models, currently applied to forecast demand amounts and prices. It describes limits of time-series models and fundamental ones in case of hourly forecasting an irregular demand schedule of the electricity market entity. The features of electricity trading at WEM are carefully analysed. Factors that influence on irregularity of demand schedule of the metallurgical plant are shown. The article proposes method for the qualitative forecast of market price ratios as a tool to reduce a dependence on the accuracy of forecasting an irregular schedule of demand. It describes the differences between the offered method and the similar ones considered in research studies and scholarly works. The correlation between price ratios and relaxation in the requirements for the forecast accuracy of the electric power consumption is analysed. The efficiency function of forecast method is derived. The article puts an increased focus on description of the mathematical model based on the method of qualitative forecast. It shows main model parameters and restrictions the electricity market imposes on them. The model prototype is described as a programme module. Methods to assess an effectiveness of the proposed forecast model are examined. The positive test results of the model using JSC «Volzhsky Pipe Plant» data are given. A conclusion is drawn concerning the possibility to decrease dependence on the forecast accuracy of irregular schedule of entity’s demand at WEM. The effective trading tool has been found for the entities of irregular demand schedule at WEM. The tool application allows minimizing cost

  17. The Optimal Confidence Intervals for Agricultural Products’ Price Forecasts Based on Hierarchical Historical Errors

    Directory of Open Access Journals (Sweden)

    Yi Wang

    2016-12-01

    Full Text Available With the levels of confidence and system complexity, interval forecasts and entropy analysis can deliver more information than point forecasts. In this paper, we take receivers’ demands as our starting point, use the trade-off model between accuracy and informativeness as the criterion to construct the optimal confidence interval, derive the theoretical formula of the optimal confidence interval and propose a practical and efficient algorithm based on entropy theory and complexity theory. In order to improve the estimation precision of the error distribution, the point prediction errors are STRATIFIED according to prices and the complexity of the system; the corresponding prediction error samples are obtained by the prices stratification; and the error distributions are estimated by the kernel function method and the stability of the system. In a stable and orderly environment for price forecasting, we obtain point prediction error samples by the weighted local region and RBF (Radial basis function neural network methods, forecast the intervals of the soybean meal and non-GMO (Genetically Modified Organism soybean continuous futures closing prices and implement unconditional coverage, independence and conditional coverage tests for the simulation results. The empirical results are compared from various interval evaluation indicators, different levels of noise, several target confidence levels and different point prediction methods. The analysis shows that the optimal interval construction method is better than the equal probability method and the shortest interval method and has good anti-noise ability with the reduction of system entropy; the hierarchical estimation error method can obtain higher accuracy and better interval estimation than the non-hierarchical method in a stable system.

  18. Apples with apples: accounting for fuel price risk in comparisons of gas-fired and renewable generation

    Energy Technology Data Exchange (ETDEWEB)

    Bolinger, Mark; Wiser, Ryan

    2003-12-18

    For better or worse, natural gas has become the fuel of choice for new power plants being built across the United States. According to the US Energy Information Administration (EIA), natural gas combined-cycle and combustion turbine power plants accounted for 96% of the total generating capacity added in the US between 1999 and 2002--138 GW out of a total of 144 GW. Looking ahead, the EIA expects that gas-fired technology will account for 61% of the 355 GW new generating capacity projected to come on-line in the US up to 2025, increasing the nationwide market share of gas-fired generation from 18% in 2002 to 22% in 2025. While the data are specific to the US, natural gas-fired generation is making similar advances in other countries as well. Regardless of the explanation for (or interpretation of) the empirical findings, however, the basic implications remain the same: one should not blindly rely on gas price forecasts when comparing fixed-price renewable with variable-price gas-fired generation contracts. If there is a cost to hedging, gas price forecasts do not capture and account for it. Alternatively, if the forecasts are at risk of being biased or out of tune with the market, then one certainly would not want to use them as the basis for resource comparisons or investment decisions if a more certain source of data (forwards) existed. Accordingly, assuming that long-term price stability is valued, the most appropriate way to compare the levelized cost of these resources in both cases would be to use forward natural gas price data--i.e. prices that can be locked in to create price certainty--as opposed to uncertain natural gas price forecasts. This article suggests that had utilities and analysts in the US done so over the sample period from November 2000 to November 2003, they would have found gas-fired generation to be at least 0.3-0.6 cents/kWh more expensive (on a levelized cost basis) than otherwise thought. With some renewable resources, in particular wind

  19. Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets

    OpenAIRE

    Ziel, Florian; Steinert, Rick; Husmann, Sven

    2015-01-01

    In our paper we analyze the relationship between the day-ahead electricity price of the Energy Exchange Austria (EXAA) and other day-ahead electricity prices in Europe. We focus on markets, which settle their prices after the EXAA, which enables traders to include the EXAA price into their calculations. For each market we employ econometric models to incorporate the EXAA price and compare them with their counterparts without the price of the Austrian exchange. By employing a forecasting study...

  20. Comparison of control systems for the optimisation of ice storage in a dynamic real time electricity pricing environment

    International Nuclear Information System (INIS)

    Murphy, M.D.; O’Mahony, M.J.; Upton, J.

    2015-01-01

    forecasted day ahead real time electricity pricing environment. The energy savings achieved for 100% ice bank charge were low, but as the desired charge level was reduced to 75%, 50% and 25% the savings potential increased. The introduction of uncertain real time electricity pricing forecasts nullified any cost savings made by the load-shifting controller in comparison to the current state of the art

  1. Forecasting oil price movements with crack spread futures

    International Nuclear Information System (INIS)

    Murat, Atilim; Tokat, Ekin

    2009-01-01

    In oil markets, the crack spread refers to the crude-product price relationship. Refiners are major participants in oil markets and they are primarily exposed to the crack spread. In other words, refiner activity is substantially driven by the objective of protecting the crack spread. Moreover, oil consumers are active participants in the oil hedging market and they are frequently exposed to the crack spread. From another perspective, hedge funds are heavily using crack spread to speculate in oil markets. Based on the high volume of crack spread futures trading in oil markets, the question we want to raise is whether the crack spread futures can be a good predictor of oil price movements. We investigated first whether there is a causal relationship between the crack spread futures and the spot oil markets in a vector error correction framework. We found the causal impact of crack spread futures on spot oil market both in the long- and the short-run after April 2003 where we detected a structural break in the model. To examine the forecasting performance, we use the random walk model (RWM) as a benchmark, and we also evaluate the forecasting power of crack spread futures against the crude oil futures. The results showed that (a) both the crack spread futures and the crude oil futures outperformed the RWM; and (b) the crack spread futures are almost as good as the crude oil futures in predicting the movements in spot oil markets. (author)

  2. An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

    Directory of Open Access Journals (Sweden)

    Ping-Huan Kuo

    2018-04-01

    Full Text Available Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN and the Long Short Term Memory (LSTM. In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE and Root-Mean-Square error (RMSE evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper.

  3. Price formation in electricity forward markets and the relevance of systematic forecast errors

    International Nuclear Information System (INIS)

    Redl, Christian; Haas, Reinhard; Huber, Claus; Boehm, Bernhard

    2009-01-01

    Since the liberalisation of the European electricity sector, forward and futures contracts have gained significant interest of market participants due to risk management reasons. For pricing of these contracts an important fact concerns the non-storability of electricity. In this case, according to economic theory, forward prices are related to the expected spot prices which are built on fundamental market expectations. In the following article the crucial impact parameters of forward electricity prices and the relationship between forward and future spot prices will be assessed by an empirical analysis of electricity prices at the European Energy Exchange and the Nord Pool Power Exchange. In fact, price formation in the considered markets is influenced by historic spot market prices yielding a biased forecasting power of long-term contracts. Although market and risk assessment measures of market participants and supply and demand shocks can partly explain the futures-spot bias inefficiencies in the analysed forward markets cannot be ruled out. (author)

  4. Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

    Science.gov (United States)

    Shabri, Ani; Samsudin, Ruhaidah

    2014-01-01

    Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series. PMID:24895666

  5. Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis.

    Science.gov (United States)

    Shabri, Ani; Samsudin, Ruhaidah

    2014-01-01

    Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

  6. Forecasting Construction Tender Price Index in Ghana using Autoregressive Integrated Moving Average with Exogenous Variables Model

    Directory of Open Access Journals (Sweden)

    Ernest Kissi

    2018-03-01

    Full Text Available Prices of construction resources keep on fluctuating due to unstable economic situations that have been experienced over the years. Clients knowledge of their financial commitments toward their intended project remains the basis for their final decision. The use of construction tender price index provides a realistic estimate at the early stage of the project. Tender price index (TPI is influenced by various economic factors, hence there are several statistical techniques that have been employed in forecasting. Some of these include regression, time series, vector error correction among others. However, in recent times the integrated modelling approach is gaining popularity due to its ability to give powerful predictive accuracy. Thus, in line with this assumption, the aim of this study is to apply autoregressive integrated moving average with exogenous variables (ARIMAX in modelling TPI. The results showed that ARIMAX model has a better predictive ability than the use of the single approach. The study further confirms the earlier position of previous research of the need to use the integrated model technique in forecasting TPI. This model will assist practitioners to forecast the future values of tender price index. Although the study focuses on the Ghanaian economy, the findings can be broadly applicable to other developing countries which share similar economic characteristics.

  7. Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

    Science.gov (United States)

    Kanchymalay, Kasturi; Salim, N.; Sukprasert, Anupong; Krishnan, Ramesh; Raba'ah Hashim, Ummi

    2017-08-01

    The aim of this paper was to study the correlation between crude palm oil (CPO) price, selected vegetable oil prices (such as soybean oil, coconut oil, and olive oil, rapeseed oil and sunflower oil), crude oil and the monthly exchange rate. Comparative analysis was then performed on CPO price forecasting results using the machine learning techniques. Monthly CPO prices, selected vegetable oil prices, crude oil prices and monthly exchange rate data from January 1987 to February 2017 were utilized. Preliminary analysis showed a positive and high correlation between the CPO price and soy bean oil price and also between CPO price and crude oil price. Experiments were conducted using multi-layer perception, support vector regression and Holt Winter exponential smoothing techniques. The results were assessed by using criteria of root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE) and Direction of accuracy (DA). Among these three techniques, support vector regression(SVR) with Sequential minimal optimization (SMO) algorithm showed relatively better results compared to multi-layer perceptron and Holt Winters exponential smoothing method.

  8. Market data analysis and short-term price forecasting in the Iran electricity market with pay-as-bid payment mechanism

    International Nuclear Information System (INIS)

    Bigdeli, N.; Afshar, K.; Amjady, N.

    2009-01-01

    Market data analysis and short-term price forecasting in Iran electricity market as a market with pay-as-bid payment mechanism has been considered in this paper. The data analysis procedure includes both correlation and predictability analysis of the most important load and price indices. The employed data are the experimental time series from Iran electricity market in its real size and is long enough to make it possible to take properties such as non-stationarity of market into account. For predictability analysis, the bifurcation diagrams and recurrence plots of the data have been investigated. The results of these analyses indicate existence of deterministic chaos in addition to non-stationarity property of the system which implies short-term predictability. In the next step, two artificial neural networks have been developed for forecasting the two price indices in Iran's electricity market. The models' input sets are selected regarding four aspects: the correlation properties of the available data, the critiques of Iran's electricity market, a proper convergence rate in case of sudden variations in the market price behavior, and the omission of cumulative forecasting errors. The simulation results based on experimental data from Iran electricity market are representative of good performance of the developed neural networks in coping with and forecasting of the market behavior, even in the case of severe volatility in the market price indices. (author)

  9. Target Price Accuracy

    Directory of Open Access Journals (Sweden)

    Alexander G. Kerl

    2011-04-01

    Full Text Available This study analyzes the accuracy of forecasted target prices within analysts’ reports. We compute a measure for target price forecast accuracy that evaluates the ability of analysts to exactly forecast the ex-ante (unknown 12-month stock price. Furthermore, we determine factors that explain this accuracy. Target price accuracy is negatively related to analyst-specific optimism and stock-specific risk (measured by volatility and price-to-book ratio. However, target price accuracy is positively related to the level of detail of each report, company size and the reputation of the investment bank. The potential conflicts of interests between an analyst and a covered company do not bias forecast accuracy.

  10. Modelling the Errors of EIA’s Oil Prices and Production Forecasts by the Grey Markov Model

    Directory of Open Access Journals (Sweden)

    Gholam Hossein Hasantash

    2012-01-01

    Full Text Available Grey theory is about systematic analysis of limited information. The Grey-Markov model can improve the accuracy of forecast range in the random fluctuating data sequence. In this paper, we employed this model in energy system. The average errors of Energy Information Administrations predictions for world oil price and domestic crude oil production from 1982 to 2007 and from 1985 to 2008 respectively were used as two forecasted examples. We showed that the proposed Grey-Markov model can improve the forecast accuracy of original Grey forecast model.

  11. Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

    Directory of Open Access Journals (Sweden)

    Ani Shabri

    2014-01-01

    Full Text Available Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI, has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

  12. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

    International Nuclear Information System (INIS)

    Yu, Lean; Wang, Shouyang; Lai, Kin Keung

    2008-01-01

    In this study, an empirical mode decomposition (EMD) based neural network ensemble learning paradigm is proposed for world crude oil spot price forecasting. For this purpose, the original crude oil spot price series were first decomposed into a finite, and often small, number of intrinsic mode functions (IMFs). Then a three-layer feed-forward neural network (FNN) model was used to model each of the extracted IMFs, so that the tendencies of these IMFs could be accurately predicted. Finally, the prediction results of all IMFs are combined with an adaptive linear neural network (ALNN), to formulate an ensemble output for the original crude oil price series. For verification and testing, two main crude oil price series, West Texas Intermediate (WTI) crude oil spot price and Brent crude oil spot price, are used to test the effectiveness of the proposed EMD-based neural network ensemble learning methodology. Empirical results obtained demonstrate attractiveness of the proposed EMD-based neural network ensemble learning paradigm. (author)

  13. A computationally efficient electricity price forecasting model for real time energy markets

    International Nuclear Information System (INIS)

    Feijoo, Felipe; Silva, Walter; Das, Tapas K.

    2016-01-01

    Highlights: • A fast hybrid forecast model for electricity prices. • Accurate forecast model that combines K-means and machine learning techniques. • Low computational effort by elimination of feature selection techniques. • New benchmark results by using market data for year 2012 and 2015. - Abstract: Increased significance of demand response and proliferation of distributed energy resources will continue to demand faster and more accurate models for forecasting locational marginal prices. This paper presents such a model (named K-SVR). While yielding prediction accuracy comparable with the best known models in the literature, K-SVR requires a significantly reduced computational time. The computational reduction is attained by eliminating the use of a feature selection process, which is commonly used by the existing models in the literature. K-SVR is a hybrid model that combines clustering algorithms, support vector machine, and support vector regression. K-SVR is tested using Pennsylvania–New Jersey–Maryland market data from the periods 2005–6, 2011–12, and 2014–15. Market data from 2006 has been used to measure performance of many of the existing models. Authors chose these models to compare performance and demonstrate strengths of K-SVR. Results obtained from K-SVR using the market data from 2012 and 2015 are new, and will serve as benchmark for future models.

  14. International price comparisons for pharmaceuticals. Measurement and policy issues.

    Science.gov (United States)

    Danzon, P M; Kim, J D

    1998-01-01

    Cross-national price comparisons for pharmaceuticals are commonly used for two purposes. Comparisons based on a sample of products are used to draw conclusions about differences in average price levels. Cross-national comparisons applied to individual products are also used by governments to set domestic prices. This paper examines the major methodological issues raised by international price comparisons, focusing on measurement of differences in average price levels and the validity of policy conclusions drawn from such price comparison studies. It argues that valid measures of average price levels can only be obtained from comparisons based on a comprehensive or representative sample of products, appropriately weighted, following standard index number methods. Comparisons of individual product prices should take into account the manufacturer's entire product portfolio over time rather than focus narrowly on a single product at a point in time. Because of the great variation across countries in both the range of drug compounds available and the dosage forms, strengths and pack sizes for each compound, obtaining a broadly comprehensive or representative sample is problematical. If products are required to match on all dimensions, including molecule, manufacturer, strength and pack, as is common in most international price comparisons, then only a very small and unrepresentative sample of the drugs available in each country can be included in the analysis. A trade-off between the desire to compare only identical products and the need to compare a truly representative sample of a country's pharmaceutical market is therefore necessary. A valid comparison of average drug prices should include generics and over-the-counter products that are good substitutes for branded prescription drugs, with all forms, strengths and packs. To achieve this broad representation, however, the requirements of same manufacturer, same brand, dosage form, strength and pack size must be

  15. ℓ(p)-Norm multikernel learning approach for stock market price forecasting.

    Science.gov (United States)

    Shao, Xigao; Wu, Kun; Liao, Bifeng

    2012-01-01

    Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ(1)-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ(p)-norm multiple kernel support vector regression (1 ≤ p stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ(1)-norm multiple support vector regression model.

  16. Models of Investor Forecasting Behavior — Experimental Evidence

    Directory of Open Access Journals (Sweden)

    Federico Bonetto

    2017-12-01

    Full Text Available Different forecasting behaviors affect investors’ trading decisions and lead to qualitatively different asset price trajectories. It has been shown in the literature that the weights that investors place on observed asset price changes when forecasting future price changes, and the nature of their confidence when price changes are forecast, determine whether price bubbles, price crashes, and unpredictable price cycles occur. In this paper, we report the results of behavioral experiments involving multiple investors who participated in a market for a virtual asset. Our goal is to study investors’ forecast formation. We conducted three experimental sessions with different participants in each session. We fit different models of forecast formation to the observed data. There is strong evidence that the investors forecast future prices by extrapolating past price changes, even when they know the fundamental value of the asset exactly and the extrapolated forecasts differ significantly from the fundamental value. The rational expectations hypothesis seems inconsistent with the observed forecasts. The forecasting models of all participants that best fit the observed forecasting data were of the type that cause price bubbles and cycles in dynamical systems models, and price bubbles and cycles ended up occurring in all three sessions.

  17. A Study on the Determination of the World Crude Oil Price and Methods for Its Forecast

    Energy Technology Data Exchange (ETDEWEB)

    Kim, J.K. [Korea Energy Economics Institute, Euiwang (Korea)

    2001-11-01

    The primary purpose of this report is to provide the groundwork to develop the methods to forecast the world crude oil price. The methodology is used by both literature survey and empirical study. For this purpose, first of all, this report reviewed the present situation and the outlook of the world oil market based on oil demand, supply and prices. This analysis attempted to provide a deeper understanding to support the development of oil forecasting methods. The result of this review, in general, showed that the oil demand will be maintained annually at an average rate of around 2.4% under assumption that oil supply has no problem until 2020. The review showed that crude oil price will be a 3% increasing rate annually in the 1999 real term. This report used the contents of the summary review as reference data in order to link the KEEIOF model. In an effort to further investigate the contents of oil political economy, this report reviewed the articles of political economy about oil industry. It pointed out that the world oil industry is experiencing the change of restructuring oil industry after the Gulf War in 1990. The contents of restructuring oil industry are characterized by the 'open access' to resources not only in the Persian Gulf, but elsewhere in the world as well - especially the Caspian Sea Basin. In addition, the contents showed that the oil industries are shifted from government control to government and industry cooperation after the Gulf War. In order to examine the characters and the problems surrounding oil producing countries, this report described the model of OPEC behavior and strategy of oil management with political and military factors. Among examining the models of OPEC behavior, this report focused on hybrid model to explain OPEC behavior. In reviewing political and religious power structure in the Middle East, the report revealed that US emphasizes the importance of the Middle East for guaranteeing oil security. However, three

  18. A Hybrid Forecasting Model Based on Bivariate Division and a Backpropagation Artificial Neural Network Optimized by Chaos Particle Swarm Optimization for Day-Ahead Electricity Price

    Directory of Open Access Journals (Sweden)

    Zhilong Wang

    2014-01-01

    Full Text Available In the electricity market, the electricity price plays an inevitable role. Nevertheless, accurate price forecasting, a vital factor affecting both government regulatory agencies and public power companies, remains a huge challenge and a critical problem. Determining how to address the accurate forecasting problem becomes an even more significant task in an era in which electricity is increasingly important. Based on the chaos particle swarm optimization (CPSO, the backpropagation artificial neural network (BPANN, and the idea of bivariate division, this paper proposes a bivariate division BPANN (BD-BPANN method and the CPSO-BD-BPANN method for forecasting electricity price. The former method creatively transforms the electricity demand and price to be a new variable, named DV, which is calculated using the division principle, to forecast the day-ahead electricity by multiplying the forecasted values of the DVs and forecasted values of the demand. Next, to improve the accuracy of BD-BPANN, chaos particle swarm optimization and BD-BPANN are synthesized to form a novel model, CPSO-BD-BPANN. In this study, CPSO is utilized to optimize the initial parameters of BD-BPANN to make its output more stable than the original model. Finally, two forecasting strategies are proposed regarding different situations.

  19. Black-Scholes finite difference modeling in forecasting of call warrant prices in Bursa Malaysia

    Science.gov (United States)

    Mansor, Nur Jariah; Jaffar, Maheran Mohd

    2014-07-01

    Call warrant is a type of structured warrant in Bursa Malaysia. It gives the holder the right to buy the underlying share at a specified price within a limited period of time. The issuer of the structured warrants usually uses European style to exercise the call warrant on the maturity date. Warrant is very similar to an option. Usually, practitioners of the financial field use Black-Scholes model to value the option. The Black-Scholes equation is hard to solve analytically. Therefore the finite difference approach is applied to approximate the value of the call warrant prices. The central in time and central in space scheme is produced to approximate the value of the call warrant prices. It allows the warrant holder to forecast the value of the call warrant prices before the expiry date.

  20. Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations

    International Nuclear Information System (INIS)

    Wang, Jie; Wang, Jun

    2016-01-01

    In an attempt to improve the forecasting accuracy of crude oil price fluctuations, a new neural network architecture is established in this work which combines Multilayer perception and ERNN (Elman recurrent neural networks) with stochastic time effective function. ERNN is a time-varying predictive control system and is developed with the ability to keep memory of recent events in order to predict future output. The stochastic time effective function represents that the recent information has a stronger effect for the investors than the old information. With the established model the empirical research has a good performance in testing the predictive effects on four different time series indices. Compared to other models, the present model is possible to evaluate data from 1990s to today with extreme accuracy and speedy. The applied CID (complexity invariant distance) analysis and multiscale CID analysis, are provided as the new useful measures to evaluate a better predicting ability of the proposed model than other traditional models. - Highlights: • A new forecasting model is developed by a random Elman recurrent neural network. • The forecasting accuracy of crude oil price fluctuations is improved by the model. • The forecasting results of the proposed model are more accurate than compared models. • Two new distance analysis methods are applied to confirm the predicting results.

  1. Wind Power Forecasting Error Distributions: An International Comparison

    DEFF Research Database (Denmark)

    Hodge, Bri-Mathias; Lew, Debra; Milligan, Michael

    2012-01-01

    Wind power forecasting is essential for greater penetration of wind power into electricity systems. Because no wind forecasting system is perfect, a thorough understanding of the errors that may occur is a critical factor for system operation functions, such as the setting of operating reserve...... levels. This paper provides an international comparison of the distribution of wind power forecasting errors from operational systems, based on real forecast data. The paper concludes with an assessment of similarities and differences between the errors observed in different locations....

  2. Model documentation, Coal Market Module of the National Energy Modeling System

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1998-01-01

    This report documents the objectives and the conceptual and methodological approach used in the development of the National Energy Modeling System`s (NEMS) Coal Market Module (CMM) used to develop the Annual Energy Outlook 1998 (AEO98). This report catalogues and describes the assumptions, methodology, estimation techniques, and source code of CMM`s two submodules. These are the Coal Production Submodule (CPS) and the Coal Distribution Submodule (CDS). CMM provides annual forecasts of prices, production, and consumption of coal for NEMS. In general, the CDS integrates the supply inputs from the CPS to satisfy demands for coal from exogenous demand models. The international area of the CDS forecasts annual world coal trade flows from major supply to major demand regions and provides annual forecasts of US coal exports for input to NEMS. Specifically, the CDS receives minemouth prices produced by the CPS, demand and other exogenous inputs from other NEMS components, and provides delivered coal prices and quantities to the NEMS economic sectors and regions.

  3. Forecasting of palm oil price in Malaysia using linear and nonlinear methods

    Science.gov (United States)

    Nor, Abu Hassan Shaari Md; Sarmidi, Tamat; Hosseinidoust, Ehsan

    2014-09-01

    The first question that comes to the mind is: "How can we predict the palm oil price accurately?" This question is the authorities, policy makers and economist's question for a long period of time. The first reason is that in the recent years Malaysia showed a comparative advantage in palm oil production and has become top producer and exporter in the world. Secondly, palm oil price plays significant role in government budget and represents important source of income for Malaysia, which potentially can influence the magnitude of monetary policies and eventually have an impact on inflation. Thirdly, knowledge on the future trends would be helpful in the planning and decision making procedures and will generate precise fiscal and monetary policy. Daily data on palm oil prices along with the ARIMA models, neural networks and fuzzy logic systems are employed in this paper. Empirical findings indicate that the dynamic neural network of NARX and the hybrid system of ANFIS provide higher accuracy than the ARIMA and static neural network for forecasting the palm oil price in Malaysia.

  4. Modeling and forecasting foreign exchange daily closing prices with normal inverse Gaussian

    Science.gov (United States)

    Teneng, Dean

    2013-09-01

    We fit the normal inverse Gaussian(NIG) distribution to foreign exchange closing prices using the open software package R and select best models by Käärik and Umbleja (2011) proposed strategy. We observe that daily closing prices (12/04/2008 - 07/08/2012) of CHF/JPY, AUD/JPY, GBP/JPY, NZD/USD, QAR/CHF, QAR/EUR, SAR/CHF, SAR/EUR, TND/CHF and TND/EUR are excellent fits while EGP/EUR and EUR/GBP are good fits with a Kolmogorov-Smirnov test p-value of 0.062 and 0.08 respectively. It was impossible to estimate normal inverse Gaussian parameters (by maximum likelihood; computational problem) for JPY/CHF but CHF/JPY was an excellent fit. Thus, while the stochastic properties of an exchange rate can be completely modeled with a probability distribution in one direction, it may be impossible the other way around. We also demonstrate that foreign exchange closing prices can be forecasted with the normal inverse Gaussian (NIG) Lévy process, both in cases where the daily closing prices can and cannot be modeled by NIG distribution.

  5. CAViaR-based forecast for oil price risk

    International Nuclear Information System (INIS)

    Huang, Dashan; Yu, Baimin; Fabozzi, Frank J.; Fukushima, Masao

    2009-01-01

    As a benchmark for measuring market risk, value-at-risk (VaR) reduces the risk associated with any kind of asset to just a number (amount in terms of a currency), which can be well understood by regulators, board members, and other interested parties. This paper employs a new VaR approach due to Engle and Manganelli [Engle, R.F., Manganelli, S., 2004. CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles. Journal of Business and Economic Statistics 22, 367-381] to forecasting oil price risk. In doing so, we provide two original contributions by introducing a new exponentially weighted moving average CAViaR model and developing a mixed data regression model for multi-period VaR prediction. (author)

  6. Model developer`s appendix to the model documentation report: NEMS macroeconomic activity module

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1994-07-15

    The NEMS Macroeconomic Activity Module (MAM) tested here was used to generate the Annual Energy Outlook 1994 (AEO94). MAM is a response surface model, not a structural model, composed of three submodules: the National Submodule, the Interindustry Submodule, and the Regional Submodule. Contents of this report are as follows: properties of the mathematical solution; NEMS MAM empirical basis; and scenario analysis. Scenario analysis covers: expectations for scenario analysis; historical world oil price scenario; AEO94 high world oil price scenario; AEO94 low world oil price scenario; and immediate increase world oil price scenario.

  7. Scenarios for Benefits Analysis of Energy Research, Development,Demonstration and Deployment

    Energy Technology Data Exchange (ETDEWEB)

    Gumerman, Etan; Marnay, Chris

    2005-09-07

    difference was that LBNL started working with the AEO 2004NEMS code and EIA was using AEO 2005 NEMS code. Unlike the High Price Scenario the Carbon Cap scenario gives a radically different forecast than the Reference Scenario. NEMS-LBNL proved that it can handle these alternative scenarios. However, results are price inelastic (for both oil and natural gas prices) within the price range evaluated. Perhaps even higher price paths would lead to a distinctly different forecast than the Reference Scenario. On the other hand, the Carbon Cap Scenario behaves more like an alternative future. The future in the Carbon Cap Scenario has higher electricity prices, reduced driving, more renewable capacity, and reduced energy consumption. The next step for this work is to evaluate the EE benefits under each of the three scenarios. Comparing those three sets of predicted benefits will indicate how much uncertainty is inherent within this sort of deterministic forecasting.

  8. Forecasting Day-Ahead Electricity Prices : Utilizing Hourly Prices

    NARCIS (Netherlands)

    E. Raviv (Eran); K.E. Bouwman (Kees); D.J.C. van Dijk (Dick)

    2013-01-01

    textabstractThe daily average price of electricity represents the price of electricity to be delivered over the full next day and serves as a key reference price in the electricity market. It is an aggregate that equals the average of hourly prices for delivery during each of the 24 individual

  9. Forecasting Day-Ahead Electricity Prices: Utilizing Hourly Prices

    OpenAIRE

    Raviv, Eran; Bouwman, Kees E.; van Dijk, Dick

    2013-01-01

    This discussion paper led to a publication in 'Energy Economics' , 2015, 50, 227-239. The daily average price of electricity represents the price of electricity to be delivered over the full next day and serves as a key reference price in the electricity market. It is an aggregate that equals the average of hourly prices for delivery during each of the 24 individual hours. This paper demonstrates that the disaggregated hourly prices contain useful predictive information for the daily average ...

  10. Price Comparisons on the Internet Based on Computational Intelligence

    Science.gov (United States)

    Kim, Jun Woo; Ha, Sung Ho

    2014-01-01

    Information-intensive Web services such as price comparison sites have recently been gaining popularity. However, most users including novice shoppers have difficulty in browsing such sites because of the massive amount of information gathered and the uncertainty surrounding Web environments. Even conventional price comparison sites face various problems, which suggests the necessity of a new approach to address these problems. Therefore, for this study, an intelligent product search system was developed that enables price comparisons for online shoppers in a more effective manner. In particular, the developed system adopts linguistic price ratings based on fuzzy logic to accommodate user-defined price ranges, and personalizes product recommendations based on linguistic product clusters, which help online shoppers find desired items in a convenient manner. PMID:25268901

  11. Distributed Generation potential of the U.S. commercial sector

    International Nuclear Information System (INIS)

    Hamachi LaCommare, Kristina; Edwards, Jennifer L.; Gumerman, Etan; Marnay, Chris

    2005-01-01

    Small-scale (100 kW - 5 MW) on-site distributed generation (DG) economically driven by combined heat and power (CHP) applications and, in some cases, reliability concerns will likely emerge as a common feature of commercial building energy systems in developed countries over the next two decades. In the U.S., private and public expectations for this technology are heavily influenced by forecasts published by the Energy Information Administration (EIA), most notably the Annual Energy Outlook (AEO). EIA's forecasts are typically made using the National Energy Modeling System (NEMS), which has a forecasting module that predicts the penetration of several possible commercial building DG technologies over the period 2005-2025. Annual penetration is forecast by estimating the payback period for each technology, for each of a limited number of representative building types, for each of nine regions. This process results in an AEO2004 forecast deployment of about a total 3 GW of DG electrical generating capacity by 2025, which is only 0.25% of total forecast U.S. capacity. Analyses conducted using both the AEO2003 and AEO2004 versions of NEMS changes the baseline costs and performance characteristics of DG to reflect a world without U.S. Dept. of Energy (DOE) research into several thermal DG technologies, which is then compared to a case with enhanced technology representative of the successful achievement of DOE research goals. The net difference in 2025 DG penetration is dramatic using the AEO2003 version of NEMS, but much smaller in the AEO2004 version. The significance and validity of these contradictory results are discussed, and possibilities for improving estimates of commercial U.S. DG potential are explored

  12. Deriving inflation forecasts from government bond prices

    Directory of Open Access Journals (Sweden)

    Kožul Nataša

    2014-01-01

    Full Text Available In financial research and practice, it is widely accepted that nominal interest rates derived from the prices of various financial products of different maturities comprise of corresponding real interest rates and inflation. While extensive research has been conducted on the relationship between these three variables, estimation of their levels is still largely based on the industry surveys and market data. As this information only indicates the current expectations of interest rate and inflation movements over time, a number of caveats should be noted when interpreting such measures. In the US and the UK, where the government bond markets are the largest and most active, a comparative analysis between conventional government bonds and those whose yield is linked to inflation provides a measure of inflation expectations. However, as such analyses implicitly assume that investment in government bonds is virtually risk free, it is questionable whether the derived estimates are of any value in current economic conditions. Moreover, this approach cannot be generalized to other countries, where number of traded products from which any relationship between interest rates and inflation can be determined is limited and different economic conditions prevail. Thus, this paper aims to present an overview of the methodologies used to forecast inflation rates from government bond prices, drawing attention to the key assumptions and limitations of these approaches. The goal is to ascertain their accuracy, and thus their value in determining the real yields of various interest rate-linked products.

  13. Forecasting Housing Approvals in Australia: Do Forecasters Herd?

    DEFF Research Database (Denmark)

    Stadtmann, Georg; Pierdzioch; Rülke

    2012-01-01

    Price trends in housing markets may reflect herding of market participants. A natural question is whether such herding, to the extent that it occurred, reflects herding in forecasts of professional forecasters. Using more than 6,000 forecasts of housing approvals for Australia, we did not find...

  14. Outlook and overview of the North American marketplace: 1997-2015

    International Nuclear Information System (INIS)

    Sitzer, S.

    1997-01-01

    A series of projections for natural gas prices and production patterns in North America to the year 2015 were presented, based on the 'Annual Energy Outlook 1997' (AEO97) and on the 'International Energy Outlook 1996' (IEO96), two publications of the US Energy Information Administration. AEO's primary focus is the U.S. domestic situation, whereas the IEO provides international forecasts and analysis. In general, it is expected that while the growth in U.S. demand will put severe strain on North American natural gas reserves, production and delivery systems, deregulation will provide the incentives to maintain the improvements in efficiency and deliverability that have characterized gas supply over the past 10 years. To buttress these predictions, a series of analysis on U.S. economic growth, energy demand, world oil prices, U.S. electricity generation and cogeneration by fuel type, U.S. natural gas supply and prices were modelled under different price scenarios. Estimates included Canadian and Mexican reserves, storage and productive capacities in the overall North American energy picture. It was concluded that because of the relatively abundant supply and low environmental emissions, natural gas will continue to play a key role in North American energy markets. 4 refs., 13 figs

  15. On the electrification of road transport - Learning rates and price forecasts for hybrid-electric and battery-electric vehicles

    International Nuclear Information System (INIS)

    Weiss, Martin; Patel, Martin K.; Junginger, Martin; Perujo, Adolfo; Bonnel, Pierre; Grootveld, Geert van

    2012-01-01

    Hybrid-electric vehicles (HEVs) and battery-electric vehicles (BEVs) are currently more expensive than conventional passenger cars but may become cheaper due to technological learning. Here, we obtain insight into the prospects of future price decline by establishing ex-post learning rates for HEVs and ex-ante price forecasts for HEVs and BEVs. Since 1997, HEVs have shown a robust decline in their price and price differential at learning rates of 7±2% and 23±5%, respectively. By 2010, HEVs were only 31±22 € 2010 kW −1 more expensive than conventional cars. Mass-produced BEVs are currently introduced into the market at prices of 479±171 € 2010 kW −1 , which is 285±213 € 2010 kW −1 and 316±209 € 2010 kW −1 more expensive than HEVs and conventional cars. Our forecast suggests that price breakeven with these vehicles may only be achieved by 2026 and 2032, when 50 and 80 million BEVs, respectively, would have been produced worldwide. We estimate that BEVs may require until then global learning investments of 100–150 billion € which is less than the global subsidies for fossil fuel consumption paid in 2009. These findings suggest that HEVs, including plug-in HEVs, could become the dominant vehicle technology in the next two decades, while BEVs may require long-term policy support. - Highlights: ► Learning rates for hybrid-electric and battery-electric vehicles. ► Prices and price differentials of hybrid-electric vehicles show a robust decline. ► Battery-electric vehicles may require policy support for decades.

  16. THE LONG MEMORY PROPERTY OF HUNGARIAN MARKET PIG PRICES: A COMPARISON OF THREE DIFFERENT METHODS

    Directory of Open Access Journals (Sweden)

    SÁNDOR KOVÁCS

    2012-10-01

    Full Text Available The present study investigates the long memory property of market pig prices. Simply knowing that these time series have long term dependence could have strong significance when forecasting prices. The presence of long memory is crucial information in making business decisions and creating portfolios. Long memory can be measured by calculating the so-called Hurst exponent. In our article, we studied and described three different methods (Rescaled range, Detrended Fluctuation Analysis, Autoregressive Fractionally Integrated Moving Average. Data consist of four time series (piglet, young pig, sow, slaughter pig between 1991 and 2011. Before conducting the econometric analysis, all the series were seasonally adjusted using the TRAMO/SEATS method. Data preparation was followed by differencing the time series and testing their normality and stationarity. In the next step, we divided the analysed period into four parts and determined the Hurst exponent for each sub-period, using all three methods. In summary, results showed that slaughter pig prices are random; pig and piglet prices developed similarly and have long memory, while sow price changes definitely have short memory. Among the methods of pinpointing long term memory, ARFIMA was used for making the forecast. The forecasting ability of the method was compared to the traditional ARIMA model, with ARFIMA proving to be the better of the two.

  17. Stock price forecasting for companies listed on Tehran stock exchange using multivariate adaptive regression splines model and semi-parametric splines technique

    Science.gov (United States)

    Rounaghi, Mohammad Mahdi; Abbaszadeh, Mohammad Reza; Arashi, Mohammad

    2015-11-01

    One of the most important topics of interest to investors is stock price changes. Investors whose goals are long term are sensitive to stock price and its changes and react to them. In this regard, we used multivariate adaptive regression splines (MARS) model and semi-parametric splines technique for predicting stock price in this study. The MARS model as a nonparametric method is an adaptive method for regression and it fits for problems with high dimensions and several variables. semi-parametric splines technique was used in this study. Smoothing splines is a nonparametric regression method. In this study, we used 40 variables (30 accounting variables and 10 economic variables) for predicting stock price using the MARS model and using semi-parametric splines technique. After investigating the models, we select 4 accounting variables (book value per share, predicted earnings per share, P/E ratio and risk) as influencing variables on predicting stock price using the MARS model. After fitting the semi-parametric splines technique, only 4 accounting variables (dividends, net EPS, EPS Forecast and P/E Ratio) were selected as variables effective in forecasting stock prices.

  18. Arima model and exponential smoothing method: A comparison

    Science.gov (United States)

    Wan Ahmad, Wan Kamarul Ariffin; Ahmad, Sabri

    2013-04-01

    This study shows the comparison between Autoregressive Moving Average (ARIMA) model and Exponential Smoothing Method in making a prediction. The comparison is focused on the ability of both methods in making the forecasts with the different number of data sources and the different length of forecasting period. For this purpose, the data from The Price of Crude Palm Oil (RM/tonne), Exchange Rates of Ringgit Malaysia (RM) in comparison to Great Britain Pound (GBP) and also The Price of SMR 20 Rubber Type (cents/kg) with three different time series are used in the comparison process. Then, forecasting accuracy of each model is measured by examinethe prediction error that producedby using Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute deviation (MAD). The study shows that the ARIMA model can produce a better prediction for the long-term forecasting with limited data sources, butcannot produce a better prediction for time series with a narrow range of one point to another as in the time series for Exchange Rates. On the contrary, Exponential Smoothing Method can produce a better forecasting for Exchange Rates that has a narrow range of one point to another for its time series, while itcannot produce a better prediction for a longer forecasting period.

  19. High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets

    Science.gov (United States)

    Chen, Tai-Liang; Cheng, Ching-Hsue; Teoh, Hia-Jong

    2008-02-01

    Stock investors usually make their short-term investment decisions according to recent stock information such as the late market news, technical analysis reports, and price fluctuations. To reflect these short-term factors which impact stock price, this paper proposes a comprehensive fuzzy time-series, which factors linear relationships between recent periods of stock prices and fuzzy logical relationships (nonlinear relationships) mined from time-series into forecasting processes. In empirical analysis, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) and HSI (Heng Seng Index) are employed as experimental datasets, and four recent fuzzy time-series models, Chen’s (1996), Yu’s (2005), Cheng’s (2006) and Chen’s (2007), are used as comparison models. Besides, to compare with conventional statistic method, the method of least squares is utilized to estimate the auto-regressive models of the testing periods within the databases. From analysis results, the performance comparisons indicate that the multi-period adaptation model, proposed in this paper, can effectively improve the forecasting performance of conventional fuzzy time-series models which only factor fuzzy logical relationships in forecasting processes. From the empirical study, the traditional statistic method and the proposed model both reveal that stock price patterns in the Taiwan stock and Hong Kong stock markets are short-term.

  20. THE ANALYSIS OF THE COMMODITY PRICE FORECASTING SUCCESS CONSIDERING DIFFERENT LENGTHS OF THE INITIAL CONDITION DRIFT

    Directory of Open Access Journals (Sweden)

    Marcela Lascsáková

    2015-09-01

    Full Text Available In the paper the numerical model based on the exponential approximation of commodity stock exchanges was derived. The price prognoses of aluminium on the London Metal Exchange were determined as numerical solution of the Cauchy initial problem for the 1st order ordinary differential equation. To make the numerical model more accurate the idea of the modification of the initial condition value by the stock exchange was realized. By having analyzed the forecasting success of the chosen initial condition drift types, the initial condition drift providing the most accurate prognoses for the commodity price movements was determined. The suggested modification of the original model made the commodity price prognoses more accurate.

  1. Dynamical behavior of price forecasting in structures of group correlations

    Science.gov (United States)

    Lim, Kyuseong; Kim, Soo Yong; Kim, Kyungsik

    2015-07-01

    We investigate the prediction of the future prices from the structures and the networks of the companies in special financial groups. After the financial group network has been constructed from the value of the high cross-correlation, each company in a group is simulated and analyzed how it buys or sells stock is anaylzed and how it makes rational investments is forecasted. In the shortmemory behavior rather than the long-memory behavior, each company among a group can make a rational investment decision by using a stochastic evolution rule in the financial network. In particular, we simulate and analyze the investment situation in connection with the empirical data and the simulated result.

  2. Effects of the uncertainty of energy price and water availability forecasts on the operation of Alpine hydropower reservoir systems

    Science.gov (United States)

    Anghileri, D.; Castelletti, A.; Burlando, P.

    2016-12-01

    European energy markets have experienced dramatic changes in the last years because of the massive introduction of Variable Renewable Sources (VRSs), such as wind and solar power sources, in the generation portfolios in many countries. VRSs i) are intermittent, i.e., their production is highly variable and only partially predictable, ii) are characterized by no correlation between production and demand, iii) have negligible costs of production, and iv) have been largely subsidized. These features result in lower energy prices, but, at the same time, in increased price volatility, and in network stability issues, which pose a threat to traditional power sources because of smaller incomes and higher maintenance costs associated to a more flexible operation of power systems. Storage hydropower systems play an important role in compensating production peaks, both in term of excess and shortage of energy. Traditionally, most of the research effort in hydropower reservoir operation has focused on modeling and forecasting reservoir inflow as well as designing reservoir operation accordingly. Nowadays, price variability may be the largest source of uncertainty in the context of hydropower systems, especially when considering medium-to-large reservoirs, whose storage can easily buffer small inflow fluctuations. In this work, we compare the effects of uncertain inflow and energy price forecasts on hydropower production and profitability. By adding noise to historic inflow and price trajectories, we build a set of synthetic forecasts corresponding to different levels of predictability and assess their impact on reservoir operating policies and performances. The study is conducted on different hydropower systems, including storage systems and pumped-storage systems, with different characteristics, e.g., different inflow-capacity ratios. The analysis focuses on Alpine hydropower systems where the hydrological regime ranges from purely ice and snow-melt dominated to mixed snow

  3. A novel improved fuzzy support vector machine based stock price trend forecast model

    OpenAIRE

    Wang, Shuheng; Li, Guohao; Bao, Yifan

    2018-01-01

    Application of fuzzy support vector machine in stock price forecast. Support vector machine is a new type of machine learning method proposed in 1990s. It can deal with classification and regression problems very successfully. Due to the excellent learning performance of support vector machine, the technology has become a hot research topic in the field of machine learning, and it has been successfully applied in many fields. However, as a new technology, there are many limitations to support...

  4. Petroleum price

    International Nuclear Information System (INIS)

    Maurice, J.

    2001-01-01

    The oil market is the most volatile of all markets, with the exception of the Nasdaq. It is also the biggest commodity market in the world. Therefore one cannot avoid forecasting oil prices, nor can one expect to avoid the forecasting errors that have been made in the past. In his report, Joel Maurice draws a distinction between the short term and the medium-long term in analysing the outlook for oil prices. (author)

  5. On maximizing profit of wind-battery supported power station based on wind power and energy price forecasting

    DEFF Research Database (Denmark)

    Khalid, Muhammad; Aguilera, Ricardo P.; Savkin, Andrey V.

    2017-01-01

    This paper proposes a framework to develop an optimal power dispatch strategy for grid-connected wind power plants containing a Battery Energy Storage System (BESS). Considering the intermittent nature of wind power and rapidly varying electricity market price, short-term forecasting...... Dynamic Programming tool which can incorporate the predictions of both wind power and market price simultaneously as inputs in a receding horizon approach. The proposed strategy is validated using real electricity market price and wind power data in different scenarios of BESS power and capacity...... of these variables is used for efficient energy management. The predicted variability trends in market price assist in earning additional income which subsequently increase the operational profit. Then on the basis of income improvement, optimal capacity of the BESS can be determined. The proposed framework utilizes...

  6. Cancer Drugs: An International Comparison of Postlicensing Price Inflation.

    Science.gov (United States)

    Savage, Philip; Mahmoud, Sarah; Patel, Yogin; Kantarjian, Hagop

    2017-06-01

    The cost of cancer drugs forms a rising proportion of health care budgets worldwide. A number of studies have examined international comparisons of initial cost, but there is little work on postlicensing price increases. To examine this, we compared cancer drug prices at initial sale and subsequent price inflation in the United States and United Kingdom and also reviewed relevant price control mechanisms. The 10 top-selling cancer drugs were selected, and their prices at initial launch and in 2015 were compared. Standard nondiscounted prices were obtained from the relevant annual copies of the RED BOOK and the British National Formulary. At initial marketing, prices were on average 42% higher in the United States than in the United Kingdom. After licensing in the United States, all 10 drugs had price rises averaging an overall annual 8.8% (range, 1.4% to 24.1%) increase. In comparison, in the United Kingdom, six drugs had unchanged prices, two had decreased prices, and two had modest price increases. The overall annual increase in the United Kingdom was 0.24%. Cancer drug prices are rising substantially, both at their initial marketing price and, in the United States, at postlicensing prices. In the United Kingdom, the Pharmaceutical Price Regulation Scheme, an agreement between the government and the pharmaceutical industry, controls health care costs while allowing a return on investment and funds for research. The increasing costs of cancer drugs are approaching the limits of sustainability, and a similar government-industry agreement may allow stability for both health care provision and the pharmaceutical industry in the United States.

  7. Competitive Pricing by a Price Leader

    OpenAIRE

    Abhik Roy; Dominique M. Hanssens; Jagmohan S. Raju

    1994-01-01

    We examine the problem of pricing in a market where one brand acts as a price leader. We develop a procedure to estimate a leader's price rule, which is optimal given a sales target objective, and allows for the inclusion of demand forecasts. We illustrate our estimation procedure by calibrating this optimal price rule for both the leader and the follower using data on past sales and prices from the mid-size sedan segment of the U.S. automobile market. Our results suggest that a leader-follow...

  8. Comparison of two new short-term wind-power forecasting systems

    Energy Technology Data Exchange (ETDEWEB)

    Ramirez-Rosado, Ignacio J. [Department of Electrical Engineering, University of Zaragoza, Zaragoza (Spain); Fernandez-Jimenez, L. Alfredo [Department of Electrical Engineering, University of La Rioja, Logrono (Spain); Monteiro, Claudio; Sousa, Joao; Bessa, Ricardo [FEUP, Fac. Engenharia Univ. Porto (Portugal)]|[INESC - Instituto de Engenharia de Sistemas e Computadores do Porto, Porto (Portugal)

    2009-07-15

    This paper presents a comparison of two new advanced statistical short-term wind-power forecasting systems developed by two independent research teams. The input variables used in both systems were the same: forecasted meteorological variable values obtained from a numerical weather prediction model; and electric power-generation registers from the SCADA system of the wind farm. Both systems are described in detail and the forecasting results compared, revealing great similarities, although the proposed structures of the two systems are different. The forecast horizon for both systems is 72 h, allowing the use of the forecasted values in electric market operations, as diary and intra-diary power generation bid offers, and in wind-farm maintenance planning. (author)

  9. The Value of Renewable Energy as a Hedge Against Fuel Price Risk: Analytic Contributions from Economic and Finance Theory

    Energy Technology Data Exchange (ETDEWEB)

    Bolinger, Mark A; Wiser, Ryan

    2008-09-15

    natural gas in the United States over a relatively brief period. Perhaps of most concern is that this dramatic price increase was largely unforeseen. Figure 2 compares the EIA's natural gas wellhead price forecast from each year's Annual Energy Outlook (AEO) going back to 1985 against the average US wellhead price that actually transpired. As shown, our forecasting abilities have proven rather dismal over time, as over-forecasts made in the late 1980's eventually yielded to under-forecasts that have persisted to this day. This historical experience demonstrates that little weight should be placed on any one forecast of future natural gas prices, and that a broad range of future price conditions ought to be considered in planning and investment decisions. Against this backdrop of high, volatile, and unpredictable natural gas prices, increasing the market penetration of renewable generation such as wind, solar, and geothermal power may provide economic benefits to ratepayers by displacing gas-fired generation. These benefits may manifest themselves in several ways. First, the displacement of natural gas-fired generation by increased renewable generation reduces ratepayer exposure to natural gas price risk--i.e., the risk that future gas prices (and by extension future electricity prices) may end up markedly different than expected. Second, this displacement reduces demand for natural gas among gas-fired generators, which, all else equal, will put downward pressure on natural gas prices. Lower natural gas prices in turn benefit both electric ratepayers and other end-users of natural gas. Using analytic approaches that build upon, yet differ from, the past work of others, including Awerbuch (1993, 1994, 2003), Kahn and Stoft (1993), and Humphreys and McClain (1998), this chapter explores each of these two potential 'hedging' benefits of renewable electricity. Though we do not seek to judge whether these two specific benefits outweigh any incremental

  10. Modeling spot markets for electricity and pricing electricity derivatives

    Science.gov (United States)

    Ning, Yumei

    Spot prices for electricity have been very volatile with dramatic price spikes occurring in restructured market. The task of forecasting electricity prices and managing price risk presents a new challenge for market players. The objectives of this dissertation are: (1) to develop a stochastic model of price behavior and predict price spikes; (2) to examine the effect of weather forecasts on forecasted prices; (3) to price electricity options and value generation capacity. The volatile behavior of prices can be represented by a stochastic regime-switching model. In the model, the means of the high-price and low-price regimes and the probabilities of switching from one regime to the other are specified as functions of daily peak load. The probability of switching to the high-price regime is positively related to load, but is still not high enough at the highest loads to predict price spikes accurately. An application of this model shows how the structure of the Pennsylvania-New Jersey-Maryland market changed when market-based offers were allowed, resulting in higher price spikes. An ARIMA model including temperature, seasonal, and weekly effects is estimated to forecast daily peak load. Forecasts of load under different assumptions about weather patterns are used to predict changes of price behavior given the regime-switching model of prices. Results show that the range of temperature forecasts from a normal summer to an extremely warm summer cause relatively small increases in temperature (+1.5%) and load (+3.0%). In contrast, the increases in prices are large (+20%). The conclusion is that the seasonal outlook forecasts provided by NOAA are potentially valuable for predicting prices in electricity markets. The traditional option models, based on Geometric Brownian Motion are not appropriate for electricity prices. An option model using the regime-switching framework is developed to value a European call option. The model includes volatility risk and allows changes

  11. A New Approach to Forecasting Exchange Rates

    OpenAIRE

    Kenneth W Clements; Yihui Lan

    2006-01-01

    Building on purchasing power parity theory, this paper proposes a new approach to forecasting exchange rates using the Big Mac data from The Economist magazine. Our approach is attractive in three aspects. Firstly, it uses easily-available Big Mac prices as input. These prices avoid several serious problems associated with broad price indexes, such as the CPI, that are used in conventional PPP studies. Secondly, this approach provides real-time exchange-rate forecasts at any forecast horizon....

  12. Stock price prediction using geometric Brownian motion

    Science.gov (United States)

    Farida Agustini, W.; Restu Affianti, Ika; Putri, Endah RM

    2018-03-01

    Geometric Brownian motion is a mathematical model for predicting the future price of stock. The phase that done before stock price prediction is determine stock expected price formulation and determine the confidence level of 95%. On stock price prediction using geometric Brownian Motion model, the algorithm starts from calculating the value of return, followed by estimating value of volatility and drift, obtain the stock price forecast, calculating the forecast MAPE, calculating the stock expected price and calculating the confidence level of 95%. Based on the research, the output analysis shows that geometric Brownian motion model is the prediction technique with high rate of accuracy. It is proven with forecast MAPE value ≤ 20%.

  13. Optimal decisions and comparison of VMI and CPFR under price-sensitive uncertain demand

    Directory of Open Access Journals (Sweden)

    Yasaman Kazemi

    2013-06-01

    Full Text Available Purpose: The purpose of this study is to compare the performance of two advanced supply chain coordination mechanisms, Vendor Managed Inventory (VMI and Collaborative Planning Forecasting and Replenishment (CPFR, under a price-sensitive uncertain demand environment, and to make the optimal decisions on retail price and order quantity for both mechanisms. Design/ methodology/ approach: Analytical models are first applied to formulate a profit maximization problem; furthermore, by applying simulation optimization solution procedures, the optimal decisions and performance comparisons are accomplished. Findings: The results of the case study supported the widely held view that more advanced coordination mechanisms yield greater supply chain profit than less advanced ones. Information sharing does not only increase the supply chain profit, but also is required for the coordination mechanisms to achieve improved performance. Research limitations/implications: This study considers a single vendor and a single retailer in order to simplify the supply chain structure for modeling. Practical implications: Knowledge obtained from this study about the conditions appropriate for each specific coordination mechanism and the exact functions of coordination programs is critical to managerial decisions for industry practitioners who may apply the coordination mechanisms considered. Originality/value: This study includes the production cost in Economic Order Quantity (EOQ equations and combines it with price-sensitive demand under stochastic settings while comparing VMI and CPFR supply chain mechanisms and maximizing the total profit. Although many studies have worked on information sharing within the supply chain, determining the performance measures when the demand is price-sensitive and stochastic was not reported by researchers in the past literature.

  14. Comparison of historically simulated VaR: Evidence from oil prices

    International Nuclear Information System (INIS)

    Costello, Alexandra; Asem, Ebenezer; Gardner, Eldon

    2008-01-01

    Cabedo and Moya [Cabedo, J.D., Moya, I., 2003. Estimating oil price 'Value at Risk' using the historical simulation approach. Energy Economics 25, 239-253] find that ARMA with historical simulation delivers VaR forecasts that are superior to those from GARCH. We compare the ARMA with historical simulation to the semi-parametric GARCH model proposed by Barone-Adesi et al. [Barone-Adesi, G., Giannopoulos, K., Vosper, L., 1999. VaR without correlations for portfolios of derivative securities. Journal of Futures Markets 19 (5), 583-602]. The results suggest that the semi-parametric GARCH model generates VaR forecasts that are superior to the VaR forecasts from the ARMA with historical simulation. This is due to the fact that GARCH captures volatility clustering. Our findings suggest that Cabedo and Moya's conclusion is mainly driven by the normal distributional assumption imposed on the future risk structure in the GARCH model. (author)

  15. Selecting the Best Forecasting-Implied Volatility Model Using Genetic Programming

    Directory of Open Access Journals (Sweden)

    Wafa Abdelmalek

    2009-01-01

    Full Text Available The volatility is a crucial variable in option pricing and hedging strategies. The aim of this paper is to provide some initial evidence of the empirical relevance of genetic programming to volatility's forecasting. By using real data from S&P500 index options, the genetic programming's ability to forecast Black and Scholes-implied volatility is compared between time series samples and moneyness-time to maturity classes. Total and out-of-sample mean squared errors are used as forecasting's performance measures. Comparisons reveal that the time series model seems to be more accurate in forecasting-implied volatility than moneyness time to maturity models. Overall, results are strongly encouraging and suggest that the genetic programming approach works well in solving financial problems.

  16. Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence

    Directory of Open Access Journals (Sweden)

    Simon Pezzutto

    2018-06-01

    Full Text Available The scope of the present investigation is to provide a description of final electricity prices development in the context of deregulated electricity markets in EU28, up to 2020. We introduce a new methodology to predict long-term electricity market prices consisting of two parts: (1 a self-developed form of Porter’s five forces analysis (PFFA determining that electricity markets are characterized by a fairly steady price increase. Dominant driving factors come out to be: (i uncertainty of future electricity prices; (ii regulatory complexity; and (iii generation overcapacities. Similar conclusions derive from (2 a self-developed form of multiple-criteria decision analysis (MCDA. In this case, we find that the electricity market particularly depends on (i market liberalization and (ii the European Union (EU’s economy growth. The applied methodologies provide a novel contribution in forecasting electricity price trends, by analyzing the sentiments, expectations, and knowledge of industry experts, through an assessment of factors influencing the market price and goals of key market participants. An extensive survey was conducted, interviewing experts all over Europe showed that the electricity market is subject to a future slight price increase.

  17. Bayesian Forecasting of Options Prices: A Natural Framework for Pooling Historical and Implied Volatiltiy Information

    OpenAIRE

    Darsinos, T.; Satchell, S.E.

    2001-01-01

    Bayesian statistical methods are naturally oriented towards pooling in a rigorous way information from separate sources. It has been suggested that both historical and implied volatilities convey information about future volatility. However, typically in the literature implied and return volatility series are fed separately into models to provide rival forecasts of volatility or options prices. We develop a formal Bayesian framework where we can merge the backward looking information as r...

  18. Forecasting with Option-Implied Information

    DEFF Research Database (Denmark)

    Christoffersen, Peter; Jacobs, Kris; Chang, Bo Young

    2013-01-01

    This chapter surveys the methods available for extracting information from option prices that can be used in forecasting. We consider option-implied volatilities, skewness, kurtosis, and densities. More generally, we discuss how any forecasting object that is a twice differentiable function...... of the future realization of the underlying risky asset price can utilize option-implied information in a well-defined manner. Going beyond the univariate option-implied density, we also consider results on option-implied covariance, correlation and beta forecasting, as well as the use of option......-implied information in cross-sectional forecasting of equity returns. We discuss how option-implied information can be adjusted for risk premia to remove biases in forecasting regressions....

  19. Estimating Price Elasticity of Demand for Motor Fuel in the Transport Sectors

    Directory of Open Access Journals (Sweden)

    Olga Vasilyevna Mazurova

    2015-03-01

    Full Text Available Modeling of long-term forecasts of prices and demand on regional energy markets requires accounting for the future changes in the interactions between the greater economy and its energy sector, along with the possible emergence of new factors and specific regional features determining those interactions. The proposed approach allows the study of a correlation between demand and prices for motor fuel, taking into account the competition of energy carriers, the dynamics of energy prices, resource constraints, the use of new technologies and the uncertainty of input data. The main feature of the proposed approach is the combined estimation of the price elasticity of demand for motor fuel with optimization of fuel supply in the region. Thus the author determined elasticity of demand based on the comparison of economic efficiency of the use of different fuels. The study includes results of experimental calculations and forecasted price according to demand for motor fuel in freight transportation for the expected development conditions of the Far Eastern federal district

  20. Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting

    Directory of Open Access Journals (Sweden)

    Yi Xiao

    2013-02-01

    Full Text Available Stock e-exchange prices forecasting is an important financial problem that is receiving increasing attention. This study proposes a novel three-stage nonlinear ensemble model. In the proposed model, three different types of neural-network based models, i.e. Elman network, generalized regression neural network (GRNN and wavelet neural network (WNN are constructed by three non-overlapping training sets and are further optimized by improved particle swarm optimization (IPSO. Finally, a neural-network-based nonlinear meta-model is generated by learning three neural-network based models through support vector machines (SVM neural network. The superiority of the proposed approach lies in its flexibility to account for potentially complex nonlinear relationships. Three daily stock indices time series are used for validating the forecasting model. Empirical results suggest the ensemble ANNs-PSO-GA approach can significantly improve the prediction performance over other individual models and linear combination models listed in this study.

  1. Day-Ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model

    Directory of Open Access Journals (Sweden)

    Qing Zhu

    2014-01-01

    Full Text Available As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations.

  2. Comparison of vector autoregressive (VAR) and vector error correction models (VECM) for index of ASEAN stock price

    Science.gov (United States)

    Suharsono, Agus; Aziza, Auliya; Pramesti, Wara

    2017-12-01

    Capital markets can be an indicator of the development of a country's economy. The presence of capital markets also encourages investors to trade; therefore investors need information and knowledge of which shares are better. One way of making decisions for short-term investments is the need for modeling to forecast stock prices in the period to come. Issue of stock market-stock integration ASEAN is very important. The problem is that ASEAN does not have much time to implement one market in the economy, so it would be very interesting if there is evidence whether the capital market in the ASEAN region, especially the countries of Indonesia, Malaysia, Philippines, Singapore and Thailand deserve to be integrated or still segmented. Furthermore, it should also be known and proven What kind of integration is happening: what A capital market affects only the market Other capital, or a capital market only Influenced by other capital markets, or a Capital market as well as affecting as well Influenced by other capital markets in one ASEAN region. In this study, it will compare forecasting of Indonesian share price (IHSG) with neighboring countries (ASEAN) including developed and developing countries such as Malaysia (KLSE), Singapore (SGE), Thailand (SETI), Philippines (PSE) to find out which stock country the most superior and influential. These countries are the founders of ASEAN and share price index owners who have close relations with Indonesia in terms of trade, especially exports and imports. Stock price modeling in this research is using multivariate time series analysis that is VAR (Vector Autoregressive) and VECM (Vector Error Correction Modeling). VAR and VECM models not only predict more than one variable but also can see the interrelations between variables with each other. If the assumption of white noise is not met in the VAR modeling, then the cause can be assumed that there is an outlier. With this modeling will be able to know the pattern of relationship

  3. Comparison of historically simulated VaR: Evidence from oil prices

    Energy Technology Data Exchange (ETDEWEB)

    Costello, Alexandra [Seminole Canada Energy, Calgary, AB (Canada); Asem, Ebenezer; Gardner, Eldon [Faculty of Management, University of Lethbridge, Lethbridge, AB (Canada)

    2008-09-15

    Cabedo and Moya [Cabedo, J.D., Moya, I., 2003. Estimating oil price 'Value at Risk' using the historical simulation approach. Energy Economics 25, 239-253] find that ARMA with historical simulation delivers VaR forecasts that are superior to those from GARCH. We compare the ARMA with historical simulation to the semi-parametric GARCH model proposed by Barone-Adesi et al. [Barone-Adesi, G., Giannopoulos, K., Vosper, L., 1999. VaR without correlations for portfolios of derivative securities. Journal of Futures Markets 19 (5), 583-602]. The results suggest that the semi-parametric GARCH model generates VaR forecasts that are superior to the VaR forecasts from the ARMA with historical simulation. This is due to the fact that GARCH captures volatility clustering. Our findings suggest that Cabedo and Moya's conclusion is mainly driven by the normal distributional assumption imposed on the future risk structure in the GARCH model. (author)

  4. Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels

    Directory of Open Access Journals (Sweden)

    Taiyong Li

    2016-12-01

    Full Text Available Crude oil, as one of the most important energy sources in the world, plays a crucial role in global economic events. An accurate prediction for crude oil price is an interesting and challenging task for enterprises, governments, investors, and researchers. To cope with this issue, in this paper, we proposed a method integrating ensemble empirical mode decomposition (EEMD, adaptive particle swarm optimization (APSO, and relevance vector machine (RVM—namely, EEMD-APSO-RVM—to predict crude oil price based on the “decomposition and ensemble” framework. Specifically, the raw time series of crude oil price were firstly decomposed into several intrinsic mode functions (IMFs and one residue by EEMD. Then, RVM with combined kernels was applied to predict target value for the residue and each IMF individually. To improve the prediction performance of each component, an extended particle swarm optimization (PSO was utilized to simultaneously optimize the weights and parameters of single kernels for the combined kernel of RVM. Finally, simple addition was used to aggregate all the predicted results of components into an ensemble result as the final result. Extensive experiments were conducted on the crude oil spot price of the West Texas Intermediate (WTI to illustrate and evaluate the proposed method. The experimental results are superior to those by several state-of-the-art benchmark methods in terms of root mean squared error (RMSE, mean absolute percent error (MAPE, and directional statistic (Dstat, showing that the proposed EEMD-APSO-RVM is promising for forecasting crude oil price.

  5. The mirage of higher petroleum prices

    International Nuclear Information System (INIS)

    Lynch, M.C.

    1996-01-01

    Most petroleum industry price forecasters do not possess a record of which they can be proud. Long-term petroleum market forecasting has been so inaccurate that it has often been described as virtually impossible. To avoid criticism of their performance, many organizations no longer circulate their forecasts. Why have the forecasts been so wrong? Because of failure to predict supply. This paper reviews the erroneous methods used to predict price trends in the oil and gas industry and identifies methods to correct the problem

  6. Canary tomato export prices: comparison and relationships between daily seasonal patterns

    Directory of Open Access Journals (Sweden)

    G. Martin-Rodriguez

    2013-10-01

    Full Text Available Statistical procedures are proposed to describe, compare and forecast the behaviour of seasonal variations in two daily price series of Canary tomato exported to German and British markets, respectively, over the last decade. These seasonal patterns are pseudo-periodic as the length of the seasonal period changes frequently in dependence of market conditions. Seasonal effect at a day in the harvesting period is defined as a spline function of the proportion of the length of such a period elapsed up to such a day. Then, seasonal patterns for the two series are compared in terms of the area between the corresponding spline functions. The ability of these models to capture the dynamic process of change in the seasonal pattern is useful to forecasting purpose. Furthermore, an analytical tool is also proposed to obtain forecasts of the seasonal pattern in one of these two series from the forecasts of the seasonal pattern in the other one. These procedures are useful for farmers in developing strategies related to the seasonal distribution of tomato production exported to each market.

  7. On the directional accuracy of survey forecasts: the case of gold and silver

    DEFF Research Database (Denmark)

    Fritsche, U.; Pierdzioch, C.; Rulke, J. C.

    2013-01-01

    We use a nonparametric market-timing test to study the directional accuracy of survey forecasts of the prices of gold and silver. We find that forecasters have market-timing ability with respect to the direction of change of the price of silver at various forecast horizons. In contrast, forecasters...... have no market-timing ability with respect to the direction of change in the gold price. Combining forecasts of both metal prices to set up a multivariate market-timing test yields no evidence of joint predictability of the directions of change of the prices of gold and silver....

  8. On the market impact of wind energy forecasts

    International Nuclear Information System (INIS)

    Jonsson, Tryggvi; Pinson, Pierre; Madsen, Henrik

    2010-01-01

    This paper presents an analysis of how day-ahead electricity spot prices are affected by day-ahead wind power forecasts. Demonstration of this relationship is given as a test case for the Western Danish price area of the Nord Pool's Elspot market. Impact on the average price behaviour is investigated as well as that on the distributional properties of the price. By using a non-parametric regression model to assess the effects of wind power forecasts on the average behaviour, the non-linearities and time variations in the relationship are captured well and the effects are shown to be quite substantial. Furthermore, by evaluating the distributional properties of the spot prices under different scenarios, the impact of the wind power forecasts on the price distribution is proved to be considerable. The conditional price distribution is moreover shown to be non-Gaussian. This implies that forecasting models for electricity spot prices for which parameters are estimated by a least squares techniques will not have Gaussian residuals. Hence the widespread assumption of Gaussian residuals from electricity spot price models is shown to be inadequate for these model types. The revealed effects are likely to be observable and qualitatively similar in other day-ahead electricity markets significantly penetrated by wind power. (author)

  9. Study the Effect of Value-Added of Services Sector on Forecasting of Electricity Demand in Services Sector due to Price Reform

    Directory of Open Access Journals (Sweden)

    Sayed Mahdi Mostafavi

    2016-07-01

    Full Text Available Electrical energy is as one of the important effective factors on economic growth and development. In recent decades, numerous studies in different countries to estimate and forecast electricity demand in different parts of the economy have been made. In this paper, using the method ARDL, estimation and forecasting of electricity demand in the services sector of Iran are determined for the time period from 1983 to 2012. Estimated equations show that the added value of the services sector and a significant positive impact on the demand for electricity in this sector. The price elasticity for services sector is smaller than 1 due to low electricity prices and subsidized electricity. Hence, electricity prices have little impact on the demand for electricity. The results of the estimate represents a long-term relationship between the variables in the services sector. In this paper, based on amendments to the law on subsidies and estimated values, anticipated electricity demand until the end of the fifth development plan was carried out. The results indicate an increase in power consumption in the services sector.

  10. Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond

    DEFF Research Database (Denmark)

    Hong, Tao; Pinson, Pierre; Fan, Shu

    2016-01-01

    The energy industry has been going through a significant modernization process over the last decade. Its infrastructure is being upgraded rapidly. The supply, demand and prices are becoming more volatile and less predictable than ever before. Even its business model is being challenged fundamenta......The energy industry has been going through a significant modernization process over the last decade. Its infrastructure is being upgraded rapidly. The supply, demand and prices are becoming more volatile and less predictable than ever before. Even its business model is being challenged...... fundamentally. In this competitive and dynamic environment, many decision-making processes rely on probabilistic forecasts to quantify the uncertain future. Although most of the papers in the energy forecasting literature focus on point or singlevalued forecasts, the research interest in probabilistic energy...

  11. Near-term oil prices

    International Nuclear Information System (INIS)

    Lynch, M.C.

    2001-01-01

    This PowerPoint presentation included 36 slides that described the state of oil prices and how to predict them. Prices are random, stochastic, chaotic, mean-reverting and driven by speculators, oil companies and OPEC. The many factors that enable price forecasting are economic growth, weather, industry behaviour, speculators, OPEC policy choices, Mexico/Russia production policy, non-OPEC supply and the interpretation of the above factors by OPEC, speculators, traders and the petroleum industry. Several graphs were included depicting such things as WTI price forecasts, differentials, oil market change in 2001, inventory levels, and WTI backwardation. The presentation provided some explanations for price uncertainties, price surges and collapses. U.S. GDP growth and the volatility of Iraq's production was also depicted. The author predicted that economic growth will occur and that oil demand will go up. Oil prices will fluctuate as the Middle East will be politically unstable and weather will be a major factor that will influence oil prices. The prices are likely to be more volatile than in the 1986 to 1995 period. 2 tabs., 22 figs

  12. Detecting Chaos from Agricultural Product Price Time Series

    Directory of Open Access Journals (Sweden)

    Xin Su

    2014-12-01

    Full Text Available Analysis of the characteristics of agricultural product price volatility and trend forecasting are necessary to formulate and implement agricultural price control policies. Taking wholesale cabbage prices as an example, a multiple test methodology has been adopted to identify the nonlinearity, fractality, and chaos of the data. The approaches used include the R/S analysis, the BDS test, the power spectra, the recurrence plot, the largest Lyapunov exponent, the Kolmogorov entropy, and the correlation dimension. The results show that there is chaos in agricultural wholesale price data, which provides a good theoretical basis for selecting reasonable forecasting models as prediction techniques based on chaos theory can be applied to forecasting agricultural prices.

  13. Forecast Accuracy Uncertainty and Momentum

    OpenAIRE

    Bing Han; Dong Hong; Mitch Warachka

    2009-01-01

    We demonstrate that stock price momentum and earnings momentum can result from uncertainty surrounding the accuracy of cash flow forecasts. Our model has multiple information sources issuing cash flow forecasts for a stock. The investor combines these forecasts into an aggregate cash flow estimate that has minimal mean-squared forecast error. This aggregate estimate weights each cash flow forecast by the estimated accuracy of its issuer, which is obtained from their past forecast errors. Mome...

  14. The Impact of Price Comparison Service on Pricing Strategy in a Dual-Channel Supply Chain

    Directory of Open Access Journals (Sweden)

    Qi Xu

    2013-01-01

    Full Text Available Recently, price comparison service (PCS websites are more and more popular due to its features in facilitating transparent price and promoting rational purchase decision. Motivated by the industrial practices, in this study, we examine the pricing strategies of retailers and supplier in a dual-channel supply chain influenced by the signals of PCS. We categorize and discuss three situations according to the signal availability of PCS, under which the optimal pricing strategies are derived. Finally, we conduct a numerical study and find that in fact the retailers and supplier are all more willing to avoid the existence of PCS with the objective of profit maximization. When both of retailers are affected by the PCS, the supplier is more willing to reduce the availability of price information. Important managerial insights are discussed.

  15. Long range dependency and forecasting of housing price index and mortgage market rate: evidence of subprime crisis

    Directory of Open Access Journals (Sweden)

    Nadhem Selmi

    2015-05-01

    Full Text Available In this paper, we examine and forecast the House Price Index (HPI and mortgage market rate in terms of the description of the subprime crisis. We use a semi-parametric local polynomial Whittle estimator proposed by Shimotsu et al. (2005 [Shimotsu, K., & Phillips, P.C.B. (2005, Exact local Whittle estimation of fractional integration. The Annals of Statistics, 33(4, 1890-1933.] in a long memory parameter time series. Empirical investigation of HPI and mortgage market rate shows that these variables are more persistent when the d estimates are found on the Shimotsu method than on the one of Künsch (1987 [Künsch, H.R. (1987. Statistical aspects of self-similar processes. In Y. Prokhorov and V.V. Sazanov (eds., Proceedings of the First World Congress of the Bernoulli Society, VNU Science Press, Utrecht, 67-74.]. The estimating forecast values are more realistic and they strongly reflect the present US economy actuality in the two series as indicated by the forecast evaluation topics.

  16. Preferential Price and Trade Tied Aid in Fiji: Implications on Price ...

    African Journals Online (AJOL)

    Pacific island countries (PICs) have been receiving the highest percapita aid .... dimension of the preferential price by examining the role of forecast price as an ..... Abbot, D and S. Pollard (2004) Hardship and Poverty in the Pacific, Asian.

  17. Modeling and forecasting petroleum futures volatility

    International Nuclear Information System (INIS)

    Sadorsky, Perry

    2006-01-01

    Forecasts of oil price volatility are important inputs into macroeconometric models, financial market risk assessment calculations like value at risk, and option pricing formulas for futures contracts. This paper uses several different univariate and multivariate statistical models to estimate forecasts of daily volatility in petroleum futures price returns. The out-of-sample forecasts are evaluated using forecast accuracy tests and market timing tests. The TGARCH model fits well for heating oil and natural gas volatility and the GARCH model fits well for crude oil and unleaded gasoline volatility. Simple moving average models seem to fit well in some cases provided the correct order is chosen. Despite the increased complexity, models like state space, vector autoregression and bivariate GARCH do not perform as well as the single equation GARCH model. Most models out perform a random walk and there is evidence of market timing. Parametric and non-parametric value at risk measures are calculated and compared. Non-parametric models outperform the parametric models in terms of number of exceedences in backtests. These results are useful for anyone needing forecasts of petroleum futures volatility. (author)

  18. Electricity market price spike analysis by a hybrid data model and feature selection technique

    International Nuclear Information System (INIS)

    Amjady, Nima; Keynia, Farshid

    2010-01-01

    In a competitive electricity market, energy price forecasting is an important activity for both suppliers and consumers. For this reason, many techniques have been proposed to predict electricity market prices in the recent years. However, electricity price is a complex volatile signal owning many spikes. Most of electricity price forecast techniques focus on the normal price prediction, while price spike forecast is a different and more complex prediction process. Price spike forecasting has two main aspects: prediction of price spike occurrence and value. In this paper, a novel technique for price spike occurrence prediction is presented composed of a new hybrid data model, a novel feature selection technique and an efficient forecast engine. The hybrid data model includes both wavelet and time domain variables as well as calendar indicators, comprising a large candidate input set. The set is refined by the proposed feature selection technique evaluating both relevancy and redundancy of the candidate inputs. The forecast engine is a probabilistic neural network, which are fed by the selected candidate inputs of the feature selection technique and predict price spike occurrence. The efficiency of the whole proposed method for price spike occurrence forecasting is evaluated by means of real data from the Queensland and PJM electricity markets. (author)

  19. Mid-Term Electricity Market Clearing Price Forecasting with Sparse Data: A Case in Newly-Reformed Yunnan Electricity Market

    Directory of Open Access Journals (Sweden)

    Chuntian Cheng

    2016-10-01

    Full Text Available For the power systems, for which few data are available for mid-term electricity market clearing price (MCP forecasting at the early stage of market reform, a novel grey prediction model (defined as interval GM(0, N model is proposed in this paper. Over the traditional GM(0, N model, three major improvements of the proposed model are: (i the lower and upper bounds are firstly identified to give an interval estimation of the forecasting value; (ii a novel whitenization method is then established to determine the definite forecasting value from the forecasting interval; and (iii the model parameters are identified by an improved particle swarm optimization (PSO instead of the least square method (LSM for the limitation of LSM. Finally, a newly-reformed electricity market in Yunnan province of China is studied, and input variables are contrapuntally selected. The accuracy of the proposed model is validated by observed data. Compared with the multiple linear regression (MLR model, the traditional GM(0, N model and the artificial neural network (ANN model, the proposed model gives a better performance and its superiority is further ensured by the use of the modified Diebold–Mariano (MDM test, suggesting that it is suitable for mid-term electricity MCP forecasting in a data-sparse electricity market.

  20. Addressing forecast uncertainty impact on CSP annual performance

    Science.gov (United States)

    Ferretti, Fabio; Hogendijk, Christopher; Aga, Vipluv; Ehrsam, Andreas

    2017-06-01

    This work analyzes the impact of weather forecast uncertainty on the annual performance of a Concentrated Solar Power (CSP) plant. Forecast time series has been produced by a commercial forecast provider using the technique of hindcasting for the full year 2011 in hourly resolution for Ouarzazate, Morocco. Impact of forecast uncertainty has been measured on three case studies, representing typical tariff schemes observed in recent CSP projects plus a spot market price scenario. The analysis has been carried out using an annual performance model and a standard dispatch optimization algorithm based on dynamic programming. The dispatch optimizer has been demonstrated to be a key requisite to maximize the annual revenues depending on the price scenario, harvesting the maximum potential out of the CSP plant. Forecasting uncertainty affects the revenue enhancement outcome of a dispatch optimizer depending on the error level and the price function. Results show that forecasting accuracy of direct solar irradiance (DNI) is important to make best use of an optimized dispatch but also that a higher number of calculation updates can partially compensate this uncertainty. Improvement in revenues can be significant depending on the price profile and the optimal operation strategy. Pathways to achieve better performance are presented by having more updates both by repeatedly generating new optimized trajectories but also more often updating weather forecasts. This study shows the importance of working on DNI weather forecasting for revenue enhancement as well as selecting weather services that can provide multiple updates a day and probabilistic forecast information.

  1. Energy price forecast by market analysis

    International Nuclear Information System (INIS)

    Jongepier, A.G.

    2000-01-01

    A power trader benefits from accurate price predictions. Based on market analyses, KEMA Connect has developed - in cooperation with Essent Energy Trading - a market model, enhancing the insight into market operation and one's own actions and thus resulting in accurate price predictions

  2. Estimating the common trend rate of inflation for consumer prices and consumer prices excluding food and energy prices

    OpenAIRE

    Michael T. Kiley

    2008-01-01

    I examine the common trend in inflation for consumer prices and consumer prices excluding prices of food and energy. Both the personal consumption expenditure (PCE) indexes and the consumer price indexes (CPI) are examined. The statistical model employed is a bivariate integrated moving average process; this model extends a univariate model that fits the data on inflation very well. The bivariate model forecasts as well as the univariate models. The results suggest that the relationship betwe...

  3. Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case

    Directory of Open Access Journals (Sweden)

    Antonio Bello

    2016-03-01

    Full Text Available One of the most relevant challenges that have arisen in electricity markets during the last few years is the emergence of extremely low prices. Trying to predict these events is crucial for market agents in a competitive environment. This paper proposes a novel methodology to simultaneously accomplish punctual and probabilistic hourly predictions about the appearance of extremely low electricity prices in a medium-term scope. The proposed approach for making real ex ante forecasts consists of a nested compounding of different forecasting techniques, which incorporate Monte Carlo simulation, combined with spatial interpolation techniques. The procedure is based on the statistical identification of the process key drivers. Logistic regression for rare events, decision trees, multilayer perceptrons and a hybrid approach, which combines a market equilibrium model with logistic regression, are used. Moreover, this paper assesses whether periodic models in which parameters switch according to the day of the week can be even more accurate. The proposed techniques are compared to a Markov regime switching model and several naive methods. The proposed methodology empirically demonstrates its effectiveness by achieving promising results on a real case study based on the Spanish electricity market. This approach can provide valuable information for market agents when they face decision making and risk-management processes. Our findings support the additional benefit of using a hybrid approach for deriving more accurate predictions.

  4. Estimating the commodity market price of risk for energy prices

    International Nuclear Information System (INIS)

    Kolos, Sergey P.; Ronn, Ehud I.

    2008-01-01

    The purpose of this paper is to estimate the ''market price of risk'' (MPR) for energy commodities, the ratio of expected return to standard deviation. The MPR sign determines whether energy forward prices are upward- or downward-biased predictors of expected spot prices. We estimate MPRs using spot and futures prices, while accounting for the Samuelson effect. We find long-term MPRs generally positive and short-term negative, consistent with positive energy betas and hedging, respectively. In spot electricity markets, MPRs in Day-Ahead Prices agree with short-dated futures. Our results relate risk premia to informed hedging decisions, and futures prices to forecast/expected prices. (author)

  5. The economic benefit of short-term forecasting for wind energy in the UK electricity market

    International Nuclear Information System (INIS)

    Barthelmie, R.J.; Murray, F.; Pryor, S.C.

    2008-01-01

    In the UK market, the total price of renewable electricity is made up of the Renewables Obligation Certificate and the price achieved for the electricity. Accurate forecasting improves the price if electricity is traded via the power exchange. In order to understand the size of wind farm for which short-term forecasting becomes economically viable, we develop a model for wind energy. Simulations were carried out for 2003 electricity prices for different forecast accuracies and strategies. The results indicate that it is possible to increase the price obtained by around pound 5/MWh which is about 14% of the electricity price in 2003 and about 6% of the total price. We show that the economic benefit of using short-term forecasting is also dependant on the accuracy and cost of purchasing the forecast. As the amount of wind energy requiring integration into the grid increases, short-term forecasting becomes more important to both wind farm owners and the transmission/distribution operators. (author)

  6. An analysis of the oil prices: stationary and forecasting models; Uma analise dos precos do petroleo no mercado internacional: estacionaridade e modelos de previsao

    Energy Technology Data Exchange (ETDEWEB)

    Salles, Andre A. de; Veiga, Iago E. B. da Costa; Machado, Rafael G.T. [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil)

    2008-07-01

    The movements of the oil prices in the international market are important for any planning, so the study of this variable is relevant for the investment and financing decisions of the production. The purpose of this work is to study the time series of the quotations of the spot prices of the crude oil in the international market. The objectives of this work are to study the movements of time series of the prices, and the returns, of the crude oil prices gives emphasis in the stationary. The other focus of this work is to develop forecasting models for the oil prices, or the returns of the oil prices. The selected sample was of the daily quotation of the prices of types WTI and Brent, for the period from January 2005 to April 2007. (author)

  7. A trend discontinuity: The mystery of natural gas prices

    International Nuclear Information System (INIS)

    Steffes, D.W.

    1995-01-01

    For the last fifteen years, the natural gas price forecasting experts have had a terrible record of forecasting future natural gas prices. (In the early 80's, the gas price was forecasted to be over $10/MMBtu in the late 80's). To make matters even worse, they can't seem to understand why the price is what it is, even in hindsight. If these experts can't even get it right in hindsight, how can one ever expect to get it right in foresight? It is concluded that the traditional laws of supply and demand don't work very well in this new quasi-regulated natural gas industry. Evidently, Social Influences and Political Influences are more important than the Economic Influence on natural gas prices

  8. Petroleum price; Prix du petrole

    Energy Technology Data Exchange (ETDEWEB)

    Maurice, J

    2001-07-01

    The oil market is the most volatile of all markets, with the exception of the Nasdaq. It is also the biggest commodity market in the world. Therefore one cannot avoid forecasting oil prices, nor can one expect to avoid the forecasting errors that have been made in the past. In his report, Joel Maurice draws a distinction between the short term and the medium-long term in analysing the outlook for oil prices. (author)

  9. International comparison of energy price using a purchasing power parity

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Dong Hun; Jo, Sung Han [Korea Energy Economics Institute, Euiwang (Korea)

    1999-05-01

    The price control of government results in price distortion as well as lowering efficiency of energy market and distortion of allocating resources. Consequently, such a price policy leads to energy overconsumption and has negative influences on other policies trying to reduce environmental burden resulted from energy consumption. When the energy price does not reflect the market price properly, it results in inefficiency of energy industry and it makes very difficult to supply investment funds. Therefore, the government is planning to implement liberalization of energy price step by step. The purpose of this study is to provide basic materials for establishing a reasonable energy price policy through the international comparison among OECD countries on major products price focusing on petroleum products. To overcome problems of exchange rate, a purchasing power indicator from OECD was used for comparative analysis with OECD countries. 11 refs. 1 fig., 23 tabs.

  10. Natural gas pricing and contracting practices in North America

    International Nuclear Information System (INIS)

    Hassan, F.

    1992-01-01

    Over the past 5 years the natural gas industry in North America has undergone substantial change as a result of the deregulated market. A comparison is provided of the key contract parameters in gas purchase contracts utilized by local distribution companies, industrial customers, cogenerators and marketers. Issues discussed include pricing mechanisms, indexed contracts, negotiated contracts, combinations, dispute resolution, supply, government regulation, industry structures, financial considerations, perception, geological influences, demand, transmission, storage, distribution, price trends and forecasts, Order 636 in the U.S., the evolution of North American market hubs, the futures market, and 'daisy chains' of connecting pipelines. 15 refs., 7 figs., 1 tab

  11. Olympic games in Sochi, comparison and forecast

    OpenAIRE

    Kudrina, Anastasia

    2015-01-01

    Title: Olympic Games in Sochi, comparison and prediction Goals: Based on a research of the Olympic Games in Sochi to create a forecast of future economic effects on the territorium Method: The prediction is based on the analogic situation in other countries that held the Olympic Games in the past. Induction method of representing the thought process, during which the individual facts formulate more general assertions and theories; deduction method, which are derived from the general conclusio...

  12. Using time series structural characteristics to analyze grain prices in food insecure countries

    Science.gov (United States)

    Davenport, Frank; Funk, Chris

    2015-01-01

    Two components of food security monitoring are accurate forecasts of local grain prices and the ability to identify unusual price behavior. We evaluated a method that can both facilitate forecasts of cross-country grain price data and identify dissimilarities in price behavior across multiple markets. This method, characteristic based clustering (CBC), identifies similarities in multiple time series based on structural characteristics in the data. Here, we conducted a simulation experiment to determine if CBC can be used to improve the accuracy of maize price forecasts. We then compared forecast accuracies among clustered and non-clustered price series over a rolling time horizon. We found that the accuracy of forecasts on clusters of time series were equal to or worse than forecasts based on individual time series. However, in the following experiment we found that CBC was still useful for price analysis. We used the clusters to explore the similarity of price behavior among Kenyan maize markets. We found that price behavior in the isolated markets of Mandera and Marsabit has become increasingly dissimilar from markets in other Kenyan cities, and that these dissimilarities could not be explained solely by geographic distance. The structural isolation of Mandera and Marsabit that we find in this paper is supported by field studies on food security and market integration in Kenya. Our results suggest that a market with a unique price series (as measured by structural characteristics that differ from neighboring markets) may lack market integration and food security.

  13. Energy markets and price relations

    International Nuclear Information System (INIS)

    Bergendahl, P.A.

    1986-10-01

    The aim of the report is to elucidate the way and extent of the dependence of the price of different energy species of one another and particularly of crude oil prices. Oil, coal and natural gas can substitute each other at many applications. The prices are dependent on mining, processing and transporting. Forecasting of prices and future trends are discussed

  14. A long-term view of worldwide fossil fuel prices

    International Nuclear Information System (INIS)

    Shafiee, Shahriar; Topal, Erkan

    2010-01-01

    This paper reviews a long-term trend of worldwide fossil fuel prices in the future by introducing a new method to forecast oil, natural gas and coal prices. The first section of this study analyses the global fossil fuel market and the historical trend of real and nominal fossil fuel prices from 1950 to 2008. Historical fossil fuel price analysis shows that coal prices are decreasing, while natural gas prices are increasing. The second section reviews previously available price modelling techniques and proposes a new comprehensive version of the long-term trend reverting jump and dip diffusion model. The third section uses the new model to forecast fossil fuel prices in nominal and real terms from 2009 to 2018. The new model follows the extrapolation of the historical sinusoidal trend of nominal and real fossil fuel prices. The historical trends show an increase in nominal/real oil and natural gas prices plus nominal coal prices, as well as a decrease in real coal prices. Furthermore, the new model forecasts that oil, natural gas and coal will stay in jump for the next couple of years and after that they will revert back to the long-term trend until 2018. (author)

  15. Comparison between stochastic and machine learning methods for hydrological multi-step ahead forecasting: All forecasts are wrong!

    Science.gov (United States)

    Papacharalampous, Georgia; Tyralis, Hristos; Koutsoyiannis, Demetris

    2017-04-01

    Machine learning (ML) is considered to be a promising approach to hydrological processes forecasting. We conduct a comparison between several stochastic and ML point estimation methods by performing large-scale computational experiments based on simulations. The purpose is to provide generalized results, while the respective comparisons in the literature are usually based on case studies. The stochastic methods used include simple methods, models from the frequently used families of Autoregressive Moving Average (ARMA), Autoregressive Fractionally Integrated Moving Average (ARFIMA) and Exponential Smoothing models. The ML methods used are Random Forests (RF), Support Vector Machines (SVM) and Neural Networks (NN). The comparison refers to the multi-step ahead forecasting properties of the methods. A total of 20 methods are used, among which 9 are the ML methods. 12 simulation experiments are performed, while each of them uses 2 000 simulated time series of 310 observations. The time series are simulated using stochastic processes from the families of ARMA and ARFIMA models. Each time series is split into a fitting (first 300 observations) and a testing set (last 10 observations). The comparative assessment of the methods is based on 18 metrics, that quantify the methods' performance according to several criteria related to the accurate forecasting of the testing set, the capturing of its variation and the correlation between the testing and forecasted values. The most important outcome of this study is that there is not a uniformly better or worse method. However, there are methods that are regularly better or worse than others with respect to specific metrics. It appears that, although a general ranking of the methods is not possible, their classification based on their similar or contrasting performance in the various metrics is possible to some extent. Another important conclusion is that more sophisticated methods do not necessarily provide better forecasts

  16. Of Needles and Haystacks: Novel Techniques for Data-Rich Economic Forecasting Data-Rich Economic Forecasting

    NARCIS (Netherlands)

    P. Exterkate (Peter)

    2011-01-01

    textabstractThis thesis discusses various novel techniques for economic forecasting. The focus is on methods that exploit the information in large data sets effectively. Each of these methods is compared to established techniques for forecasting yields on U.S. Treasury Bills, housing prices,

  17. Novel methodology for pharmaceutical expenditure forecast.

    Science.gov (United States)

    Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Rémuzat, Cécile; Urbinati, Duccio; Kornfeld, Åsa; Mzoughi, Olfa; Toumi, Mondher

    2014-01-01

    The value appreciation of new drugs across countries today features a disruption that is making the historical data that are used for forecasting pharmaceutical expenditure poorly reliable. Forecasting methods rarely addressed uncertainty. The objective of this project was to propose a methodology to perform pharmaceutical expenditure forecasting that integrates expected policy changes and uncertainty (developed for the European Commission as the 'EU Pharmaceutical expenditure forecast'; see http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). 1) Identification of all pharmaceuticals going off-patent and new branded medicinal products over a 5-year forecasting period in seven European Union (EU) Member States. 2) Development of a model to estimate direct and indirect impacts (based on health policies and clinical experts) on savings of generics and biosimilars. Inputs were originator sales value, patent expiry date, time to launch after marketing authorization, price discount, penetration rate, time to peak sales, and impact on brand price. 3) Development of a model for new drugs, which estimated sales progression in a competitive environment. Clinical expected benefits as well as commercial potential were assessed for each product by clinical experts. Inputs were development phase, marketing authorization dates, orphan condition, market size, and competitors. 4) Separate analysis of the budget impact of products going off-patent and new drugs according to several perspectives, distribution chains, and outcomes. 5) Addressing uncertainty surrounding estimations via deterministic and probabilistic sensitivity analysis. This methodology has proven to be effective by 1) identifying the main parameters impacting the variations in pharmaceutical expenditure forecasting across countries: generics discounts and penetration, brand price after patent loss, reimbursement rate, the penetration of biosimilars and discount price, distribution chains, and the time

  18. A new approach for crude oil price prediction based on stream learning

    Directory of Open Access Journals (Sweden)

    Shuang Gao

    2017-01-01

    Full Text Available Crude oil is the world's leading fuel, and its prices have a big impact on the global environment, economy as well as oil exploration and exploitation activities. Oil price forecasts are very useful to industries, governments and individuals. Although many methods have been developed for predicting oil prices, it remains one of the most challenging forecasting problems due to the high volatility of oil prices. In this paper, we propose a novel approach for crude oil price prediction based on a new machine learning paradigm called stream learning. The main advantage of our stream learning approach is that the prediction model can capture the changing pattern of oil prices since the model is continuously updated whenever new oil price data are available, with very small constant overhead. To evaluate the forecasting ability of our stream learning model, we compare it with three other popular oil price prediction models. The experiment results show that our stream learning model achieves the highest accuracy in terms of both mean squared prediction error and directional accuracy ratio over a variety of forecast time horizons.

  19. Forecasting volatility of crude oil markets

    International Nuclear Information System (INIS)

    Kang, Sang Hoon; Kang, Sang-Mok; Yoon, Seong-Min

    2009-01-01

    This article investigates the efficacy of a volatility model for three crude oil markets - Brent, Dubai, and West Texas Intermediate (WTI) - with regard to its ability to forecast and identify volatility stylized facts, in particular volatility persistence or long memory. In this context, we assess persistence in the volatility of the three crude oil prices using conditional volatility models. The CGARCH and FIGARCH models are better equipped to capture persistence than are the GARCH and IGARCH models. The CGARCH and FIGARCH models also provide superior performance in out-of-sample volatility forecasts. We conclude that the CGARCH and FIGARCH models are useful for modeling and forecasting persistence in the volatility of crude oil prices. (author)

  20. Forecasting volatility of crude oil markets

    Energy Technology Data Exchange (ETDEWEB)

    Kang, Sang Hoon [Department of Business Administration, Gyeongsang National University, Jinju, 660-701 (Korea); Kang, Sang-Mok; Yoon, Seong-Min [Department of Economics, Pusan National University, Busan, 609-735 (Korea)

    2009-01-15

    This article investigates the efficacy of a volatility model for three crude oil markets - Brent, Dubai, and West Texas Intermediate (WTI) - with regard to its ability to forecast and identify volatility stylized facts, in particular volatility persistence or long memory. In this context, we assess persistence in the volatility of the three crude oil prices using conditional volatility models. The CGARCH and FIGARCH models are better equipped to capture persistence than are the GARCH and IGARCH models. The CGARCH and FIGARCH models also provide superior performance in out-of-sample volatility forecasts. We conclude that the CGARCH and FIGARCH models are useful for modeling and forecasting persistence in the volatility of crude oil prices. (author)

  1. THE CORN-EGG PRICE TRANSMISSION MECHANISM

    OpenAIRE

    Babula, Ronald A.; Bessler, David A.

    1990-01-01

    A vector autoregression (VAR) model of corn, farm egg, and retail egg prices is estimated and shocked with a corn price increase. Impulse responses in egg prices, t-statistics for the impulse responses, and decompositions of forecast error variance are presented. Analyses of results provide insights on the corn/egg price transmission mechanism and on how corn price shocks pulsate through the egg-related economy.

  2. Electric power systems advanced forecasting techniques and optimal generation scheduling

    CERN Document Server

    Catalão, João P S

    2012-01-01

    Overview of Electric Power Generation SystemsCláudio MonteiroUncertainty and Risk in Generation SchedulingRabih A. JabrShort-Term Load ForecastingAlexandre P. Alves da Silva and Vitor H. FerreiraShort-Term Electricity Price ForecastingNima AmjadyShort-Term Wind Power ForecastingGregor Giebel and Michael DenhardPrice-Based Scheduling for GencosGovinda B. Shrestha and Songbo QiaoOptimal Self-Schedule of a Hydro Producer under UncertaintyF. Javier Díaz and Javie

  3. Long-term forecast 2010; Laangsiktsprognos 2010

    Energy Technology Data Exchange (ETDEWEB)

    2011-07-01

    This report presents the energy forecast to the year 2030, and two different sensitivity scenarios. The forecast is based on existing instruments, which means that the report's findings should not be considered a proper forecast of the future energy use, but as an impact assessment of existing policy instruments, given different circumstances such as economic growth and fuel prices

  4. Annual Energy Outlook 2009 with Projections to 2030

    Energy Technology Data Exchange (ETDEWEB)

    None

    2009-03-01

    The Annual Energy Outlook 2009 (AEO2009), prepared by the Energy Information Administration (EIA), presents long-term projections of energy supply, demand, and prices through 2030, based on results from EIA’s National Energy Modeling System (NEMS). EIA published an “early release” version of the AEO2009 reference case in December 2008.

  5. A comparison of the VAR model and the PC factor model in forecasting inflation in Montenegro

    Directory of Open Access Journals (Sweden)

    Lipovina-Božović Milena

    2013-01-01

    Full Text Available Montenegro started using the euro in 2002 and regained independence in 2006. Its main economic partners are European countries, yet inflation movements in Montenegro do not coincide with consumer price fluctuations in the eurozone. Trying to develop a useful forecasting model for Montenegrin inflation, we compare the results of a three-variable vector autoregression (VAR model, and a principle component (PC factor model starting with twelve variables. The estimation period is January 2001 to December 2012, and the control months are the first six months of 2013. The results show that in forecasting inflation, despite a high level of Montenegrin economic dependence on international developments, more reliable forecasts are achieved with the use of additional information on a larger number of factors, which includes domestic economic activity.

  6. An Extrapolative Model of House Price Dynamics

    OpenAIRE

    Edward L. Glaeser; Charles G. Nathanson

    2015-01-01

    A modest approximation by homebuyers leads house prices to display three features that are present in the data but usually missing from perfectly rational models: momentum at one-year horizons, mean reversion at five-year horizons, and excess longer-term volatility relative to fundamentals. Valuing a house involves forecasting the current and future demand to live in the surrounding area. Buyers forecast using past transaction prices. Approximating buyers do not adjust for the expectations of...

  7. Understanding international commodity price fluctuations

    NARCIS (Netherlands)

    Arezki, Rabah; Loungani, Prakash; van der Ploeg, Rick; Venables, Anthony J.

    An overview is provided of recent work on commodity prices, focusing on three themes: (i) "financialization" of commodity markets--commodities being considered by financial investors as a distinct asset class, (ii) trends and forecasts of commodity prices, and (iii) fracking-a shorthand for the

  8. INDIA’S ELECTRICITY DEMAND FORECAST USING REGRESSION ANALYSIS AND ARTIFICIAL NEURAL NETWORKS BASED ON PRINCIPAL COMPONENTS

    Directory of Open Access Journals (Sweden)

    S. Saravanan

    2012-07-01

    Full Text Available Power System planning starts with Electric load (demand forecasting. Accurate electricity load forecasting is one of the most important challenges in managing supply and demand of the electricity, since the electricity demand is volatile in nature; it cannot be stored and has to be consumed instantly. The aim of this study deals with electricity consumption in India, to forecast future projection of demand for a period of 19 years from 2012 to 2030. The eleven input variables used are Amount of CO2 emission, Population, Per capita GDP, Per capita gross national income, Gross Domestic savings, Industry, Consumer price index, Wholesale price index, Imports, Exports and Per capita power consumption. A new methodology based on Artificial Neural Networks (ANNs using principal components is also used. Data of 29 years used for training and data of 10 years used for testing the ANNs. Comparison made with multiple linear regression (based on original data and the principal components and ANNs with original data as input variables. The results show that the use of ANNs with principal components (PC is more effective.

  9. Forecasting Analysis of Shanghai Stock Index Based on ARIMA Model

    Directory of Open Access Journals (Sweden)

    Li Chenggang

    2017-01-01

    Full Text Available Prediction and analysis of the Shanghai Composite Index is conducive for investors to investing in the stock market, and providing investors with reference. This paper selects Shanghai Composite Index monthly closing price from Jan, 2005 to Oct, 2016 to construct ARIMA model. This paper carries on the forecast of the last three monthly closing price of Shanghai Stock Index that have occurred, and compared it with the actual value, which tests the accuracy and feasibility of the model in the short term Shanghai Stock Index forecast. At last, this paper uses the ARIMA model to forecast the Shanghai Composite Index closing price of the last two months in 2016.

  10. Daily House Price Indices: Construction, Modeling, and Longer-Run Predictions

    DEFF Research Database (Denmark)

    Bollerslev, Tim; Patton, Andrew J.; Wang, Wenjing

    We construct daily house price indices for ten major U.S. metropolitan areas. Our calculations are based on a comprehensive database of several million residential property transactions and a standard repeat-sales method that closely mimics the methodology of the popular monthly Case-Shiller house...... price indices. Our new daily house price indices exhibit dynamic features similar to those of other daily asset prices, with mild autocorrelation and strong conditional heteroskedasticity of the corresponding daily returns. A relatively simple multivariate time series model for the daily house price...... index returns, explicitly allowing for commonalities across cities and GARCH effects, produces forecasts of monthly house price changes that are superior to various alternative forecast procedures based on lower frequency data....

  11. Annual energy outlook 1993 with projections to 2010

    International Nuclear Information System (INIS)

    1992-01-01

    The Energy Information Administration's (EIA's) Annual Energy Outlook 1993 (AEO93) presents forecasts for energy prices, supply, demand, and imports over the period 1990 to 2010. These projections take into account existing legislation, including the Energy Policy Act of 1992. Even though the world oil market remains relatively tight, the long-term outlook for oil prices has been revised downward since the Annual Energy Outlook 1992 as expectations for both the Organization of Petroleum Exporting Countries (OPEC) and non-OPEC production potential have been revised upward. Domestic natural gas prices are also expected to be lower than projected last year, in part because of a more optimistic outlook for drilling technology. Finally, lower growth in the demand for electricity is expected because of the Energy Policy Act of 1992, which mandates efficiency standards for new energy-using equipment. These are the most striking differences between last year's EIA evaluation of long-term energy market trends and this year's evaluation

  12. The rationality of EIA forecasts under symmetric and asymmetric loss

    International Nuclear Information System (INIS)

    Auffhammer, Maximilian

    2007-01-01

    The United States Energy Information Administration publishes annual forecasts of nationally aggregated energy consumption, production, prices, intensity and GDP. These government issued forecasts often serve as reference cases in the calibration of simulation and econometric models, which climate and energy policy are based on. This study tests for rationality of published EIA forecasts under symmetric and asymmetric loss. We find strong empirical evidence of asymmetric loss for oil, coal and electricity prices as well as natural gas consumption, electricity sales, GDP and energy intensity. (author)

  13. The rationality of EIA forecasts under symmetric and asymmetric loss

    Energy Technology Data Exchange (ETDEWEB)

    Auffhammer, Maximilian [Department of Agricultural and Resource Economics, University of California, 207 Giannini Hall 3310, Berkeley, CA 94720 (United States)

    2007-05-15

    The United States Energy Information Administration publishes annual forecasts of nationally aggregated energy consumption, production, prices, intensity and GDP. These government issued forecasts often serve as reference cases in the calibration of simulation and econometric models, which climate and energy policy are based on. This study tests for rationality of published EIA forecasts under symmetric and asymmetric loss. We find strong empirical evidence of asymmetric loss for oil, coal and electricity prices as well as natural gas consumption, electricity sales, GDP and energy intensity. (author)

  14. An empirical model of daily highs and lows of West Texas Intermediate crude oil prices

    International Nuclear Information System (INIS)

    He, Angela W.W.; Wan, Alan T.K.; Kwok, Jerry T.K.

    2010-01-01

    There is a large collection of literature on energy price forecasting, but most studies typically use monthly average or close-to-close daily price data. In practice, the daily price range constructed from the daily high and low also contains useful information on price volatility and is used frequently in technical analysis. The interaction between the daily high and low and the associated daily range has been examined in several recent studies on stock price and exchange rate forecasts. The present paper adopts a similar approach to analyze the behaviour of the West Texas Intermediate (WTI) crude oil price over a ten-year period. We find that daily highs and lows of the WTI oil price are cointegrated, with the error correction term being closely approximated by the daily price range. Two forecasting models, one based on a vector error correction mechanism and the other based on a transfer function framework with the range taken as a driver variable, are presented for forecasting the daily highs and lows. The results show that both of these models offer significant advantages over the naive random walk and univariate ARIMA models in terms of out-of-sample forecast accuracy. A trading strategy that makes use of the daily high and low forecasts is further developed. It is found that this strategy generally yields very reasonable trading returns over an evaluation period of about two years. (author)

  15. Optimal operation and forecasting policy for pump storage plants in day-ahead markets

    International Nuclear Information System (INIS)

    Muche, Thomas

    2014-01-01

    Highlights: • We investigate unit commitment deploying stochastic and deterministic approaches. • We consider day-ahead markets, its forecast and weekly price based unit commitment. • Stochastic and deterministic unit commitment are identical for the first planning day. • Unit commitment and bidding policy can be based on the deterministic approach. • Robust forecasting models should be estimated based on the whole planning horizon. - Abstract: Pump storage plants are an important electricity storage technology at present. Investments in this technology are expected to increase. The necessary investment valuation often includes expected cash flows from future price-based unit commitment policies. A price-based unit commitment policy has to consider market price uncertainty and the information revealing nature of electricity markets. For this environment stochastic programming models are suggested to derive the optimal unit commitment policy. For the considered day-ahead price electricity market stochastic and deterministic unit commitment policies are comparable suggesting an application of easier implementable deterministic models. In order to identify suitable unit commitment and forecasting policies, deterministic unit commitment models are applied to actual day-ahead electricity prices of a whole year. As a result, a robust forecasting model should consider the unit commitment planning period. This robust forecasting models result in expected cash flows similar to realized ones allowing a reliable investment valuation

  16. Toward crustacean without chemicals: a descriptive analysis of consumer response using price comparisons.

    Science.gov (United States)

    Okpala, Charles Odilichukwu R; Bono, Gioacchino; Pipitone, Vito; Vitale, Sergio; Cannizzaro, Leonardo

    2016-01-01

    To date, there seems to be limited-to-zero emphasis about how consumers perceive crustacean products subject to either chemical and or non-chemical preservative treatments. In addition, studies that investigated price comparisons of crustacean products subject to either chemical or chemical-free preservative methods seem unreported. This study focused on providing some foundational knowledge about how consumers perceive traditionally harvested crustaceans that are either chemical-treated and or free of chemicals, incorporating price comparisons using a descriptive approach. The study design employed a questionnaire approach via interview using a computer-assisted telephone system and sampled 1,540 participants across five key locations in Italy. To actualize consumer sensitivity, 'price' was the focus given its crucial role as a consumption barrier. Prior to this, variables such as demographic characteristics of participants, frequency of purchasing, quality attributes/factors that limit the consumption of crustaceans were equally considered. By price comparisons, consumers are likely to favor chemical-free (modified atmosphere packaging) crustacean products amid a price increase of up to 15%. But, a further price increase such as by 25% could markedly damage consumers' feelings, which might lead to a considerable number opting out in favor of either chemical-treated or other seafood products. Comparing locations, the studied variables showed no statistical differences ( p >0.05). On the contrary, the response weightings fluctuated across the studied categories. Both response weightings and coefficient of variation helped reveal more about how responses deviated per variable categories. This study has revealed some foundational knowledge about how consumers perceive traditionally harvested crustaceans that were either chemical-treated or subject to chemical-free preservative up to price sensitivity using Italy as a reference case, which is applicable to other parts

  17. World coal prices and future energy demand

    International Nuclear Information System (INIS)

    Bennett, J.

    1992-01-01

    The Clean Air Act Amendments will create some important changes in the US domestic steam coal market, including price increases for compliance coal by the year 2000 and price decreases for high-sulfur coal. In the international market, there is likely to be a continuing oversupply which will put a damper on price increases. The paper examines several forecasts for domestic and international coal prices and notes a range of predictions for future oil prices

  18. An Efficient and Simplified Model for Forecasting using SRM

    Directory of Open Access Journals (Sweden)

    Hafiz Muhammad Shahzad Asif

    2014-01-01

    Full Text Available Learning form continuous financial systems play a vital role in enterprise operations. One of the most sophisticated non-parametric supervised learning classifiers, SVM (Support Vector Machines, provides robust and accurate results, however it may require intense computation and other resources. The heart of SLT (Statistical Learning Theory, SRM (Structural Risk Minimization Principle can also be used for model selection. In this paper, we focus on comparing the performance of model estimation using SRM with SVR (Support Vector Regression for forecasting the retail sales of consumer products. The potential benefits of an accurate sales forecasting technique in businesses are immense. Retail sales forecasting is an integral part of strategic business planning in areas such as sales planning, marketing research, pricing, production planning and scheduling. Performance comparison of support vector regression with model selection using SRM shows comparable results to SVR but in a computationally efficient manner. This research targeted the real life data to conclude the results after investigating the computer generated datasets for different types of model building

  19. An efficient and simplified model for forecasting using SRM

    International Nuclear Information System (INIS)

    Asif, H.M.; Hyat, M.F.; Ahmad, T.

    2014-01-01

    Learning form continuous financial systems play a vital role in enterprise operations. One of the most sophisticated non-parametric supervised learning classifiers, SVM (Support Vector Machines), provides robust and accurate results, however it may require intense computation and other resources. The heart of SLT (Statistical Learning Theory), SRM (Structural Risk Minimization )Principle can also be used for model selection. In this paper, we focus on comparing the performance of model estimation using SRM with SVR (Support Vector Regression) for forecasting the retail sales of consumer products. The potential benefits of an accurate sales forecasting technique in businesses are immense. Retail sales forecasting is an integral part of strategic business planning in areas such as sales planning, marketing research, pricing, production planning and scheduling. Performance comparison of support vector regression with model selection using SRM shows comparable results to SVR but in a computationally efficient manner. This research targeted the real life data to conclude the results after investigating the computer generated datasets for different types of model building. (author)

  20. Controlling Electricity Consumption by Forecasting its Response to Varying Prices

    DEFF Research Database (Denmark)

    Corradi, Olivier; Ochsenfeld, Henning Peter; Madsen, Henrik

    2013-01-01

    electricity consumption using a one-way price signal. Estimation of the price-response is based on data measurable at grid level, removing the need to install sensors and communication devices between each individual consumer and the price-generating entity. An application for price-responsive heating systems......In a real-time electricity pricing context where consumers are sensitive to varying prices, having the ability to anticipate their response to a price change is valuable. This paper proposes models for the dynamics of such price-response, and shows how these dynamics can be used to control...... is studied based on real data, before conducting a control by price experiment using a mixture of real and synthetic data. With the control objective of following a constant consumption reference, peak heating consumption is reduced by nearly 5%, and 11% of the mean daily heating consumption is shifted....

  1. A comparison of various forecasting techniques applied to mean hourly wind speed time series

    Energy Technology Data Exchange (ETDEWEB)

    Sfetsos, A. [7 Pirsou Street, Athens (Greece)

    2000-09-01

    This paper presents a comparison of various forecasting approaches, using time series analysis, on mean hourly wind speed data. In addition to the traditional linear (ARMA) models and the commonly used feed forward and recurrent neural networks, other approaches are also examined including the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Neural Logic Networks. The developed models are evaluated for their ability to produce accurate and fast forecasts. (Author)

  2. Long-term projections for electricity and gas prices

    International Nuclear Information System (INIS)

    Borggrefe, Frieder; Lochner, Stefan

    2009-01-01

    The article analyses potential developments of wholesale electricity prices in Germany until 2030. The relevant determinants and their effects on prices are shown. Several projections demonstrate the impact of future fuel prices taking the political framework into account. The importance of carbon and gas prices - and the latter's relationship to oil prices - are discussed extensively. Although forecasting electricity prices is associated with great uncertainties, the article illustrates the relative impacts of the various price determinants and their interactions. (orig.)

  3. Evaluating the US government's crude oil price projections

    International Nuclear Information System (INIS)

    Williams, M.D.

    1992-01-01

    The U.S. Department of Energy's (DOE) 1991 official long run crude oil price projections are evaluated by comparing parameter averages for the forecast period (1991-2010) to parameter averages from crude oil price history (1859-1990). The parameters used in the evaluation are average price, average annual price changes, and average cycle duration (in years). All prices used in the analysis are annual prices in constant 1990 dollars per barrel. 13 figs

  4. The determinants of oil prices

    International Nuclear Information System (INIS)

    Angelier, J-P.

    1991-01-01

    In recent years, swings in oil prices have been of unprecendented severity and frequency. Three factors work together to determine the price of oil: in the short term, the balance between supply and demand; in the medium term, the structure of the oil industry; and in the long term, the marginal production cost consistent with world oil demand. An oil price forecast is presented based on these considerations, and it is predicted that in the year 2000, oil prices will not be significantly different from those of today. 28 refs

  5. A comparison of generic drug prices in seven European countries: a methodological analysis.

    Science.gov (United States)

    Wouters, Olivier J; Kanavos, Panos G

    2017-03-31

    Policymakers and researchers frequently compare the prices of medicines between countries. Such comparisons often serve as barometers of how pricing and reimbursement policies are performing. The aim of this study was to examine methodological challenges to comparing generic drug prices. We calculated all commonly used price indices based on 2013 IMS Health data on sales of 3156 generic drugs in seven European countries. There were large differences in generic drug prices between countries. However, the results varied depending on the choice of index, base country, unit of volume, method of currency conversion, and therapeutic category. The results also differed depending on whether one looked at the prices charged by manufacturers or those charged by pharmacists. Price indices are a useful statistical approach for comparing drug prices across countries, but researchers and policymakers should interpret price indices with caution given their limitations. Price-index results are highly sensitive to the choice of method and sample. More research is needed to determine the drivers of price differences between countries. The data suggest that some governments should aim to reduce distribution costs for generic drugs.

  6. Forecast errors in IEA-countries' energy consumption

    DEFF Research Database (Denmark)

    Linderoth, Hans

    2002-01-01

    Every year Policy of IEA Countries includes a forecast of the energy consumption in the member countries. Forecasts concerning the years 1985,1990 and 1995 can now be compared to actual values. The second oil crisis resulted in big positive forecast errors. The oil price drop in 1986 did not have...... the small value is often the sum of large positive and negative errors. Almost no significant correlation is found between forecast errors in the 3 years. Correspondingly, no significant correlation coefficient is found between forecasts errors in the 3 main energy sectors. Therefore, a relatively small...

  7. The macroeconomic effects of oil price fluctuations on a small open oil-producing country. The case of Trinidad and Tobago

    International Nuclear Information System (INIS)

    Lorde, Troy; Thomas, Chrystol; Jackman, Mahalia

    2009-01-01

    Using vector autoregressive (VAR) methodology, this paper empirically investigates the macroeconomic effects of oil price fluctuations on Trinidad and Tobago. Overall, we find that the price of oil is a major determinant of economic activity of the country. Our impulse response functions suggest that following a positive oil price shock, output falls within the first two years followed by positive and growing response. We also investigate the macroeconomic impact of oil price volatility. Results suggest that an unanticipated shock to oil price volatility brings about random swings in the macroeconomy; however, only government revenue and the price level exhibit significant responses. With regard to the magnitude of the responses, shocks to oil price volatility tend to yield smaller macroeconomic impacts in comparison to shocks to oil prices. Variance decompositions suggest that the price of oil is a major component of forecast variation for most macroeconomic variables. Finally, Granger-causality tests indicate causality from oil prices to output and oil prices to government revenue. (author)

  8. Another look on the relationships between oil prices and energy prices

    International Nuclear Information System (INIS)

    Lahiani, Amine; Miloudi, Anthony; Benkraiem, Ramzi; Shahbaz, Muhammad

    2017-01-01

    This paper employs the Quantile Autoregressive Distributed Lags (QARDL) model developed recently by Cho et al. (2015) to investigate the pass-through of oil prices to a set of energy prices. This approach allows analyzing simultaneously short-term connections and long-run cointegrating relationships across a range of quantiles. It also provides insights on the short-run predictive power of oil prices in predicting energy prices while accounting for the cointegration between oil prices and each of the considered energy prices in low, medium and high quantiles. Two key findings emerge from this paper. First, all considered energy prices are shown to be cointegrated with oil price across quantiles meaning that a stationaryequilibriumrelationship exists between single energy price and oil price. Second, we find evidence that oil price is a significant predictor of individual petroleum products prices and natural gas in the short run. This paper has important policy implications for forecasters, energy policy-makers and portfolio managers. - Highlights: • The pass-through of oil prices to a set of energy prices is investigated for US economy. • All considered energy prices are shown to be cointegrated with oil price across quantiles. • Oil price is a significant predictor of individual petroleum products prices in the short run. • Oil price also predicts natural gas prices in the short run.

  9. Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling

    International Nuclear Information System (INIS)

    Che Jinxing; Wang Jianzhou

    2010-01-01

    In this paper, we present the use of different mathematical models to forecast electricity price under deregulated power. A successful prediction tool of electricity price can help both power producers and consumers plan their bidding strategies. Inspired by that the support vector regression (SVR) model, with the ε-insensitive loss function, admits of the residual within the boundary values of ε-tube, we propose a hybrid model that combines both SVR and Auto-regressive integrated moving average (ARIMA) models to take advantage of the unique strength of SVR and ARIMA models in nonlinear and linear modeling, which is called SVRARIMA. A nonlinear analysis of the time-series indicates the convenience of nonlinear modeling, the SVR is applied to capture the nonlinear patterns. ARIMA models have been successfully applied in solving the residuals regression estimation problems. The experimental results demonstrate that the model proposed outperforms the existing neural-network approaches, the traditional ARIMA models and other hybrid models based on the root mean square error and mean absolute percentage error.

  10. Toward crustacean without chemicals: a descriptive analysis of consumer response using price comparisons

    Directory of Open Access Journals (Sweden)

    Charles Odilichukwu R. Okpala

    2016-10-01

    Full Text Available Background: To date, there seems to be limited-to-zero emphasis about how consumers perceive crustacean products subject to either chemical and or non-chemical preservative treatments. In addition, studies that investigated price comparisons of crustacean products subject to either chemical or chemical-free preservative methods seem unreported. Objective: This study focused on providing some foundational knowledge about how consumers perceive traditionally harvested crustaceans that are either chemical-treated and or free of chemicals, incorporating price comparisons using a descriptive approach. Design: The study design employed a questionnaire approach via interview using a computer-assisted telephone system and sampled 1,540 participants across five key locations in Italy. To actualize consumer sensitivity, ‘price’ was the focus given its crucial role as a consumption barrier. Prior to this, variables such as demographic characteristics of participants, frequency of purchasing, quality attributes/factors that limit the consumption of crustaceans were equally considered. Results: By price comparisons, consumers are likely to favor chemical-free (modified atmosphere packaging crustacean products amid a price increase of up to 15%. But, a further price increase such as by 25% could markedly damage consumers’ feelings, which might lead to a considerable number opting out in favor of either chemical-treated or other seafood products. Comparing locations, the studied variables showed no statistical differences (p>0.05. On the contrary, the response weightings fluctuated across the studied categories. Both response weightings and coefficient of variation helped reveal more about how responses deviated per variable categories. Conclusions: This study has revealed some foundational knowledge about how consumers perceive traditionally harvested crustaceans that were either chemical-treated or subject to chemical-free preservative up to price

  11. Energy forecasts, perspectives and methods

    Energy Technology Data Exchange (ETDEWEB)

    Svensson, J E; Mogren, A

    1984-01-01

    The authors have analyzed different methods for long term energy prognoses, in particular energy consumption forecasts. Energy supply and price prognoses are also treated, but in a less detailed manner. After defining and discussing the various methods/models used in forecasts, a generalized discussion of the influence on the prognoses from the perspectives (background factors, world view, norms, ideology) of the prognosis makers is given. Some basic formal demands that should be asked from any rational forecast are formulated and discussed. The authors conclude that different forecasting methodologies are supplementing each other. There is no best method, forecasts should be accepted as views of the future from differing perspectives. The primary prognostic problem is to show the possible futures, selecting the wanted future is a question of political process.

  12. Expectations and bubbles in asset pricing experiments

    NARCIS (Netherlands)

    Hommes, C.; Sonnemans, J.; Tuinstra, J.; van de Velden, H.

    2008-01-01

    We present results on expectation formation in a controlled experimental environment. In each period subjects are asked to predict the next price of a risky asset. The realized market price is derived from an unknown market equilibrium equation with feedback from individual forecasts. In most

  13. The outlook for oil prices in 1992 - results of a survey

    International Nuclear Information System (INIS)

    Hawdon, D.

    1992-01-01

    The eighth in a series of oil price expectation studies took place on 18th March 1992 at the Prospects for Oil Prices conference held at the University of Surrey. Thirty-one participants returned a questionnaire designed to elicit 12 month ahead and 5 year ahead price expectations. Respondents were asked to indicate their view of the likely price of oil in certain broad price ranges. These were selected to cover the wide variation of prices experienced since the early 1970s. The results show the 12 month's ahead expectations all clustered in the range $10 to $25 per barrel and $16-$20 as the median predicted price. In comparison with the 1991 expectations, a much higher proportion of respondents (77.4 as compared to 50% in 1991) gave $16-20 as their expected price range, whilst fewer expected prices to rise (19% compared with 46% in 1991). The stability of the 12 month ahead price expectations is a remarkable feature of a period which has witnessed much tension in the Middle East and in the former Soviet Union. This stability extends to the 5 year ahead forecasts as well. Here the median expectation is for prices to rise to the $21-25 per barrel range in money of the day terms though there is evidence of a growing scepticism about the oil market's ability to sustain higher prices in the long run. (author)

  14. Analysis and forecasting of nonresidential electricity consumption in Romania

    Energy Technology Data Exchange (ETDEWEB)

    Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio [Dipartimento di Ingegneria Aerospaziale e Meccanica, Seconda Universita degli Studi di Napoli, Via Roma 29, 81031 Aversa (CE) (Italy); Minea, Alina A. [Faculty of Materials Science and Engineering, Technical University Gh. Asachi from Iasi, Bd. D. Mangeron, No. 59, Iasi (Romania)

    2010-11-15

    Electricity consumption forecast has fundamental importance in the energy planning of a country. In this paper, we present an analysis and two forecast models for nonresidential electricity consumption in Romania. A first part of the paper is dedicated to the estimation of GDP and price elasticities of consumption. Nonresidential short run GDP and price elasticities are found to be approximately 0.136 and -0.0752, respectively, whereas long run GDP and price elasticities are equal to 0.496 and -0.274 respectively. The second part of the study is dedicated to the forecasting of nonresidential electricity consumption up to year 2020. A Holt-Winters exponential smoothing method and a trigonometric grey model with rolling mechanism (TGMRM) are employed for the consumption prediction. The two models lead to similar results, with an average deviation less than 5%. This deviation is to be considered acceptable in relation to the time horizon considered in the present study. (author)

  15. Analysis and forecasting of nonresidential electricity consumption in Romania

    International Nuclear Information System (INIS)

    Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio; Minea, Alina A.

    2010-01-01

    Electricity consumption forecast has fundamental importance in the energy planning of a country. In this paper, we present an analysis and two forecast models for nonresidential electricity consumption in Romania. A first part of the paper is dedicated to the estimation of GDP and price elasticities of consumption. Nonresidential short run GDP and price elasticities are found to be approximately 0.136 and -0.0752, respectively, whereas long run GDP and price elasticities are equal to 0.496 and -0.274 respectively. The second part of the study is dedicated to the forecasting of nonresidential electricity consumption up to year 2020. A Holt-Winters exponential smoothing method and a trigonometric grey model with rolling mechanism (TGMRM) are employed for the consumption prediction. The two models lead to similar results, with an average deviation less than 5%. This deviation is to be considered acceptable in relation to the time horizon considered in the present study. (author)

  16. Generation Mix Study Focusing on Nuclear Power by Practical Peak Forecast

    International Nuclear Information System (INIS)

    Shin, Jung Ho; Roh, Myung Sub

    2013-01-01

    The excessive underestimation can lead to a range of problem; expansion of LNG plant requiring short construction period, the following increase of electricity price, low reserve margin and inefficient configuration of power source. With regard to nuclear power, the share of the stable and economic base load plant, nuclear power, can reduce under the optimum level. Amongst varied factors which contribute to the underestimate, immoderate target for demand side management (DSM) including double deduction of the constraint amount by DSM from peak demand forecast is one of the causes. The hypothesis in this study is that the better optimum generation mix including the adequate share of nuclear power can be obtained under the condition of the peak demand forecast without deduction of DSM target because this forecast is closer to the actual peak demand. In this study, the hypothesis is verified with comparison between peak demand forecast before (or after) DSM target application and the actual peak demand in the 3 rd through 5 th BPE from 2006 to 2010. Furthermore, this research compares and analyzes several generation mix in 2027 focusing on the nuclear power by a few conditions using the WASP-IV program on the basis of the 6 th BPE in 2013. According to the comparative analysis on the peak demand forecast and actual peak demand from 2006 to 2010, the peak demand forecasts without the deduction of the DSM target is closer to the actual peak demand than the peak demand forecasts considering the DSM target in the 3 th , 4 th , 5 th entirely. In addition, the generation mix until 2027 is examined by the WASP-IV. As a result of the program run, when considering the peak demand forecast without DSM reflection, since the base load plants including nuclear power take up adequate proportion, stable and economic supply of electricity can be achieved. On the contrary, in case of planning based on the peak demand forecast with DSM reflected and then compensating the shortage by

  17. An ill-posed problem for the Black-Scholes equation for a profitable forecast of prices of stock options on real market data

    Science.gov (United States)

    Klibanov, Michael V.; Kuzhuget, Andrey V.; Golubnichiy, Kirill V.

    2016-01-01

    A new empirical mathematical model for the Black-Scholes equation is proposed to forecast option prices. This model includes new interval for the price of the underlying stock, new initial and new boundary conditions. Conventional notions of maturity time and strike prices are not used. The Black-Scholes equation is solved as a parabolic equation with the reversed time, which is an ill-posed problem. Thus, a regularization method is used to solve it. To verify the validity of our model, real market data for 368 randomly selected liquid options are used. A new trading strategy is proposed. Our results indicates that our method is profitable on those options. Furthermore, it is shown that the performance of two simple extrapolation-based techniques is much worse. We conjecture that our method might lead to significant profits of those financial insitutions which trade large amounts of options. We caution, however, that further studies are necessary to verify this conjecture.

  18. Logical design of a decision support system to forecast technology, prices and costs for the national communications system

    Science.gov (United States)

    Williams, K. A.; Partridge, E. C., III

    1984-09-01

    Originally envisioned as a means to integrate the many systems found throughout the government, the general mission of the NCS continues to be to ensure the survivability of communications during and subsequent to any national emergency. In order to accomplish this mission the NCS is an arrangement of heterogeneous telecommunications systems which are provided by their sponsor Federal agencies. The physical components of Federal telecommunications systems and networks include telephone and digital data switching facilities and primary common user communications centers; Special purpose local delivery message switching and exchange facilities; Government owned or leased radio systems; Technical control facilities which are under exclusive control of a government agency. This thesis describes the logical design of a proposed decision support system for use by the National Communications System in forecasting technology, prices, and costs. It is general in nature and only includes those forecasting models which are suitable for computer implementation. Because it is a logical design it can be coded and applied in many different hardware and/or software configurations.

  19. Projections of the energy prices

    International Nuclear Information System (INIS)

    Jankauskas, V.

    1996-01-01

    This article deals with the trends of the main fuel prices development in the Western European markets. There are two possible price development scenarios presented in the article. Transportation costs of various internationally traded fuels from various sources (Russia, Western Europe) are estimated and their most feasible values are considered. Fuel prices for the final big consumers are calculated adding the domestic distribution costs. Trends of heat and electricity price development in Lithuania during the period of 1991-1995 are analyzed. Forecasts of the electricity generation and supply costs are calculated according to various scenarios. Electricity prices will be lowest in the case of the further operation of the Ignalina NPP and low fuel prices in international markets. (author). 8 refs., 14 figs., 4 tabs

  20. Predicting prices of agricultural commodities in Thailand using combined approach emphasizing on data pre-processing technique

    Directory of Open Access Journals (Sweden)

    Thoranin Sujjaviriyasup

    2018-02-01

    Full Text Available In this research, a combined approach emphasizing on data pre-processing technique is developed to forecast prices of agricultural commodities in Thailand. The future prices play significant role in decision making to cultivate crops in next year. The proposed model takes ability of MODWT to decompose original time series data into more stable and explicit subseries, and SVR model to formulate complex function of forecasting. The experimental results indicated that the proposed model outperforms traditional forecasting models based on MAE and MAPE criteria. Furthermore, the proposed model reveals that it is able to be a useful forecasting tool for prices of agricultural commodities in Thailand

  1. Deregulated power prices: comparison of volatility

    International Nuclear Information System (INIS)

    Li Ying; Flynn, P.C.

    2004-01-01

    We examine electrical power price variability for 14 deregulated markets. Power price volatility is measured by price velocity, the daily average of the absolute value of price change per hour. Deregulated markets show a wide variability in price velocity. Some price velocity is expected and arises from the daily diurnal price pattern, which differs significantly between markets. Even when the expected daily variability in price is removed, the residual unexpected variability differs between markets. Some deregulated markets, most notably Britain and Spain, show patterns that are predictable and consistent and have low values of unexpected price velocity. These markets create a climate conducive to consumers facing the market through real time pricing and shaping consumption behaviors in response to price changes. Other markets, for example, South Australia and Alberta, have patterns that are inconsistent and irregular, and hence are hard for a customer to interpret; a customer in such a market will have a higher incentive to avoid demand side management and escape risk through hedging mechanisms

  2. Annual Energy Outlook 2016 With Projections to 2040

    Energy Technology Data Exchange (ETDEWEB)

    None, None

    2016-08-01

    The Annual Energy Outlook 2016 (AEO2016), prepared by the U.S. Energy Information Administration (EIA), presents long-term projections of energy supply, demand, and prices through 2040. The projections, focused on U.S. energy markets, are based on results from EIA’s National Energy Modeling System (NEMS). NEMS enables EIA to make projections under alternative, internallyconsistent sets of assumptions. The analysis in AEO2016 focuses on the Reference case and 17 alternative cases. EIA published an Early Release version of the AEO2016 Reference case (including U.S. Environmental Protection Agency’s (EPA) Clean Power Plan (CPP)) and a No CPP case (excluding the CPP) in May 2016.

  3. Uranium price trends for use in strategy analyses

    International Nuclear Information System (INIS)

    James, R.A.

    1979-09-01

    Long-term price forecasts for mined uranium are quoted. These will be used in Ontario Hydro's nuclear fuel cycle strategy analyses. They are, of necessity, speculative. The accuracy of the forecasts is considered adequate for long-term strategy analyses, but not for other purposes. (auth)

  4. Evaluation of long-term natural gas marketing agreements: An application of commodity forward and option pricing theory

    International Nuclear Information System (INIS)

    Salahor, G.S.; Laughton, D.G.

    1993-01-01

    Methods that have been empirically validated in the analysis of short-term traded securities are adapted to evaluate long-term natural gas direct-sale contracts. A sample contract is examined from the perspective of the producer, and analyzed as a series of forward and option contracts. The assessment of contract value is based on the gas price forecast, the volatility in that forecast, and the valuation of risk caused by that volatility. The method presented allows the gas producer to quantify these elements, and to evaluate the variety of terms encountered in direct-sale natural gas agreements, including features such as load factors and penalty charges. The analysis uses as inputs a probabilistic price forecast and a determination of a price of risk for gas prices. Once the forecast volatility is derived from the probabilistic forecast, the forward contracts imbedded in the long-term gas contract can be valued with a risk-discounting model, and optional aspects can be evaluated using the Black-Scholes option pricing method. 10 refs., 3 figs., 2 tabs

  5. Modeling and Forecasting Average Temperature for Weather Derivative Pricing

    Directory of Open Access Journals (Sweden)

    Zhiliang Wang

    2015-01-01

    Full Text Available The main purpose of this paper is to present a feasible model for the daily average temperature on the area of Zhengzhou and apply it to weather derivatives pricing. We start by exploring the background of weather derivatives market and then use the 62 years of daily historical data to apply the mean-reverting Ornstein-Uhlenbeck process to describe the evolution of the temperature. Finally, Monte Carlo simulations are used to price heating degree day (HDD call option for this city, and the slow convergence of the price of the HDD call can be found through taking 100,000 simulations. The methods of the research will provide a frame work for modeling temperature and pricing weather derivatives in other similar places in China.

  6. The impact of wind forecast errors on the efficiency of the Ontario electricity market

    International Nuclear Information System (INIS)

    Ng, H.

    2008-01-01

    Ontario's Independent System Operator (IESO) is currently involved in a number of wind projects in the province, and has developed both a resource commitment and dispatch timeline in relation to increased wind power penetration in the Ontario electricity grid. This presentation discussed the impacts of wind forecast errors on the province's electricity market. Day-ahead planning is used to commit fossil fuels and gas resources, while 3-hours ahead planning is used to commit generation in real time. Inter-ties are committed 1 hour ahead of dispatch. Over-forecasts for wind can cause market prices to increase in real-time, or cause markets to miss opportunities to schedule cheaper imports. The inefficient scheduling caused by overforecasts can also lead to exports not being purchases at high enough prices. Under-forecasts can cause market prices to decrease, and may cause imports to be scheduled that would not have been economic at lower prices. The scheduling difficulties related to under-forecasting can cause markets to miss opportunities to schedule efficient exports. Wind facility forecast errors typically improve closer to real-time. One-hour ahead wind forecast errors can reach approximately 12 per cent. The annual costs of overforecasting are under $200,000. Underforecasting costs are usually less than $30,000. The costs of the wind forecasting inefficiencies are relatively small in the $10 billion electricity market. It was concluded that system operators will continue to track forecast errors and inefficiencies as wind power capacity in the electric power industry increases. tabs., figs

  7. Benefits of seasonal forecasts of crop yields

    Science.gov (United States)

    Sakurai, G.; Okada, M.; Nishimori, M.; Yokozawa, M.

    2017-12-01

    Major factors behind recent fluctuations in food prices include increased biofuel production and oil price fluctuations. In addition, several extreme climate events that reduced worldwide food production coincided with upward spikes in food prices. The stabilization of crop yields is one of the most important tasks to stabilize food prices and thereby enhance food security. Recent development of technologies related to crop modeling and seasonal weather forecasting has made it possible to forecast future crop yields for maize and soybean. However, the effective use of these technologies remains limited. Here we present the potential benefits of seasonal crop-yield forecasts on a global scale for choice of planting day. For this purpose, we used a model (PRYSBI-2) that can well replicate past crop yields both for maize and soybean. This model system uses a Bayesian statistical approach to estimate the parameters of a basic process-based model of crop growth. The spatial variability of model parameters was considered by estimating the posterior distribution of the parameters from historical yield data by using the Markov-chain Monte Carlo (MCMC) method with a resolution of 1.125° × 1.125°. The posterior distributions of model parameters were estimated for each spatial grid with 30 000 MCMC steps of 10 chains each. By using this model and the estimated parameter distributions, we were able to estimate not only crop yield but also levels of associated uncertainty. We found that the global average crop yield increased about 30% as the result of the optimal selection of planting day and that the seasonal forecast of crop yield had a large benefit in and near the eastern part of Brazil and India for maize and the northern area of China for soybean. In these countries, the effects of El Niño and Indian Ocean dipole are large. The results highlight the importance of developing a system to forecast global crop yields.

  8. Forecasting the density of oil futures returns using model-free implied volatility and high-frequency data

    International Nuclear Information System (INIS)

    Ielpo, Florian; Sevi, Benoit

    2013-09-01

    Forecasting the density of returns is useful for many purposes in finance, such as risk management activities, portfolio choice or derivative security pricing. Existing methods to forecast the density of returns either use prices of the asset of interest or option prices on this same asset. The latter method needs to convert the risk-neutral estimate of the density into a physical measure, which is computationally cumbersome. In this paper, we take the view of a practitioner who observes the implied volatility under the form of an index, namely the recent OVX, to forecast the density of oil futures returns for horizons going from 1 to 60 days. Using the recent methodology in Maheu and McCurdy (2011) to compute density predictions, we compare the performance of time series models using implied volatility and either daily or intra-daily futures prices. Our results indicate that models based on implied volatility deliver significantly better density forecasts at all horizons, which is in line with numerous studies delivering the same evidence for volatility point forecast. (authors)

  9. Study on Comparison of Bidding and Pricing Behavior Distinction between Estimate Methods

    Science.gov (United States)

    Morimoto, Emi; Namerikawa, Susumu

    The most characteristic trend on bidding and pricing behavior distinction in recent years is the increasing number of bidders just above the criteria for low-price bidding investigations. The contractor's markup is the difference between the bidding price and the execution price. Therefore, the contractor's markup is the difference between criteria for low-price bidding investigations price and the execution price in the public works bid in Japan. Virtually, bidder's strategies and behavior have been controlled by public engineer's budgets. Estimation and bid are inseparably linked in the Japanese public works procurement system. The trial of the unit price-type estimation method begins in 2004. On another front, accumulated estimation method is one of the general methods in public works. So, there are two types of standard estimation methods in Japan. In this study, we did a statistical analysis on the bid information of civil engineering works for the Ministry of Land, Infrastructure, and Transportation in 2008. It presents several issues that bidding and pricing behavior is related to an estimation method (several estimation methods) for public works bid in Japan. The two types of standard estimation methods produce different results that number of bidders (decide on bid-no bid strategy) and distribution of bid price (decide on mark-up strategy).The comparison on the distribution of bid prices showed that the percentage of the bid concentrated on the criteria for low-price bidding investigations have had a tendency to get higher in the large-sized public works by the unit price-type estimation method, comparing with the accumulated estimation method. On one hand, the number of bidders who bids for public works estimated unit-price tends to increase significantly Public works estimated unit-price is likely to have been one of the factors for the construction companies to decide if they participate in the biddings.

  10. Neural network versus classical time series forecasting models

    Science.gov (United States)

    Nor, Maria Elena; Safuan, Hamizah Mohd; Shab, Noorzehan Fazahiyah Md; Asrul, Mohd; Abdullah, Affendi; Mohamad, Nurul Asmaa Izzati; Lee, Muhammad Hisyam

    2017-05-01

    Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.

  11. Combining forecast weights: Why and how?

    Science.gov (United States)

    Yin, Yip Chee; Kok-Haur, Ng; Hock-Eam, Lim

    2012-09-01

    This paper proposes a procedure called forecast weight averaging which is a specific combination of forecast weights obtained from different methods of constructing forecast weights for the purpose of improving the accuracy of pseudo out of sample forecasting. It is found that under certain specified conditions, forecast weight averaging can lower the mean squared forecast error obtained from model averaging. In addition, we show that in a linear and homoskedastic environment, this superior predictive ability of forecast weight averaging holds true irrespective whether the coefficients are tested by t statistic or z statistic provided the significant level is within the 10% range. By theoretical proofs and simulation study, we have shown that model averaging like, variance model averaging, simple model averaging and standard error model averaging, each produces mean squared forecast error larger than that of forecast weight averaging. Finally, this result also holds true marginally when applied to business and economic empirical data sets, Gross Domestic Product (GDP growth rate), Consumer Price Index (CPI) and Average Lending Rate (ALR) of Malaysia.

  12. Electricity consumption forecasting in Italy using linear regression models

    Energy Technology Data Exchange (ETDEWEB)

    Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio [DIAM, Seconda Universita degli Studi di Napoli, Via Roma 29, 81031 Aversa (CE) (Italy)

    2009-09-15

    The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model. The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population. A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to -0.06, while long run elasticities are equal to -0.24 and -0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values. In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models. A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of {+-}1% for the best case and {+-}11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account. (author)

  13. Electricity consumption forecasting in Italy using linear regression models

    International Nuclear Information System (INIS)

    Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio

    2009-01-01

    The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model. The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population. A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to -0.06, while long run elasticities are equal to -0.24 and -0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values. In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models. A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of ±1% for the best case and ±11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account. (author)

  14. Valuing hydrological forecasts for a pumped storage assisted hydro facility

    Science.gov (United States)

    Zhao, Guangzhi; Davison, Matt

    2009-07-01

    SummaryThis paper estimates the value of a perfectly accurate short-term hydrological forecast to the operator of a hydro electricity generating facility which can sell its power at time varying but predictable prices. The expected value of a less accurate forecast will be smaller. We assume a simple random model for water inflows and that the costs of operating the facility, including water charges, will be the same whether or not its operator has inflow forecasts. Thus, the improvement in value from better hydrological prediction results from the increased ability of the forecast using facility to sell its power at high prices. The value of the forecast is therefore the difference between the sales of a facility operated over some time horizon with a perfect forecast, and the sales of a similar facility operated over the same time horizon with similar water inflows which, though governed by the same random model, cannot be forecast. This paper shows that the value of the forecast is an increasing function of the inflow process variance and quantifies how much the value of this perfect forecast increases with the variance of the water inflow process. Because the lifetime of hydroelectric facilities is long, the small increase observed here can lead to an increase in the profitability of hydropower investments.

  15. A Comparative Study Of Stock Price Forecasting Using Nonlinear Models

    Directory of Open Access Journals (Sweden)

    Diteboho Xaba

    2017-03-01

    Full Text Available This study compared the in-sample forecasting accuracy of three forecasting nonlinear models namely: the Smooth Transition Regression (STR model, the Threshold Autoregressive (TAR model and the Markov-switching Autoregressive (MS-AR model. Nonlinearity tests were used to confirm the validity of the assumptions of the study. The study used model selection criteria, SBC to select the optimal lag order and for the selection of appropriate models. The Mean Square Error (MSE, Mean Absolute Error (MAE and Root Mean Square Error (RMSE served as the error measures in evaluating the forecasting ability of the models. The MS-AR models proved to perform well with lower error measures as compared to LSTR and TAR models in most cases.

  16. End user prices in liberalised energy markets

    Energy Technology Data Exchange (ETDEWEB)

    Lijesen, M.G. [Afdeling Energie en Grondstoffen, Centraal Planbureau CPB, Den Haag (Netherlands)

    2002-12-01

    As European energy markets move towards deregulation, energy prices shift from classic 'cost plus' prices towards market prices. We develop a model for the retail and wholesale energy markets in Europe, based on Bertrand competition in a two part pricing structure with switching costs. We use the model to forecast end user electricity and natural gas prices and find that the introduction of competition in energy retail and wholesale markets will decrease standing charges, lowering total costs for energy users. A larger number of entrants, a cost advantage for one of the suppliers, or lower switching costs reduces standing charges further.

  17. State-level electricity demand forecasting model. [For 1980, 1985, 1990

    Energy Technology Data Exchange (ETDEWEB)

    Nguyen, H. D.

    1978-01-01

    This note briefly describes the Oak Ridge National Laboratory (ORNL) state-level electricity demand (SLED) forecasting model developed for the Nuclear Regulatory Commission. Specifically, the note presents (1) the special features of the model, (2) the methodology used to forecast electricity demand, and (3) forecasts of electricity demand and average price by sector for 15 states for 1980, 1985, 1990.

  18. Photovoltaics (PV System Energy Forecast on the Basis of the Local Weather Forecast: Problems, Uncertainties and Solutions

    Directory of Open Access Journals (Sweden)

    Kristijan Brecl

    2018-05-01

    Full Text Available When integrating a photovoltaic system into a smart zero-energy or energy-plus building, or just to lower the electricity bill by rising the share of the self-consumption in a private house, it is very important to have a photovoltaic power energy forecast for the next day(s. While the commercially available forecasting services might not meet the household prosumers interests due to the price or complexity we have developed a forecasting methodology that is based on the common weather forecast. Since the forecasted meteorological data does not include the solar irradiance information, but only the weather condition, the uncertainty of the results is relatively high. However, in the presented approach, irradiance is calculated from discrete weather conditions and with correlation of forecasted meteorological data, an RMS error of 65%, and a R2 correlation factor of 0.85 is feasible.

  19. Constructing forward price curves in electricity markets

    DEFF Research Database (Denmark)

    Fleten, S.-E.; Lemming, Jørgen Kjærgaard

    2003-01-01

    We present and analyze a method for constructing approximated high-resolution forward price curves in electricity markets. Because a limited number of forward or futures contracts are traded in the market, only a limited picture of the theoretical continuous forward price curve is available...... to the analyst. Our method combines the information contained in observed bid and ask prices with information from the forecasts generated by bottom-up models. As an example, we use information concerning the shape of the seasonal variation from a bottom-up model to improve the forward price curve quoted...

  20. A New Hybrid Model Based on Data Preprocessing and an Intelligent Optimization Algorithm for Electrical Power System Forecasting

    Directory of Open Access Journals (Sweden)

    Ping Jiang

    2015-01-01

    Full Text Available The establishment of electrical power system cannot only benefit the reasonable distribution and management in energy resources, but also satisfy the increasing demand for electricity. The electrical power system construction is often a pivotal part in the national and regional economic development plan. This paper constructs a hybrid model, known as the E-MFA-BP model, that can forecast indices in the electrical power system, including wind speed, electrical load, and electricity price. Firstly, the ensemble empirical mode decomposition can be applied to eliminate the noise of original time series data. After data preprocessing, the back propagation neural network model is applied to carry out the forecasting. Owing to the instability of its structure, the modified firefly algorithm is employed to optimize the weight and threshold values of back propagation to obtain a hybrid model with higher forecasting quality. Three experiments are carried out to verify the effectiveness of the model. Through comparison with other traditional well-known forecasting models, and models optimized by other optimization algorithms, the experimental results demonstrate that the hybrid model has the best forecasting performance.

  1. An empirical examination of restructured electricity prices

    International Nuclear Information System (INIS)

    Knittel, C.R.; Roberts, M.R.

    2005-01-01

    We present an empirical analysis of restructured electricity prices. We study the distributional and temporal properties of the price process in a non-parametric framework, after which we parametrically model the price process using several common asset price specifications from the asset-pricing literature, as well as several less conventional models motivated by the peculiarities of electricity prices. The findings reveal several characteristics unique to electricity prices including several deterministic components of the price series at different frequencies. An 'inverse leverage effect' is also found, where positive shocks to the price series result in larger increases in volatility than negative shocks. We find that forecasting performance in dramatically improved when we incorporate features of electricity prices not commonly modelled in other asset prices. Our findings have implications for how empiricists model electricity prices, as well as how theorists specify models of energy pricing. (author)

  2. Generation Mix Study Focusing on Nuclear Power by Practical Peak Forecast

    Energy Technology Data Exchange (ETDEWEB)

    Shin, Jung Ho; Roh, Myung Sub [KEPCO International Nuclear Graduate School, Ulsan (Korea, Republic of)

    2013-10-15

    The excessive underestimation can lead to a range of problem; expansion of LNG plant requiring short construction period, the following increase of electricity price, low reserve margin and inefficient configuration of power source. With regard to nuclear power, the share of the stable and economic base load plant, nuclear power, can reduce under the optimum level. Amongst varied factors which contribute to the underestimate, immoderate target for demand side management (DSM) including double deduction of the constraint amount by DSM from peak demand forecast is one of the causes. The hypothesis in this study is that the better optimum generation mix including the adequate share of nuclear power can be obtained under the condition of the peak demand forecast without deduction of DSM target because this forecast is closer to the actual peak demand. In this study, the hypothesis is verified with comparison between peak demand forecast before (or after) DSM target application and the actual peak demand in the 3{sup rd} through 5{sup th} BPE from 2006 to 2010. Furthermore, this research compares and analyzes several generation mix in 2027 focusing on the nuclear power by a few conditions using the WASP-IV program on the basis of the 6{sup th} BPE in 2013. According to the comparative analysis on the peak demand forecast and actual peak demand from 2006 to 2010, the peak demand forecasts without the deduction of the DSM target is closer to the actual peak demand than the peak demand forecasts considering the DSM target in the 3{sup th}, 4{sup th}, 5{sup th} entirely. In addition, the generation mix until 2027 is examined by the WASP-IV. As a result of the program run, when considering the peak demand forecast without DSM reflection, since the base load plants including nuclear power take up adequate proportion, stable and economic supply of electricity can be achieved. On the contrary, in case of planning based on the peak demand forecast with DSM reflected and then

  3. Pharmaceutical expenditure forecast model to support health policy decision making.

    Science.gov (United States)

    Rémuzat, Cécile; Urbinati, Duccio; Kornfeld, Åsa; Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Mzoughi, Olfa; Toumi, Mondher

    2014-01-01

    With constant incentives for healthcare payers to contain their pharmaceutical budgets, modelling policy decision impact became critical. The objective of this project was to test the impact of various policy decisions on pharmaceutical budget (developed for the European Commission for the project 'European Union (EU) Pharmaceutical expenditure forecast' - http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). A model was built to assess policy scenarios' impact on the pharmaceutical budgets of seven member states of the EU, namely France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. The following scenarios were tested: expanding the UK policies to EU, changing time to market access, modifying generic price and penetration, shifting the distribution chain of biosimilars (retail/hospital). Applying the UK policy resulted in dramatic savings for Germany (10 times the base case forecast) and substantial additional savings for France and Portugal (2 and 4 times the base case forecast, respectively). Delaying time to market was found be to a very powerful tool to reduce pharmaceutical expenditure. Applying the EU transparency directive (6-month process for pricing and reimbursement) increased pharmaceutical expenditure for all countries (from 1.1 to 4 times the base case forecast), except in Germany (additional savings). Decreasing the price of generics and boosting the penetration rate, as well as shifting distribution of biosimilars through hospital chain were also key methods to reduce pharmaceutical expenditure. Change in the level of reimbursement rate to 100% in all countries led to an important increase in the pharmaceutical budget. Forecasting pharmaceutical expenditure is a critical exercise to inform policy decision makers. The most important leverages identified by the model on pharmaceutical budget were driven by generic and biosimilar prices, penetration rate, and distribution. Reducing, even slightly, the prices of

  4. Contrail Cirrus Forecasts for the ML-CIRRUS Experiment and Some Comparison Results

    Science.gov (United States)

    Schumann, Ulrich; Graf, Kaspar; Bugliaro, Luca; Dörnbrack, Andreas; Giez, Andreas; Jurkat, Tina; Kaufmann, Stefan; Krämer, Martina; Minikin, Andreas; Schäfler, Andreas; Voigt, Christiane; Wirth, Martin; Zahn, Andreas; Ziereis, Helmut

    2015-04-01

    rerun with improved ECMWF-NWP data (at one-hour time resolution). The model results are included in the HALO mission data bank, and the results are available for comparison to in-situ data. The data are useful for identifying aircraft and other sources for measured air properties. The joint analysis of observations and model result has basically just started. Preliminary results from comparisons with lidar-measured extinction profiles, in-situ measured humidity, nitrogen oxides, and aerosol and ice particle concentrations, and with meteorological observations (wind, temperature etc.) illustrate the expected gain in insight. The contrail forecasts have been checked by comparison to available data including satellite data and HALO observations. During the campaign, it became obvious that predicted contrail cirrus cover compared qualitatively mostly well with what was found when HALO reached predicted cirrus regions. From the analysis of the measured data, some examples of significant correlation between model results and observations have been found. However, the quantitative agreement is not uniform. As expected, nature is far more variable than a model can predict. The observed optical properties of cirrus and contrails vary far more in time and space than predicted. Local values were often far higher or lower than mean values. A one-to-one correlation between local observations and model results is not to be expected. This inhomogeneity may have consequences for the climate impact of aviation induced cloud changes.

  5. Determining the best forecasting method to estimate unitary charges price indexes of PFI data in central region Peninsular Malaysia

    Science.gov (United States)

    Ahmad Kamaruddin, Saadi Bin; Md Ghani, Nor Azura; Mohamed Ramli, Norazan

    2013-04-01

    The concept of Private Financial Initiative (PFI) has been implemented by many developed countries as an innovative way for the governments to improve future public service delivery and infrastructure procurement. However, the idea is just about to germinate in Malaysia and its success is still vague. The major phase that needs to be given main attention in this agenda is value for money whereby optimum efficiency and effectiveness of each expense is attained. Therefore, at the early stage of this study, estimating unitary charges or materials price indexes in each region in Malaysia was the key objective. This particular study aims to discover the best forecasting method to estimate unitary charges price indexes in construction industry by different regions in the central region of Peninsular Malaysia (Selangor, Federal Territory of Kuala Lumpur, Negeri Sembilan, and Melaka). The unitary charges indexes data used were from year 2002 to 2011 monthly data of different states in the central region Peninsular Malaysia, comprising price indexes of aggregate, sand, steel reinforcement, ready mix concrete, bricks and partition, roof material, floor and wall finishes, ceiling, plumbing materials, sanitary fittings, paint, glass, steel and metal sections, timber and plywood. At the end of the study, it was found that Backpropagation Neural Network with linear transfer function produced the most accurate and reliable results for estimating unitary charges price indexes in every states in central region Peninsular Malaysia based on the Root Mean Squared Errors, where the values for both estimation and evaluation sets were approximately zero and highly significant at p Malaysia. The estimated price indexes of construction materials will contribute significantly to the value for money of PFI as well as towards Malaysian economical growth.

  6. Heavy oil supply economics and supply response to low oil prices

    International Nuclear Information System (INIS)

    Fisher, L.

    1999-01-01

    The dynamics of the heavy oil industry are examined, including prices, market demand, supply and supply costs. Price assumptions are provided for the reference case oil price (west Texas intermediate at Cushing). Supply cost methodology is explained. Capital and operating costs for various heavy oil and synthetic sources are derived from modeling results. The range of supply costs for heavy oil and bitumen from various sources, supply costs in terms of reference case market values and in terms of 1995-1996 average market values for Bow River crude, are derived. The CERI long term supply forecast model is explained. Western Canada upstream oil and gas cash flow and capital expenditures, eastern Canada exploration and expenditures by hydrocarbon type, and Canadian heavy oil and bitumen production based on reference case prices are estimated. Based on these projections the outlook for heavy oil at reference case prices for better than average quality resources is judged to be economic. Lower quality resources will require technology gains for successful commercialization. SAGD is a likely candidate in this respect. Again based on reference prices, production is forecast to decline by 100 Kb/d over the next five years. Diluent supply is considered to be adequate throughout the forecast period. As far as thermal bitumen is concerned, the growth could, in fact, exceed the projection, but if so, more upgrading will be required. 11 figs

  7. Estimating oil price 'Value at Risk' using the historical simulation approach

    International Nuclear Information System (INIS)

    David Cabedo, J.; Moya, Ismael

    2003-01-01

    In this paper we propose using Value at Risk (VaR) for oil price risk quantification. VaR provides an estimation for the maximum oil price change associated with a likelihood level, and can be used for designing risk management strategies. We analyse three VaR calculation methods: the historical simulation standard approach, the historical simulation with ARMA forecasts (HSAF) approach, developed in this paper, and the variance-covariance method based on autoregressive conditional heteroskedasticity models forecasts. The results obtained indicate that HSAF methodology provides a flexible VaR quantification, which fits the continuous oil price movements well and provides an efficient risk quantification

  8. Estimating oil price 'Value at Risk' using the historical simulation approach

    International Nuclear Information System (INIS)

    Cabedo, J.D.; Moya, I.

    2003-01-01

    In this paper we propose using Value at Risk (VaR) for oil price risk quantification. VaR provides an estimation for the maximum oil price change associated with a likelihood level, and can be used for designing risk management strategies. We analyse three VaR calculation methods: the historical simulation standard approach, the historical simulation with ARMA forecasts (HSAF) approach. developed in this paper, and the variance-covariance method based on autoregressive conditional heteroskedasticity models forecasts. The results obtained indicate that HSAF methodology provides a flexible VaR quantification, which fits the continuous oil price movements well and provides an efficient risk quantification. (author)

  9. World oil prices, precious metal prices and macroeconomy in Turkey

    International Nuclear Information System (INIS)

    Soytas, Ugur; Sari, Ramazan; Hammoudeh, Shawkat; Hacihasanoglu, Erk

    2009-01-01

    We examine the long- and short-run transmissions of information between the world oil price, Turkish interest rate, Turkish lira-US dollar exchange rate, and domestic spot gold and silver price. We find that the world oil price has no predictive power of the precious metal prices, the interest rate or the exchange rate market in Turkey. The results also show that the Turkish spot precious metals, exchange rate and bond markets do not also provide information that would help improve the forecasts of world oil prices in the long run. The findings suggest that domestic gold is also considered a safe haven in Turkey during devaluation of the Turkish lira, as it is globally. It is interesting to note that there does not seem to be any significant influence of developments in the world oil markets on Turkish markets in the short run either. However, transitory positive initial impacts of innovations in oil prices on gold and silver markets are observed. The short-run price transmissions between the world oil market and the Turkish precious metal markets have implications for policy makers in emerging markets and both local and global investors in the precious metals market and the oil market.

  10. Energy forecast. Final report; Energiudsigten. Slutrapport

    Energy Technology Data Exchange (ETDEWEB)

    2010-04-15

    A number of instruments, i.e. Internet, media campaigns, boxes displaying electricity prices (SEE1) and spot contract has been tested for households to shift their electricity consumption to times when prices are low. Of the implemented media campaigns, only the daily viewing of Energy forecast on TV had an impact. Consumers gained greater knowledge of electricity prices and electricity consumption loads, but only showed little interest in shifting electricity consumption. However, a measurable effect appeared at night with the group that had both concluded a spot contract and received an SEE1. These factors increase the awareness of the price of electricity and the possibility of shifting electricity consumption. (Energy 10)

  11. Modeling and forecasting natural gas demand in Bangladesh

    International Nuclear Information System (INIS)

    Wadud, Zia; Dey, Himadri S.; Kabir, Md. Ashfanoor; Khan, Shahidul I.

    2011-01-01

    Natural gas is the major indigenous source of energy in Bangladesh and accounts for almost one-half of all primary energy used in the country. Per capita and total energy use in Bangladesh is still very small, and it is important to understand how energy, and natural gas demand will evolve in the future. We develop a dynamic econometric model to understand the natural gas demand in Bangladesh, both in the national level, and also for a few sub-sectors. Our demand model shows large long run income elasticity - around 1.5 - for aggregate demand for natural gas. Forecasts into the future also show a larger demand in the future than predicted by various national and multilateral organizations. Even then, it is possible that our forecasts could still be at the lower end of the future energy demand. Price response was statistically not different from zero, indicating that prices are possibly too low and that there is a large suppressed demand for natural gas in the country. - Highlights: → Natural gas demand is modeled using dynamic econometric methods, first of its kind in Bangladesh. → Income elasticity for aggregate natural gas demand in Bangladesh is large-around 1.5. → Demand is price insensitive, indicating too low prices and/or presence of large suppressed demand. → Demand forecasts reveal large divergence from previous estimates, which is important for planning. → Attempts to model demand for end-use sectors were successful only for the industrial sector.

  12. Modeling and forecasting natural gas demand in Bangladesh

    Energy Technology Data Exchange (ETDEWEB)

    Wadud, Zia, E-mail: ziawadud@yahoo.com [Bangladesh University of Engineering and Technology (Bangladesh); Dey, Himadri S. [University of Notre Dame (United States); Kabir, Md. Ashfanoor; Khan, Shahidul I. [Bangladesh University of Engineering and Technology (Bangladesh)

    2011-11-15

    Natural gas is the major indigenous source of energy in Bangladesh and accounts for almost one-half of all primary energy used in the country. Per capita and total energy use in Bangladesh is still very small, and it is important to understand how energy, and natural gas demand will evolve in the future. We develop a dynamic econometric model to understand the natural gas demand in Bangladesh, both in the national level, and also for a few sub-sectors. Our demand model shows large long run income elasticity - around 1.5 - for aggregate demand for natural gas. Forecasts into the future also show a larger demand in the future than predicted by various national and multilateral organizations. Even then, it is possible that our forecasts could still be at the lower end of the future energy demand. Price response was statistically not different from zero, indicating that prices are possibly too low and that there is a large suppressed demand for natural gas in the country. - Highlights: > Natural gas demand is modeled using dynamic econometric methods, first of its kind in Bangladesh. > Income elasticity for aggregate natural gas demand in Bangladesh is large-around 1.5. > Demand is price insensitive, indicating too low prices and/or presence of large suppressed demand. > Demand forecasts reveal large divergence from previous estimates, which is important for planning. > Attempts to model demand for end-use sectors were successful only for the industrial sector.

  13. Forecasting European thermal coal spot prices

    Directory of Open Access Journals (Sweden)

    Alicja Krzemień

    2015-01-01

    Finally, in order to analyse the time series model performance a Generalized Regression Neural Network (GRNN was used and its performance compared against the whole AR(2 process. Empirical results obtained confirmed that there is no statistically significant difference between both methods. The GRNN analysis also allowed pointing out the main drivers that move the European Thermal Coal Spot prices: crude oil, USD/CNY change and supply side drivers.

  14. Commodity derivatives pricing with inventory effects

    DEFF Research Database (Denmark)

    Bach, Christian; Dziubinski, Matt P.

    We introduce tractable models for commodity derivatives pricing with inventory and volatility eects, and illustrate with applications to the oil market. We contribute to the existing literature in several respects. First, whereas the previous literature uses futures data for investigating...... the relationship between inventory and volatility, we use the information available in options traded on futures. Second, performance assessment in the previous literature has primarily evolved around explaining moments of data or forecasting prices of futures. Instead, we asses the performance of our model...... by considering both the ability of explaining prices in-sample and out-of-sample - assessing both the pricing-performance and the hedging-performance of the models. Third, we model the futures surface rather than the spot price process, and from the no-arbitrage relationship between spot and futures prices we...

  15. Option Pricing with Asymmetric Heteroskedastic Normal Mixture Models

    DEFF Research Database (Denmark)

    Rombouts, Jeroen V. K; Stentoft, Lars

    2015-01-01

    We propose an asymmetric GARCH in mean mixture model and provide a feasible method for option pricing within this general framework by deriving the appropriate risk neutral dynamics. We forecast the out-of-sample prices of a large sample of options on the S&P 500 index from January 2006 to December...

  16. Northeast U.S. update: price and demand issues

    International Nuclear Information System (INIS)

    Lucy, M.S.

    1997-01-01

    The issues affecting natural gas prices in the northeast United States were discussed. The supply of natural gas is high because of new pipeline projects and new market entrants. The demand for natural gas is also high because of nuclear plant closings, new electric plants, the clean air act, and economic growth. The supply of natural gas is expected to grow in the Northeast by 27 per cent by the year 2000. Future pipeline projects from Western Canada to Chicago, New York and Boston were examined and their effect on supply and pricing were analyzed. As another variable that affects the pricing of natural gas, a list of the nuclear plants that have closed and which may soon close in the northeast United States was provided, along with a list of new gas fired plants. Other factors affecting winter market prices in the northeast United States for 1997-1998 include El Nino, warm weather forecasts, NYMEX forecasts, natural gas demand, and low oil prices, were also reviewed. Cultivating long term comprehensive relationships, and focusing on customer service were considered the key to successful Canadian export growth. 7 tabs., 5 figs

  17. Projection of coal prices in international turnover in comparison to other primary energy sources; Przewidywane ceny wegla w handlu miedzynarodowym w porownaniu z innymi nosnikami energii pierwotnej

    Energy Technology Data Exchange (ETDEWEB)

    Gawlik, L.; Grudzinski, Z. [Polish Academy of Sciences, Krakow (Poland). Mineral and Energy Economy Institute

    2004-07-01

    The paper looks at trends in both steam coal and coking coal prices up to the year 2000 and then compares the trends of coal with crude oil and natural gas for the years 1987-2001. It presents forecasts for fossil fuel prices up to the year 2015. Despite temporary fluctuation, prices of coal are predicted to remain relatively stable as far as 2030, with a slight tendency to grow. 11 refs., 6 figs., 1 tab.

  18. Pool Strategy of a Price-Maker Wind Power Producer

    DEFF Research Database (Denmark)

    Zugno, Marco; Morales González, Juan Miguel; Pinson, Pierre

    2013-01-01

    We consider the problem of a wind power producer trading energy in short-term electricity markets. The producer is a price-taker in the day-ahead market, but a price-maker in the balancing market, and aims at optimizing its expected revenues from these market floors. The problem is formulated...... or median forecast of wind power distribution. Finally, sensitivity analyses are carried out to assess the impact on the offering strategy of the producer's penetration in the market, of the correlation between wind power production and residual system deviation, and of the shape of the forecast...

  19. Oil prices: The role of refinery utilization, futures markets and non-linearities

    International Nuclear Information System (INIS)

    Kaufmann, Robert K.; Mann, Michael; Dees, Stephane; Gasteuil, Audrey

    2008-01-01

    We test the hypothesis that real oil prices are determined in part by refinery capacity, non-linearities in supply conditions, and/or expectations and that observed changes in these variables can account for the rise in prices between 2004 and 2006. Results indicate that the refining sector plays an important role in the recent price increase, but not in the way described by many analysts. The relationship is negative such that higher refinery utilization rates reduce crude oil prices. This effect is associated with shifts in the production of heavy and light grades of crude oil and price spreads between them. Non-linear relationships between OPEC capacity and oil prices as well as conditions on the futures markets also account for changes in real oil prices. Together, these factors allow the model to generate a one-step ahead out-of-sample forecast that performs as well as forecasts implied by far-month contracts on the New York Mercantile Exchange and is able to account for much of the $27 rise in crude oil prices between 2004 and 2006. (author)

  20. A comparison between the ECMWF and COSMO Ensemble Prediction Systems applied to short-term wind power forecasting on real data

    DEFF Research Database (Denmark)

    Alessandrini, S.; Sperati, S.; Pinson, Pierre

    2013-01-01

    together with a single forecast power value for each future time horizon. A comparison between two different ensemble forecasting models, ECMWF EPS (Ensemble Prediction System in use at the European Centre for Medium-Range Weather Forecasts) and COSMO-LEPS (Limited-area Ensemble Prediction System developed...... ahead forecast horizon. A statistical calibration of the ensemble wind speed members based on the use of past wind speed measurements is explained. The two models are compared using common verification indices and diagrams. The higher horizontal resolution model (COSMO-LEPS) shows slightly better...

  1. Constructing forward price curves in electricity markets

    International Nuclear Information System (INIS)

    Fleten, Stein-Erik; Lemming, Jacob

    2003-01-01

    We present and analyze a method for constructing approximated high-resolution forward price curves in electricity markets. Because a limited number of forward or futures contracts are traded in the market, only a limited picture of the theoretical continuous forward price curve is available to the analyst. Our method combines the information contained in observed bid and ask prices with information from the forecasts generated by bottom-up models. As an example, we use information concerning the shape of the seasonal variation from a bottom-up model to improve the forward price curve quoted on the Nordic power exchange

  2. A framework for the assessment of the oil price

    International Nuclear Information System (INIS)

    Oliveira, A. de; Guimaraes Lodi, C.F.

    1994-01-01

    There is a wide diversity of interpretations in the literature about the mechanisms that govern oil prices. None of them has been able to produce reasonable price forecasts. Estimates of future oil prices, or at least of a range of prices, are essential for policy-making. A better understanding of the interplay of the forces that affect oil prices is absolutely necessary. This paper aims to offer a contribution to the improvement of this understanding. It suggests a rational analytical framework to estimate the future price of oil, based on the objectives and policies of the major players in the oil market. (author)

  3. An overview of alternative fossil fuel price and carbon regulation scenarios

    Energy Technology Data Exchange (ETDEWEB)

    Wiser, Ryan; Bolinger, Mark

    2004-10-01

    The benefits of the Department of Energy's research and development (R&D) efforts have historically been estimated under business-as-usual market and policy conditions. In recognition of the insurance value of R&D, however, the Office of Energy Efficiency and Renewable Energy (EERE) and the Office of Fossil Energy (FE) have been exploring options for evaluating the benefits of their R&D programs under an array of alternative futures. More specifically, an FE-EERE Scenarios Working Group (the Working Group) has proposed to EERE and FE staff the application of an initial set of three scenarios for use in the Working Group's upcoming analyses: (1) a Reference Case Scenario, (2) a High Fuel Price Scenario, which includes heightened natural gas and oil prices, and (3) a Carbon Cap-and-Trade Scenario. The immediate goal is to use these scenarios to conduct a pilot analysis of the benefits of EERE and FE R&D efforts. In this report, the two alternative scenarios being considered by EERE and FE staff--carbon cap-and-trade and high fuel prices--are compared to other scenarios used by energy analysts and utility planners. The report also briefly evaluates the past accuracy of fossil fuel price forecasts. We find that the natural gas prices through 2025 proposed in the FE-EERE Scenarios Working Group's High Fuel Price Scenario appear to be reasonable based on current natural gas prices and other externally generated gas price forecasts and scenarios. If anything, an even more extreme gas price scenario might be considered. The price escalation from 2025 to 2050 within the proposed High Fuel Price Scenario is harder to evaluate, primarily because few existing forecasts or scenarios extend beyond 2025, but, at first blush, it also appears reasonable. Similarly, we find that the oil prices originally proposed by the Working Group in the High Fuel Price Scenario appear to be reasonable, if not conservative, based on: (1) the current forward market for oil, (2

  4. Wind power forecasting accuracy and uncertainty in Finland

    Energy Technology Data Exchange (ETDEWEB)

    Holttinen, H.; Miettinen, J.; Sillanpaeae, S.

    2013-04-15

    Wind power cannot be dispatched so the production levels need to be forecasted for electricity market trading. Lower prediction errors mean lower regulation balancing costs, since relatively less energy needs to go through balance settlement. From the power system operator point of view, wind power forecast errors will impact the system net imbalances when the share of wind power increases, and more accurate forecasts mean less regulating capacity will be activated from the real time Regulating Power Market. In this publication short term forecasting of wind power is studied mainly from a wind power producer point of view. The forecast errors and imbalance costs from the day-ahead Nordic electricity markets are calculated based on real data from distributed wind power plants. Improvements to forecasting accuracy are presented using several wind forecast providers, and measures for uncertainty of the forecast are presented. Aggregation of sites lowers relative share of prediction errors considerably, up to 60%. The balancing costs were also reduced up to 60%, from 3 euro/MWh for one site to 1-1.4 euro/MWh to aggregate 24 sites. Pooling wind power production for balance settlement will be very beneficial, and larger producers who can have sites from larger geographical area will benefit in lower imbalance costs. The aggregation benefits were already significant for smaller areas, resulting in 30-40% decrease in forecast errors and 13-36% decrease in unit balancing costs, depending on the year. The resulting costs are strongly dependent on Regulating Market prices that determine the prices for the imbalances. Similar level of forecast errors resulted in 40% higher imbalance costs for 2012 compared with 2011. Combining wind forecasts from different Numerical Weather Prediction providers was studied with different combination methods for 6 sites. Averaging different providers' forecasts will lower the forecast errors by 6% for day-ahead purposes. When combining

  5. Energy price comparison of new, renewable, and fossil energy sources

    International Nuclear Information System (INIS)

    Edwaren Liun; Sunardi

    2014-01-01

    Low cost transportation for people and goods is essential to the economic well-being of the nation. Until now, if the oil prices rise, the cost of transportation will automatically follow and most of the people suffering due to soaring prices of food and other items. Almost 100 percent of Indonesian transportation energy demand is supported by oil. Supply disruption - or even the threat of disruption - in the Middle East or elsewhere may lead to a shift in consumer prices and the cost of the industry in significant numbers. While costs in the energy sector, especially electricity in developed countries that also contribute significantly to support the transport sector, is much more stable and predictable. Energy requirements are so high in the transport sector tends to force people to seek the source and means of energy in other forms such as electricity or hydrogen that can match or exceed the performance of fuel oil. This paper aims to analyze the economics of energy price comparison to see the extent of the economic opportunities some kind of energy to play a significant role in the transport sector and the subsequent impact on the energy system. From the results obtained by the analysis that will be increasingly necessary role of nuclear energy and other specific energy as a source of electrical energy considering its economical aspects are relatively better. (author)

  6. A comparison of pay-as-bid and marginal pricing in electricity markets

    Science.gov (United States)

    Ren, Yongjun

    This thesis investigates the behaviour of electricity markets under marginal and pay-as-bid pricing. Marginal pricing is believed to yield the maximum social welfare and is currently implemented by most electricity markets. However, in view of recent electricity market failures, pay-as-bid has been extensively discussed as a possible alternative to marginal pricing. In this research, marginal and pay-as-bid pricing have been analyzed in electricity markets with both perfect and imperfect competition. The perfect competition case is studied under both exact and uncertain system marginal cost prediction. The comparison of the two pricing methods is conducted through two steps: (i) identify the best offer strategy of the generating companies (gencos); (ii) analyze the market performance under these optimum genco strategies. The analysis results together with numerical simulations show that pay-as-bid and marginal pricing are equivalent in a perfect market with exact system marginal cost prediction. In perfect markets with uncertain demand prediction, the two pricing methods are also equivalent but in an expected value sense. If we compare from the perspective of second order statistics, all market performance measures exhibit much lower values under pay-as-bid than under marginal pricing. The risk of deviating from the mean is therefore much higher under marginal pricing than under pay-as-bid. In an imperfect competition market with exact demand prediction, the research shows that pay-as-bid pricing yields lower consumer payments and lower genco profits. This research provides quantitative evidence that challenges some common claims about pay-as-bid pricing. One is that under pay-as-bid, participants would soon learn how to offer so as to obtain the same or higher profits than what they would have obtained under marginal pricing. This research however shows that, under pay-as-bid, participants can at best earn the same profit or expected profit as under marginal

  7. Information flows around the globe: predicting opening gaps from overnight foreign stock price patterns

    NARCIS (Netherlands)

    de Gooijer, J.G.; Diks, C.G.H.; Gatarek, L.T.

    2012-01-01

    This paper describes a forecasting exercise of close-to-open returns on major global stock indices, based on high-frequency price patterns that have become available in foreign markets overnight. Generally speaking, out-ofsample forecast performance depends on the forecast method as well as the

  8. Daily Peak Load Forecasting of Next Day using Weather Distribution and Comparison Value of Each Nearby Date Data

    Science.gov (United States)

    Ito, Shigenobu; Yukita, Kazuto; Goto, Yasuyuki; Ichiyanagi, Katsuhiro; Nakano, Hiroyuki

    By the development of industry, in recent years; dependence to electric energy is growing year by year. Therefore, reliable electric power supply is in need. However, to stock a huge amount of electric energy is very difficult. Also, there is a necessity to keep balance between the demand and supply, which changes hour after hour. Consequently, to supply the high quality and highly dependable electric power supply, economically, and with high efficiency, there is a need to forecast the movement of the electric power demand carefully in advance. And using that forecast as the source, supply and demand management plan should be made. Thus load forecasting is said to be an important job among demand investment of electric power companies. So far, forecasting method using Fuzzy logic, Neural Net Work, Regression model has been suggested for the development of forecasting accuracy. Those forecasting accuracy is in a high level. But to invest electric power in higher accuracy more economically, a new forecasting method with higher accuracy is needed. In this paper, to develop the forecasting accuracy of the former methods, the daily peak load forecasting method using the weather distribution of highest and lowest temperatures, and comparison value of each nearby date data is suggested.

  9. The SEEC United Kingdom energy demand forecast (1993-2000)

    Energy Technology Data Exchange (ETDEWEB)

    Fouquet, R; Hawdon, D; Pearson, P; Robinson, C; Stevens, P

    1993-12-16

    The aims of this paper are to present the underlying determinants of fuel consumption, such as economic activity and prices, develop a series of simple yet reliable sectoral models of energy demand, which incorporate recent modelling developments; provide forecasts of energy demand and its environmental consequences; examine the effects of VAT on domestic fuel and increased competition in the electricity sector; and aid the present debate on energy markets. The paper analyses world oil prices, with a particular focus on Iraq's role, reviews energy policy in the UK and discusses SEEC's expectations about UK fuel prices in coming years and how they vary among sectors. It forecasts final user demand in the domestic, iron and steel, other industry, transport, agricultural, public administration and defence and miscellaneous sectors. The paper also examines the major changes that are underway in electricity generators' demand for fuel, and primary energy consumption and its environmental implications.

  10. Do Exchange Rates Really Help Forecasting Commodity Prices?

    DEFF Research Database (Denmark)

    Bork, Lasse; Kaltwasser, Pablo Rovira; Sercu, Piet

    Chen et al. (2010) report that for ‘commodity currencies’, the exchange rate predicts the country’s commodity index but not vice versa. The commodity currency hypothesis is consistent with the Engle and West (2005) exchange rate model if the fundamental is chosen to be the country’s key export...... expectations, one should mostly observe contemporaneous correlations, not one-directional cross-predictability from one variable toward the other. Using three different data sets and various econometric techniques, we do find the contemporaneous correlations as predicted by the financial asset view......-averaged prices in the commodity index data that they use (price averaging induces spurious autocorrelation and predictability) and to features in their test procedures....

  11. Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting

    Science.gov (United States)

    Zhang, Ningning; Lin, Aijing; Shang, Pengjian

    2017-07-01

    In this paper, we propose a new two-stage methodology that combines the ensemble empirical mode decomposition (EEMD) with multidimensional k-nearest neighbor model (MKNN) in order to forecast the closing price and high price of the stocks simultaneously. The modified algorithm of k-nearest neighbors (KNN) has an increasingly wide application in the prediction of all fields. Empirical mode decomposition (EMD) decomposes a nonlinear and non-stationary signal into a series of intrinsic mode functions (IMFs), however, it cannot reveal characteristic information of the signal with much accuracy as a result of mode mixing. So ensemble empirical mode decomposition (EEMD), an improved method of EMD, is presented to resolve the weaknesses of EMD by adding white noise to the original data. With EEMD, the components with true physical meaning can be extracted from the time series. Utilizing the advantage of EEMD and MKNN, the new proposed ensemble empirical mode decomposition combined with multidimensional k-nearest neighbor model (EEMD-MKNN) has high predictive precision for short-term forecasting. Moreover, we extend this methodology to the case of two-dimensions to forecast the closing price and high price of the four stocks (NAS, S&P500, DJI and STI stock indices) at the same time. The results indicate that the proposed EEMD-MKNN model has a higher forecast precision than EMD-KNN, KNN method and ARIMA.

  12. DIREM's prices. Prices and margins of petroleum products in France and in the European Union

    International Nuclear Information System (INIS)

    2002-08-01

    This report presents in a series of graphs and tables the prices and margins of petroleum products in France and in the European Union (EU) according to the data compiled by the Direction of energy and mineral resources (DIREM) of the French general direction of energy and raw materials (DGEMP, Ministry of economy, finances and industry): evolution of crude prices, evolution of Rotterdam's quotation of petroleum products, raw margin of brent refining, French fuel prices (automotive and domestic fuels, evolution, comparison with EU and Rotterdam's prices), comparison with prices in other European countries, evolution of average retail prices in France. (J.S.)

  13. The relation of monthly spot to futures prices for natural gas

    International Nuclear Information System (INIS)

    Herbert, J.H.

    1993-01-01

    The relationship between the spot price for natural gas for a delivery month and the futures contract price for the same delivery month is examined. The estimated regression equation provides a good summary of the relationship between spot and futures prices for the time period and can also be used to obtain accurate forecasts of spot prices. It appears that the natural gas futures market is inefficient. (author)

  14. Forecasting short-term wind farm production in complex terrain. Volume 1

    International Nuclear Information System (INIS)

    LeBlanc, M.

    2005-01-01

    Wind energy forecasting adds financial value to wind farms and may soon become a regulatory requirement. A robust information technology system is essential for addressing industry demands. Various forecasting methodologies for short-term wind production in complex terrain were presented. Numerical weather predictions were discussed with reference to supervisory control and data acquisition (SCADA) system site measurements. Forecasting methods using wind speed, direction, temperature and pressure, as well as issues concerning statistical modelling were presented. Model output statistics and neural networks were reviewed, as well as significant components of error. Results from a Garrad Hassan forecaster with a European wind farm were presented, including wind speed evaluation, and forecast horizon for T + 1 hours, T + 12 hours, and T + 36 hours. It was suggested that buy prices often reflect the cost of under-prediction, and that forecasting has more potential where the spread is greatest. Accurate T + 19 hours to T + 31 hours could enable participation in the day-ahead market, which is less volatile and prices are usually better. Estimates of possible profits per annum through the use of GH forecaster power predictions were presented, calculated over and above spilling power to the grid. It was concluded that accurate forecasts combined with certainty evaluation enables the optimization of wind energy in the market, and is applicable to a wide range of weather regimes and terrain types. It was suggested that site feedback is essential for good forecasts at short horizons, and that the value of forecasting is dependent on the market. refs., tabs., figs

  15. A rough multi-factor model of electricity spot prices

    International Nuclear Information System (INIS)

    Bennedsen, Mikkel

    2017-01-01

    We introduce a new continuous-time mathematical model of electricity spot prices which accounts for the most important stylized facts of these time series: seasonality, spikes, stochastic volatility, and mean reversion. Empirical studies have found a possible fifth stylized fact, roughness, and our approach explicitly incorporates this into the model of the prices. Our setup generalizes the popular Ornstein–Uhlenbeck-based multi-factor framework of and allows us to perform statistical tests to distinguish between an Ornstein–Uhlenbeck-based model and a rough model. Further, through the multi-factor approach we account for seasonality and spikes before estimating – and making inference on – the degree of roughness. This is novel in the literature and we present simulation evidence showing that these precautions are crucial for accurate estimation. Lastly, we estimate our model on recent data from six European energy exchanges and find statistical evidence of roughness in five out of six markets. As an application of our model, we show how, in these five markets, a rough component improves short term forecasting of the prices. - Highlights: • Statistical modeling of electricity spot prices • Multi-factor decomposition • Roughness • Electricity price forecasting

  16. Uranium price formation. Final report

    International Nuclear Information System (INIS)

    1977-10-01

    The modern uranium industry came into existence in 1946. Until 1966, its sole customer was the Atomic Energy Commission, whose needs for U 3 O 8 relative to industry capacity declined over the years. The development of the commercial market after 1965 coincided with a period of excess capacity and falling nominal and real prices. Gradually in 1973 and dramatically thereafter, market conditions changed and prices rose as utilities sought larger quantities of U 3 O 8 and longer term contracts. Questions about availability of long-run supplies were raised, given the known reserve base. The response of the supply of U 3 O 8 to incentives offered first by the AEC and later by the utilities in the context of new and developing market conventions is examined. The methodology used is microeconomic analysis, qualitatively applied to the history of price formation in the market. Because the study emphasizes the implications of the history of uranium price formation for forecasting supply response, the study presents many different kinds of data and evaluates their quality and appropriateness for forecasting. A simple, very-useful framework for analyzing the history of the market for U 3 O 8 was developed and used to describe supply responses in selected important periods of the industry's development. It is concluded that the response of supply of U 3 O 8 to rising prices or to expectations of demand growth has been impressively strong. The potential reserve inventory is large enough to meet the needs for nuclear power generation through the end of this century. The price necessary to induce producers to find and produce these reserves is uncertain, partly because of problems inherent in estimating long-run supply curves and partly because recent inflation has created major uncertainties about the cost of future supplies

  17. Implementing US GDP in Chained Prices for Cross-country GDP Growth and Sectoral Comparisons: Application to Selected ASEAN Countries

    OpenAIRE

    Dumagan, Jesus C.

    2008-01-01

    GDP in constant prices of ASEAN countries suffers from substitution bias by ignoring relative price changes and makes GDP growth and shares dependent on the base year. These analytical deficiencies led the US since the mid-1990s to convert GDP from constant to chained prices. Thus, cross-country comparisons in constant prices are analytically shaky even with the same base year. Therefore, this paper implements US GDP in chained prices in Indonesia, Malaysia, Philippines, and Thailand to allev...

  18. The oil price

    International Nuclear Information System (INIS)

    Alba, P.

    2000-01-01

    Statistical analysis cannot, alone, provide an oil price forecast. So, one needs to understand the fundamental phenomena which control the past trends since the end of world war II After a first period during which oil, thanks to its abundance, was able to increase its market share at the expense of other energies, the first oil shock reflects the rarefaction of oil resource with the tilting of the US production curve from growth to decline. Since then, the new situation is that of a ''cohabitation'' between oil and the other energies with the oil price, extremely volatile, reflecting the trial and error adjustment of the market share left to the other energies. Such a context may explain the recent oil price surge but the analogy between the US oil situation at the time of the first shock and that existing today for the world outside Middle East suggest another possibility, that of a structural change with higher future oil prices. The authors examine these two possibilities, think that the oil price will reflect both as long as one or the other will not become proven, and conclude with a series of political recommendations. (authors)

  19. Pricing of alcohol in Canada: A comparison of provincial policies and harm-reduction opportunities.

    Science.gov (United States)

    Giesbrecht, Norman; Wettlaufer, Ashley; Thomas, Gerald; Stockwell, Tim; Thompson, Kara; April, Nicole; Asbridge, Mark; Cukier, Samantha; Mann, Robert; McAllister, Janet; Murie, Andrew; Pauley, Chris; Plamondon, Laurie; Vallance, Kate

    2016-05-01

    Alcohol pricing is an effective prevention policy. This paper compares the 10 Canadian provinces on three research-based alcohol pricing policies-minimum pricing, pricing by alcohol content and maintaining prices relative to inflation. The selection of these three policies was based on systematic reviews and seminal research papers. Provincial data for 2012 were obtained from Statistics Canada and relevant provincial ministries, subsequently sent to provincial authorities for verification, and then scored by team members. All provinces, except for Alberta, have minimum prices for at least one beverage type sold in off-premise outlets. All provinces, except for British Columbia and Quebec, have separate (and higher) minimum pricing for on-premise establishments. Regarding pricing on alcohol content, western and central provinces typically scored higher than provinces in Eastern Canada. Generally, minimum prices were lower than the recommended $1.50 per standard drink for off-premise outlets and $3.00 per standard drink in on-premise venues. Seven of 10 provinces scored 60% or higher compared to the ideal on indexing prices to inflation. Prices for a representative basket of alcohol products in Ontario and Quebec have lagged significantly behind inflation since 2006. While examples of evidence-based alcohol pricing policies can be found in every jurisdiction in Canada, significant inter-provincial variation leaves substantial unrealised potential for further reducing alcohol-related harm and costs. This comparative assessment of alcohol price policies provides clear indications of how individual provinces could adjust their pricing policies and practices to improve public health and safety. [Giesbrecht N, Wettlaufer A, Thomas G, Stockwell T, Thompson K, April N, Asbridge M, Cukier S, Mann R, McAllister J, Murie A, Pauley C, Plamondon L, Vallance K. Pricing of alcohol in Canada: A comparison of provincial policies and harm-reduction opportunities. Drug Alcohol Rev

  20. Forecasting fluid milk and cheese demands for the next decade.

    Science.gov (United States)

    Schmit, T M; Kaiser, H M

    2006-12-01

    Predictions of future market demands and farm prices for dairy products are important determinants in developing marketing strategies and farm-production planning decisions. The objective of this report was to use current aggregate forecast data, combined with existing econometric models of demand and supply, to forecast retail demands for fluid milk and cheese and the supply and price of farm milk over the next decade. In doing so, we can investigate whether projections of population and consumer food-spending patterns will extend or alter current consumption trends and examine the implications of future generic advertising strategies for dairy products. To conduct the forecast simulations and appropriately allocate the farm milk supply to various uses, we used a partial equilibrium model of the US domestic dairy sector that segmented the industry into retail, wholesale, and farm markets. Model simulation results indicated that declines in retail per capita demand would persist but at a reduced rate from years past and that retail per capita demand for cheese would continue to grow and strengthen over the next decade. These predictions rely on expected changes in the size of populations of various ages, races, and ethnicities and on existing patterns of spending on food at home and away from home. The combined effect of these forecasted changes in demand levels was reflected in annualized growth in the total farm-milk supply that was similar to growth realized during the past few years. Although we expect nominal farm milk prices to increase over the next decade, we expect real prices (relative to assumed growth in feed costs) to remain relatively stable and show no increase until the end of the forecast period. Supplemental industry model simulations also suggested that net losses in producer revenues would result if only nominal levels of generic advertising spending were maintained in forthcoming years. In fact, if real generic advertising expenditures are

  1. Price caps and price floors in climate policy: a quantitative assessment

    Energy Technology Data Exchange (ETDEWEB)

    Philibert, Cedric

    2008-12-15

    This study assesses the long-term economic and environmental effects of introducing price caps and price floors in hypothetical climate change mitigation architecture, which aims to reduce global energy-related CO2 emissions by 50% by 2050. Based on abatement costs in IPCC and IEA reports, this quantitative analysis confirms what qualitative analyses have already suggested: introducing price caps could significantly reduce economic uncertainty. This uncertainty stems primarily from unpredictable economic growth and energy prices, and ultimately unabated emission trends. In addition, the development of abatement technologies is uncertain. With price caps, the expected costs could be reduced by about 50% and the uncertainty on economic costs could be one order of magnitude lower. Reducing economic uncertainties may spur the adoption of more ambitious policies by helping to alleviate policy makers' concerns of economic risks. Meanwhile, price floors would reduce the level of emissions beyond the objective if the abatement costs ended up lower than forecasted. If caps and floors are commensurate with the ambition of the policy pursued and combined with slightly tightened emission objectives, climatic results could be on average similar to those achieved with 'straight' objectives (i.e. with no cost-containment mechanism). (auth)

  2. Medium-term load forecasting and wholesale transaction profitability

    International Nuclear Information System (INIS)

    Selker, F.K.; Wroblewski, W.R.

    1996-01-01

    The volume of wholesale transactions quoted at firm prices is increasing. The cost, and thus profitability, of serving these contracts strongly depends upon native load during the time of delivery. However, transactions extend beyond load forecasts based on weather information, and long-term resource planning forecasts of load peaks and energy provide inadequate detail. To address this need, Decision Focus Inc. (DFI) and Commonwealth Edison (ComEd) developed a probabilistic, medium-term load forecasting capability. In this paper the authors use a hypothetical utility to explore the impact of uncertain medium-term loads on transaction profitability

  3. Nonlinear expectations in speculative markets – Evidence from the ECB survey of professional forecasters

    DEFF Research Database (Denmark)

    Stadtmann, Georg; Rülke; Reitz

    2012-01-01

    Chartist and fundamentalist models have proven to be capable of replicating stylized facts on speculative markets. In general, this is achieved by specifying nonlinear interactions of otherwise linear asset price expectations of the respective trader groups. This paper investigates whether...... in the neighborhood of the fundamental value, whereas expectations tend to be stabilizing in the presence of substantial oil price misalignment....... or not regressive and extrapolative expectations themselves exhibit significant nonlinear dynamics. The empirical results are based on a new data set from the European Central Bank Survey of Professional Forecasters on oil price expectations. In particular, we find that forecasters form destabilizing expectations...

  4. Frequency domain methods applied to forecasting electricity markets

    International Nuclear Information System (INIS)

    Trapero, Juan R.; Pedregal, Diego J.

    2009-01-01

    The changes taking place in electricity markets during the last two decades have produced an increased interest in the problem of forecasting, either load demand or prices. Many forecasting methodologies are available in the literature nowadays with mixed conclusions about which method is most convenient. This paper focuses on the modeling of electricity market time series sampled hourly in order to produce short-term (1 to 24 h ahead) forecasts. The main features of the system are that (1) models are of an Unobserved Component class that allow for signal extraction of trend, diurnal, weekly and irregular components; (2) its application is automatic, in the sense that there is no need for human intervention via any sort of identification stage; (3) the models are estimated in the frequency domain; and (4) the robustness of the method makes possible its direct use on both load demand and price time series. The approach is thoroughly tested on the PJM interconnection market and the results improve on classical ARIMA models. (author)

  5. The Effects of High and Volatile Oil Prices

    International Nuclear Information System (INIS)

    Artus, Patrick; Autume, Antoine d'; Chalmin, Philippe; Chevalier, Jean-Marie; Coeure, Benoit; Kalantizs, Yannick; Klein, Caroline; Guesnerie, Roger; Callonnec, Gael; Gaudin, Thomas; Moisan, Francois; Lescaroux, Francois; Clerc, Marie; Marcus, Vincent; Lalanne, Guy; Pouliquen, Erwan; Simon, Olivier; Mignon, Valerie

    2010-01-01

    Forecasting work carried out by a number of institutions shows how difficult it is to accurately predict trends in oil prices. The authors of this report do not carry out this forecasting exercise, but they share the same conclusions about the main features of oil price trends in the near and medium term: a rise in oil prices is inevitable, and will be accompanied by significant volatility. This expectation is based on detailed analysis of oil price determinants, their past variations and forecasts as to their future trends. On the supply side, like with all goods, the price of oil reflects production costs: extraction, transport and refining costs. Alongside this essentially technological component, more specific determinants are at play: the noncompetitive economic rent, which largely stems from OPEC's hold on the market, the scarcity rent on all non-renewable natural resources (this rent increases at a rate equal to the real interest rate according to Hotelling's rule), various taxes (mainly the TIPP domestic tax on oil products in France) and a new component that is set to gain importance in the years ahead, namely the implicit price of carbon emissions (which may take the form of a carbon tax or the cost of emission permits). It is difficult to isolate these different components and even more difficult to quantify them, but the authors' detailed analysis shows that most predictable supply-side developments will concur to bring about a rise in oil prices. On the demand side, too, forecasts and projections converge towards a rise in oil prices. Demand trends depend on crude oil prices, taxes, economic growth and energy and environmental policies. In most developed countries, the trend is towards a slowdown in demand growth and some countries are even seeing a decline in demand. In addition to the economic crisis, two explanations are put forward. The levels reached by crude oil and fuel prices in July 2008 clearly brought the price-elasticity of

  6. Value-based pricing

    OpenAIRE

    Netseva-Porcheva Tatyana

    2010-01-01

    The main aim of the paper is to present the value-based pricing. Therefore, the comparison between two approaches of pricing is made - cost-based pricing and value-based pricing. The 'Price sensitively meter' is presented. The other topic of the paper is the perceived value - meaning of the perceived value, the components of perceived value, the determination of perceived value and the increasing of perceived value. In addition, the best company strategies in matrix 'value-cost' are outlined. .

  7. World oil prices flat to declining

    International Nuclear Information System (INIS)

    Adelman, M.A.

    1993-01-01

    A forecast is presented of the likely trends in world oil prices over the short to medium term. A historical background is presented of the OPEC cartel and its role in influencing oil prices. The incentives and disincentives for OPEC to raise prices, and the tensions within the cartel are explored. Slower demand growth and the expansion of natural gas are expected to put downward pressure on oil prices, which are currently artificially high. The impacts of high taxes on development and exploration are examined, and it is shown that state ownership poses an obstacle to improved performance. Threats of price decline are expected to continue to lead to threats of hasty, or even violent action on the part of OPEC members, as happened in 1990. Privatization and tax codes designed to skim rent are positive trends

  8. Investor Expectations, Business Conditions, and the Pricing of Beta-Instability Risk

    OpenAIRE

    William Goetzmann; Akiko Watanabe; Masahiro Watanabe

    2008-01-01

    This paper examines the pricing implications of time-variation in assets' market betas over the business cycle in a conditional CAPM framework. We use a half century of real GDP growth expectations from economists' surveys to determine forecasted economic states. This approach largely avoids the confounding effects of econometric forecasting model error. The expectation measure forecasts the market return controlling for existing predictive variables. The loadings on the expectation measure e...

  9. What Do Experts Know About Forecasting Journal Quality? A Comparison with ISI Research Impact in Finance?

    NARCIS (Netherlands)

    C-L. Chang (Chia-Lin); M.J. McAleer (Michael)

    2013-01-01

    textabstractExperts possess knowledge and information that are not publicly available. The paper is concerned with forecasting academic journal quality and research impact using a survey of international experts from a national project on ranking academic finance journals in Taiwan. A comparison is

  10. What Do Experts Know About Forecasting Journal Quality? A Comparison with ISI Research Impact in Finance

    NARCIS (Netherlands)

    C-L. Chang (Chia-Lin); M.J. McAleer (Michael)

    2013-01-01

    textabstractExperts possess knowledge and information that are not publicly available. The paper is concerned with forecasting academic journal quality and research impact using a survey of international experts from a national project on ranking academic finance journals in Taiwan. A comparison is

  11. 16 CFR 233.2 - Retail price comparisons; comparable value comparisons.

    Science.gov (United States)

    2010-01-01

    ... business). This may be done either on a temporary or a permanent basis, but in either case the advertised...) Another commonly used form of bargain advertising is to offer goods at prices lower than those being... be reasonably certain that the higher price he advertises does not appreciably exceed the price at...

  12. Value-based pricing

    Directory of Open Access Journals (Sweden)

    Netseva-Porcheva Tatyana

    2010-01-01

    Full Text Available The main aim of the paper is to present the value-based pricing. Therefore, the comparison between two approaches of pricing is made - cost-based pricing and value-based pricing. The 'Price sensitively meter' is presented. The other topic of the paper is the perceived value - meaning of the perceived value, the components of perceived value, the determination of perceived value and the increasing of perceived value. In addition, the best company strategies in matrix 'value-cost' are outlined. .

  13. Deregulated power prices: comparison of diurnal patterns

    International Nuclear Information System (INIS)

    Ying Li; Flynn, P.C.

    2004-01-01

    We examine electrical power price, and in particular its daily and average weekday vs. weekend pattern of change, for 14 deregulated markets. Power price in deregulated markets shows fundamentally different patterns. North American markets show a monotonic diurnal weekday price pattern, while all other markets studied show more than one price peak. Deregulated power markets differ in maximum vs. minimum daily average price and in average weekday to weekend price, in turn creating a different incentive for a consumer to time shift power consuming activities. Markets differ in the extent to which a small fraction of the days shapes the average diurnal pattern and value of price. Deregulated markets show a wide variation in the correlation between load and price. Some deregulated markets, most notably Britain and Spain, show patterns that are predictable and consistent, and hence that can encourage a customer to shape consumption behaviors. Other markets, for example South Australia, have patterns that are inconsistent and irregular, and hence are hard for a customer to interpret; a customer in such a market will have a higher incentive to escape risk through hedging mechanisms. (Author)

  14. Deregulated power prices: comparison of diurnal patterns

    International Nuclear Information System (INIS)

    Li Ying; Flynn, Peter C.

    2004-01-01

    We examine electrical power price, and in particular its daily and average weekday vs. weekend pattern of change, for 14 deregulated markets. Power price in deregulated markets shows fundamentally different patterns. North American markets show a monotonic diurnal weekday price pattern, while all other markets studied show more than one price peak. Deregulated power markets differ in maximum vs. minimum daily average price and in average weekday to weekend price, in turn creating a different incentive for a consumer to time shift power consuming activities. Markets differ in the extent to which a small fraction of the days shapes the average diurnal pattern and value of price. Deregulated markets show a wide variation in the correlation between load and price. Some deregulated markets, most notably Britain and Spain, show patterns that are predictable and consistent, and hence that can encourage a customer to shape consumption behaviors. Other markets, for example South Australia, have patterns that are inconsistent and irregular, and hence are hard for a customer to interpret; a customer in such a market will have a higher incentive to escape risk through hedging mechanisms

  15. The fractal feature and price trend in the gold future market at the Shanghai Futures Exchange (SFE)

    Science.gov (United States)

    Wu, Binghui; Duan, Tingting

    2017-05-01

    The price of gold future is affected by many factors, which include the fluctuation of gold price and the change of trading environment. Fractal analysis can help investors gain better understandings of the price fluctuation and make reasonable investment decisions in the gold future market. After analyzing gold future price from January 2th, 2014 to April 12th, 2016 at the Shanghai Futures Exchange (SFE) in China, the conclusion is drawn that the gold future market has sustainability in each trading day, with all Hurst indexes greater than 0.5. The changing features of Hurst index indicate the sustainability of gold future market is strengthened first and weakened then. As a complicatedly nonlinear system, the gold future market can be well reflected by Elman neural network, which is capable of memorizing previous prices and particularly suited for forecasting time series in comparison with other types of neural networks. After analyzing the price trend in the gold future market, the results show that the relative error between the actual value of gold future and the predictive value of Elman neural network is smaller. This model that has a better performance in data fitting and predication, can help investors analyze and foresee the price tendency in the gold future market.

  16. Analyzing the Stability of Price Response Functions: Measuring the Influence of Different Parameters in a Monte Carlo Comparison

    Science.gov (United States)

    Brusch, Michael; Baier, Daniel

    The usage and the estimation of price response function is very important for strategic marketing decisions. Typically price response functions with an empirical basis are used. However, such price response functions are subject to a lot of disturbing influence factors, e.g., the assumed profit maximum price and the assumed corresponding quantity of sales. In such cases, the question how stable the found price response function is was not answered sufficiently up to now. In this paper, the question will be pursued how much (and what kind of) errors in market research are pardonable for a stable price response function. For the comparisons, a factorial design with synthetically generated and disturbed data is used.

  17. Macroeconomic factors and oil futures prices. A data-rich model

    International Nuclear Information System (INIS)

    Zagaglia, Paolo

    2010-01-01

    I study the dynamics of oil futures prices in the NYMEX using a large panel dataset that includes global macroeconomic indicators, financial market indices, quantities and prices of energy products. I extract common factors from the panel data series and estimate a Factor-Augmented Vector Autoregression for the maturity structure of oil futures prices. I find that latent factors generate information that, once combined with that of the yields, improves the forecasting performance for oil prices. Furthermore, I show that a factor correlated to purely financial developments contributes to the model performance, in addition to factors related to energy quantities and prices. (author)

  18. The oil price outlook in 1993 and beyond

    International Nuclear Information System (INIS)

    Himona, I.

    1993-01-01

    The current reality appears to be that with spare capacity at a minimum, oil prices are truly market determined. OPEC's actual and perceived influence is much reduced, and in the absence of the cartel, in view of weak demand and fears about potential Iraqi return, the market's tendency is to push prices down. On the basis of ''good, old fashioned extrapolation'', if the current spare capacity of 5 percent is enough to depress prices, then the forecast excess capacity of 19 per cent by 1996 will certainly achieve as much - if not more. (author)

  19. Steam coal trade: demand, supply and prices to 2020

    Energy Technology Data Exchange (ETDEWEB)

    1993-04-01

    This report on the international seaborne steam coal market was prepared using an electricity generation model developed for each coal-importing country, with the aid of WEFA Energy's power station database. The report contains chapters on: import demand forecasting methodology; orimulsion (environmental considerations and market potential); Scandinavia; North West Europe; British Isles; South West Europe; Eastern Europe; Eastern Mediterranean and North Africa; Asia; Latin America; North America; world steam coal demand summary; trade and price forecasting methodology; base case forecast; shipping rates; import demand; export supply and foreign exchange rates.

  20. A Novel Multiscale Ensemble Carbon Price Prediction Model Integrating Empirical Mode Decomposition, Genetic Algorithm and Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Bangzhu Zhu

    2012-02-01

    Full Text Available Due to the movement and complexity of the carbon market, traditional monoscale forecasting approaches often fail to capture its nonstationary and nonlinear properties and accurately describe its moving tendencies. In this study, a multiscale ensemble forecasting model integrating empirical mode decomposition (EMD, genetic algorithm (GA and artificial neural network (ANN is proposed to forecast carbon price. Firstly, the proposed model uses EMD to decompose carbon price data into several intrinsic mode functions (IMFs and one residue. Then, the IMFs and residue are composed into a high frequency component, a low frequency component and a trend component which have similar frequency characteristics, simple components and strong regularity using the fine-to-coarse reconstruction algorithm. Finally, those three components are predicted using an ANN trained by GA, i.e., a GAANN model, and the final forecasting results can be obtained by the sum of these three forecasting results. For verification and testing, two main carbon future prices with different maturity in the European Climate Exchange (ECX are used to test the effectiveness of the proposed multiscale ensemble forecasting model. Empirical results obtained demonstrate that the proposed multiscale ensemble forecasting model can outperform the single random walk (RW, ARIMA, ANN and GAANN models without EMD preprocessing and the ensemble ARIMA model with EMD preprocessing.

  1. The Interval Slope Method for Long-Term Forecasting of Stock Price Trends

    Directory of Open Access Journals (Sweden)

    Chun-xue Nie

    2016-01-01

    Full Text Available A stock price is a typical but complex type of time series data. We used the effective prediction of long-term time series data to schedule an investment strategy and obtain higher profit. Due to economic, environmental, and other factors, it is very difficult to obtain a precise long-term stock price prediction. The exponentially segmented pattern (ESP is introduced here and used to predict the fluctuation of different stock data over five future prediction intervals. The new feature of stock pricing during the subinterval, named the interval slope, can characterize fluctuations in stock price over specific periods. The cumulative distribution function (CDF of MSE was compared to those of MMSE-BC and SVR. We concluded that the interval slope developed here can capture more complex dynamics of stock price trends. The mean stock price can then be predicted over specific time intervals relatively accurately, in which multiple mean values over time intervals are used to express the time series in the long term. In this way, the prediction of long-term stock price can be more precise and prevent the development of cumulative errors.

  2. Inflation impact of food prices: Case of Serbia

    Directory of Open Access Journals (Sweden)

    Šoškić Dejan

    2015-01-01

    Full Text Available Food prices traditionally have an impact on inflation around the world. Movements in these prices are coming more from the supply side, then from the demand side. If treated as a supply shock, monetary policy should not react. However, food prices are part of headline inflation that is an official target for most central banks. Serbia conducts Inflation targeting and faces serious challenges with food price volatility. Food price volatility in Serbia hampers inflation forecasting, and may have a negative influence on inflationary expectations and public confidence in (i.e. credibility of the Central bank, all of crucial importance for success of Inflation targeting. There are several important possible improvements that may decrease volatility of food prices but also limit negative impact of food price volatility on Consumer Price Index (CPI as a measure of inflation. These improvements are very important for success of Inflation targeting in Serbia.

  3. 16 CFR 233.1 - Former price comparisons.

    Science.gov (United States)

    2010-01-01

    ... each. His usual markup is 50 percent over cost; that is, his regular retail price is $7.50. In order... that the former price is not a fictitious one. If the former price, or the amount or percentage of...

  4. Oil price fluctuations and U.S. dollar exchange rates

    International Nuclear Information System (INIS)

    Lizardo, Radhames A.; Mollick, Andre V.

    2010-01-01

    Adding oil prices to the monetary model of exchange rates, we find that oil prices significantly explain movements in the value of the U.S. dollar (USD) against major currencies from the 1970s to 2008. Our long-run and forecasting results are remarkably consistent with an oil-exchange rate relationship. Increases in real oil prices lead to a significant depreciation of the USD against net oil exporter currencies, such as Canada, Mexico, and Russia. On the other hand, the currencies of oil importers, such as Japan, depreciate relative to the USD when the real oil price goes up. (author)

  5. Retrospective evaluation of appliance price trends

    International Nuclear Information System (INIS)

    Dale, Larry; Antinori, Camille; McNeil, Michael; McMahon, James E.; Sydny Fujita, K.

    2009-01-01

    Real prices of major appliances (refrigerators, dishwashers, heating and cooling equipment) have been falling since the late 1970s despite increases in appliance efficiency and other quality variables. This paper demonstrates that historic increases in efficiency over time, including those resulting from minimum efficiency standards, incur smaller price increases than were expected by the Department of Energy (DOE) forecasts made in conjunction with standards. This effect can be explained by technological innovation, which lowers the cost of efficiency, and by market changes contributing to lower markups and economies of scale in production of higher efficiency units. We reach four principal conclusions about appliance trends and retail price setting: 1.For the past several decades, the retail price of appliances has been steadily falling while efficiency has been increasing. 2.Past retail price predictions made by the DOE analyses of efficiency standards, assuming constant prices over time, have tended to overestimate retail prices. 3.The average incremental price to increase appliance efficiency has declined over time. DOE technical support documents have typically overestimated this incremental price and retail prices. 4.Changes in retail markups and economies of scale in production of more efficient appliances may have contributed to declines in prices of efficient appliances

  6. Natural gas, NGL's and crude: supply, demand and price forecasts

    International Nuclear Information System (INIS)

    Stauft, T.L.

    2003-01-01

    This paper presents an overview of the major issues to watch in the crude oil, natural gas, and natural gas liquids (NGL) markets in North America. The presentation began with background information concerning Purvin and Gertz, an employee-owned consulting firm whose employees are chemical engineers, holders of a Master of Business Administration (MBA), or economists. They specialize in providing strategic, commercial, and technical advice to the international energy industry. A closer look at each individual market was provided, looking at demand, supply, price drivers and others. The author concluded that world oil prices continue to be influenced by a war premium. Oil prices support natural gas, as well as the possibility of a supply issue. The gas processing margins have remained strong. The unknown quantities are the weather and economic recovery. figs

  7. A Technical Analysis Information Fusion Approach for Stock Price Analysis and Modeling

    Science.gov (United States)

    Lahmiri, Salim

    In this paper, we address the problem of technical analysis information fusion in improving stock market index-level prediction. We present an approach for analyzing stock market price behavior based on different categories of technical analysis metrics and a multiple predictive system. Each category of technical analysis measures is used to characterize stock market price movements. The presented predictive system is based on an ensemble of neural networks (NN) coupled with particle swarm intelligence for parameter optimization where each single neural network is trained with a specific category of technical analysis measures. The experimental evaluation on three international stock market indices and three individual stocks show that the presented ensemble-based technical indicators fusion system significantly improves forecasting accuracy in comparison with single NN. Also, it outperforms the classical neural network trained with index-level lagged values and NN trained with stationary wavelet transform details and approximation coefficients. As a result, technical information fusion in NN ensemble architecture helps improving prediction accuracy.

  8. Model documentation: Electricity market module, electricity finance and pricing submodule

    Energy Technology Data Exchange (ETDEWEB)

    1994-04-07

    The purpose of this report is to define the objectives of the model, describe its basic approach, and provide detail on how it works. The EFP is a regulatory accounting model that projects electricity prices. The model first solves for revenue requirements by building up a rate base, calculating a return on rate base, and adding the allowed expenses. Average revenues (prices) are calculated based on assumptions regarding regulator lag and customer cost allocation methods. The model then solves for the internal cash flow and analyzes the need for external financing to meet necessary capital expenditures. Finally, the EFP builds up the financial statements. The EFP is used in conjunction with the National Energy Modeling System (NEMS). Inputs to the EFP include the forecast generating capacity expansion plans, operating costs, regulator environment, and financial data. The outputs include forecasts of income statements, balance sheets, revenue requirements, and electricity prices.

  9. Short-term data forecasting based on wavelet transformation and chaos theory

    Science.gov (United States)

    Wang, Yi; Li, Cunbin; Zhang, Liang

    2017-09-01

    A sketch of wavelet transformation and its application was given. Concerning the characteristics of time sequence, Haar wavelet was used to do data reduction. After processing, the effect of “data nail” on forecasting was reduced. Chaos theory was also introduced, a new chaos time series forecasting flow based on wavelet transformation was proposed. The largest Lyapunov exponent was larger than zero from small data sets, it verified the data change behavior still met chaotic behavior. Based on this, chaos time series to forecast short-term change behavior could be used. At last, the example analysis of the price from a real electricity market showed that the forecasting method increased the precision of the forecasting more effectively and steadily.

  10. A State-Space Estimation of the Lee-Carter Mortality Model and Implications for Annuity Pricing

    OpenAIRE

    Man Chung Fung; Gareth W. Peters; Pavel V. Shevchenko

    2015-01-01

    In this article we investigate a state-space representation of the Lee-Carter model which is a benchmark stochastic mortality model for forecasting age-specific death rates. Existing relevant literature focuses mainly on mortality forecasting or pricing of longevity derivatives, while the full implications and methods of using the state-space representation of the Lee-Carter model in pricing retirement income products is yet to be examined. The main contribution of this article is twofold. Fi...

  11. A model for Long-term Industrial Energy Forecasting (LIEF)

    Energy Technology Data Exchange (ETDEWEB)

    Ross, M. [Lawrence Berkeley Lab., CA (United States)]|[Michigan Univ., Ann Arbor, MI (United States). Dept. of Physics]|[Argonne National Lab., IL (United States). Environmental Assessment and Information Sciences Div.; Hwang, R. [Lawrence Berkeley Lab., CA (United States)

    1992-02-01

    The purpose of this report is to establish the content and structural validity of the Long-term Industrial Energy Forecasting (LIEF) model, and to provide estimates for the model`s parameters. The model is intended to provide decision makers with a relatively simple, yet credible tool to forecast the impacts of policies which affect long-term energy demand in the manufacturing sector. Particular strengths of this model are its relative simplicity which facilitates both ease of use and understanding of results, and the inclusion of relevant causal relationships which provide useful policy handles. The modeling approach of LIEF is intermediate between top-down econometric modeling and bottom-up technology models. It relies on the following simple concept, that trends in aggregate energy demand are dependent upon the factors: (1) trends in total production; (2) sectoral or structural shift, that is, changes in the mix of industrial output from energy-intensive to energy non-intensive sectors; and (3) changes in real energy intensity due to technical change and energy-price effects as measured by the amount of energy used per unit of manufacturing output (KBtu per constant $ of output). The manufacturing sector is first disaggregated according to their historic output growth rates, energy intensities and recycling opportunities. Exogenous, macroeconomic forecasts of individual subsector growth rates and energy prices can then be combined with endogenous forecasts of real energy intensity trends to yield forecasts of overall energy demand. 75 refs.

  12. A model for Long-term Industrial Energy Forecasting (LIEF)

    Energy Technology Data Exchange (ETDEWEB)

    Ross, M. (Lawrence Berkeley Lab., CA (United States) Michigan Univ., Ann Arbor, MI (United States). Dept. of Physics Argonne National Lab., IL (United States). Environmental Assessment and Information Sciences Div.); Hwang, R. (Lawrence Berkeley Lab., CA (United States))

    1992-02-01

    The purpose of this report is to establish the content and structural validity of the Long-term Industrial Energy Forecasting (LIEF) model, and to provide estimates for the model's parameters. The model is intended to provide decision makers with a relatively simple, yet credible tool to forecast the impacts of policies which affect long-term energy demand in the manufacturing sector. Particular strengths of this model are its relative simplicity which facilitates both ease of use and understanding of results, and the inclusion of relevant causal relationships which provide useful policy handles. The modeling approach of LIEF is intermediate between top-down econometric modeling and bottom-up technology models. It relies on the following simple concept, that trends in aggregate energy demand are dependent upon the factors: (1) trends in total production; (2) sectoral or structural shift, that is, changes in the mix of industrial output from energy-intensive to energy non-intensive sectors; and (3) changes in real energy intensity due to technical change and energy-price effects as measured by the amount of energy used per unit of manufacturing output (KBtu per constant $ of output). The manufacturing sector is first disaggregated according to their historic output growth rates, energy intensities and recycling opportunities. Exogenous, macroeconomic forecasts of individual subsector growth rates and energy prices can then be combined with endogenous forecasts of real energy intensity trends to yield forecasts of overall energy demand. 75 refs.

  13. Inflation, Forecast Intervals and Long Memory Regression Models

    NARCIS (Netherlands)

    C.S. Bos (Charles); Ph.H.B.F. Franses (Philip Hans); M. Ooms (Marius)

    2001-01-01

    textabstractWe examine recursive out-of-sample forecasting of monthly postwar U.S. core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading

  14. Inflation, Forecast Intervals and Long Memory Regression Models

    NARCIS (Netherlands)

    Ooms, M.; Bos, C.S.; Franses, P.H.

    2003-01-01

    We examine recursive out-of-sample forecasting of monthly postwar US core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading indicators

  15. Price (slump) forecast : the potential impact on pipelines, producers and marketers

    International Nuclear Information System (INIS)

    Duncan, J.

    2002-01-01

    Throughout this presentation, the speaker answers three basic questions: (1) why are the prices of natural gas so high?, (2) why were the prices of natural gas so high? and (3) will prices for natural gas ever go that high again? The evolution of gas supply and demand including the Canadian supply picture is briefly reviewed. The winter of 2000 and the paradox it presented was discussed, providing a history lesson of an industry taken for granted. The cold winter of 2000 saw industry players scrambling to determine where they would get gas, and the winter of 2001 witnessed them wondering where to put this gas. The new character of the market and the players is discussed, looking at the producer, pipeline expansion projects, and the end user. Neglected investment in the sector and its consequences are dealt with in the next stage of the presentation. The synthetic supply and demand theory are examined. The author concludes the presentation by discussing the factors affecting the market today, such as storage inventory creating volatility, decrease in production and imports due to lag in time when prices are depressed, increased participation by speculators due to increased uncertainty in the stock market, recent weather questions that magnify price movements, and the environment. figs

  16. Methodology for oil prices projections: a study about oil prices differentials for Brent, Arab Light, Bonny Light and Marlin; Metodologia de projecao de precos de petroleos: um estudo dos diferenciais de precos entre o 'Brent', Arabe Leve, 'Bonny Light' e Marlin

    Energy Technology Data Exchange (ETDEWEB)

    Machado, Giovani; Aragao, Amanda; Valle, Ricardo Nascimento e Silva do [Empresa de Pesquisa Energetica (EPE), Brasilia, DF (Brazil)

    2008-07-01

    Oil is not homogenous commodity in terms of its chemical and physical properties, differing from one to another in density (API degree), sulfur content, acidity etc. Such properties imply in price differentials (discount or premium) for each crude to another in the international market. This study presents a basic model to forecasts price of various crudes based on one 'marker' or reference crude price by applying econometric formulations. The relevant crudes for the study are Arab Light, Bonny Light and Marlin, while the 'marker' crude is the Brent. Based on a scenario for the Brent price, prices of Arab Light, Bonny Light and Marlin are forecast to 2020. Findings show that price differentials to Brent are minus US$ 5.09-6.57/b (discount) to Arab Light, plus US$ 1.56-3.47/b (premium) to Bonny Light and minus US$ 9.02-13.95/b (discount) to Marlin in the period analyzed (in constant prices of May/2007). Although such figures are in harmony with expected results (theoretical foundations) of discount/premium by crude quality, structural changes in oil market (in particular, large modifications in world refining conversion capacity), catalyzed by high oil prices and energy policy, may reduce forecast strength of the specifications proposed. (author)

  17. Pharmaceutical expenditure forecast model to support health policy decision making

    Science.gov (United States)

    Rémuzat, Cécile; Urbinati, Duccio; Kornfeld, Åsa; Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Mzoughi, Olfa; Toumi, Mondher

    2014-01-01

    Background and objective With constant incentives for healthcare payers to contain their pharmaceutical budgets, modelling policy decision impact became critical. The objective of this project was to test the impact of various policy decisions on pharmaceutical budget (developed for the European Commission for the project ‘European Union (EU) Pharmaceutical expenditure forecast’ – http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). Methods A model was built to assess policy scenarios’ impact on the pharmaceutical budgets of seven member states of the EU, namely France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. The following scenarios were tested: expanding the UK policies to EU, changing time to market access, modifying generic price and penetration, shifting the distribution chain of biosimilars (retail/hospital). Results Applying the UK policy resulted in dramatic savings for Germany (10 times the base case forecast) and substantial additional savings for France and Portugal (2 and 4 times the base case forecast, respectively). Delaying time to market was found be to a very powerful tool to reduce pharmaceutical expenditure. Applying the EU transparency directive (6-month process for pricing and reimbursement) increased pharmaceutical expenditure for all countries (from 1.1 to 4 times the base case forecast), except in Germany (additional savings). Decreasing the price of generics and boosting the penetration rate, as well as shifting distribution of biosimilars through hospital chain were also key methods to reduce pharmaceutical expenditure. Change in the level of reimbursement rate to 100% in all countries led to an important increase in the pharmaceutical budget. Conclusions Forecasting pharmaceutical expenditure is a critical exercise to inform policy decision makers. The most important leverages identified by the model on pharmaceutical budget were driven by generic and biosimilar prices, penetration rate

  18. Wavelet-based prediction of oil prices

    International Nuclear Information System (INIS)

    Yousefi, Shahriar; Weinreich, Ilona; Reinarz, Dominik

    2005-01-01

    This paper illustrates an application of wavelets as a possible vehicle for investigating the issue of market efficiency in futures markets for oil. The paper provides a short introduction to the wavelets and a few interesting wavelet-based contributions in economics and finance are briefly reviewed. A wavelet-based prediction procedure is introduced and market data on crude oil is used to provide forecasts over different forecasting horizons. The results are compared with data from futures markets for oil and the relative performance of this procedure is used to investigate whether futures markets are efficiently priced

  19. Econometric analysis of Australian emissions markets and electricity prices

    International Nuclear Information System (INIS)

    Cotton, Deborah; De Mello, Lurion

    2014-01-01

    Emissions trading schemes aim to reduce the emissions in certain pollutants using a market based scheme where participants can buy and sell permits for these emissions. This paper analyses the efficiency of the two largest schemes in Australia, the NSW Greenhouse Gas Abatement Scheme and the Mandatory Renewable Energy Trading Scheme, through their effect on the electricity prices from 2004 to 2010. We use a long run structural modelling technique for the first time on this market. It provides a practical long-run approach to structural relationships which enable the determination of the effectiveness of the theoretical expectations of these schemes. The generalised forecast error variance decomposition analysis finds that both schemes' emissions prices have little effect on electricity prices. Generalised impulse response function analysis support this finding indicating that when shocks are applied to electricity by the two schemes it returns to equilibrium very quickly. This indicates that these schemes are not having the effect anticipated in their legislation. - Highlights: • We analyse two emissions trading schemes in Australia. • We test for their effect on wholesale electricity prices. • The test uses generalised forecast error variance decomposition analysis. • The tests find long run relationship between the variables in both the samples. • The short run-dynamics indicate that they play a minimal role in electricity prices

  20. Implications of commodity price risk and operating leverage on petroleum project economic evaluations

    International Nuclear Information System (INIS)

    Salahor, G.; Laughton, D.G.

    1999-01-01

    The modern asset pricing method, MAP, can provide businesses with improved tools for economic analysis. This in turn leads to greater precision in the analysis of the effects of the following parameters: project structure, time, and uncertainty. This greater precision with MAP extends to analysis of the possibility for active control of the decision alternatives for managers in the petroleum business, especially where this possibility is not questioned. A methodology is developed as a model that quantifies revenue risk based on the nature of commodity price volatility and the accepted price of risk in the commodity market. A mathematical description is included of a natural gas log-normal distribution incorporating the annual volatility in the forecast, and a measure of the rate at which volatility decreases in the long run in the forecast. Give this volatility model, a risk discount factor is determinable and applicable to the current expectation of the commodity prices at a given time, and a discount time factor of all parts of the cash flow stream. Cases are used to evaluate a natural gas development project for the purpose of yielding scenarios for capital vs. operating cost trade-offs, price risk management, production profile, and the effect of the reverting vs. non-reverting price model. In application one, a comparison is made of discounted cash flow (DCF) to MAP evaluations giving a perspective on the various development choices which a producer has through third-party service providers. Further, an example is used to compare the two methods as alternative evaluations of development alternatives to speed up or slow down the production rate and decline profile of a gas field. As in the first example, the DCF discounting is higher than the net discounting in the MAP evaluation. But in this example both methods produce the same project structure decision. The small amount of incremental capital and operating costs needed for the higher production case are

  1. Short-term forecasts of energy use and energy supply 2012-2014. Spring 2013; Kortsiktsprognos - Oever energianvaendning och energitillfoersel 2012-2014, Vaaren 2013

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2013-03-15

    This report provides a description of the Swedish energy system in 2011 and an assessment of its development between 2012 - 2014. The forecast shall be interpreted as a consequence of the limitations and assumptions underlying it. Thus, it is important to remember that if any of the conditions or assumptions change, the forecast's results will also change. The forecast is based on economic conditions that have been developed from the National Institute of Economic Trend. Other conditions such as electricity prices, fuel prices, outdoor temperature and inflow into reservoirs are based on information available up to January 2013, when forecasting began.

  2. NewsMarket 2.0: Analysis of News for Stock Price Forecasting

    Science.gov (United States)

    Barazzetti, Alessandro; Mastronardi, Rosangela

    Most of the existing financial research tools use a stock's historical price and technical indicators to predict future price trends without taking into account the impact of web news. The recent explosion of demand for information on financial investment management is driving the search for alternative methods of quantitative data analysis.

  3. Price Intransparency, Consumer Decision Making and European Consumer Law

    NARCIS (Netherlands)

    W.H. van Boom (Willem)

    2011-01-01

    textabstractPrice comparison is a basic element of competition. For comparison to work, at least prices need to be transparent. Moreover, price is usually a focal point in consumer thinking and deciding on transactions. Hence, obfuscating prices can be detrimental to consumers. Therefore, it is

  4. Comparison of tiered formularies and reference pricing policies: a systematic review.

    Science.gov (United States)

    Morgan, Steve; Hanley, Gillian; Greyson, Devon

    2009-01-01

    To synthesize methodologically comparable evidence from the published literature regarding the outcomes of tiered formularies and therapeutic reference pricing of prescription drugs. We searched the following electronic databases: ABI/Inform, CINAHL, Clinical Evidence, Digital Dissertations & Theses, Evidence-Based Medicine Reviews (which incorporates ACP Journal Club, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Cochrane Methodology Register, Database of Abstracts of Reviews of Effectiveness, Health Technology Assessments and NHS Economic Evaluation Database), EconLit, EMBASE, International Pharmaceutical Abstracts, MEDLINE, PAIS International and PAIS Archive, and the Web of Science. We also searched the reference lists of relevant articles and several grey literature sources. We sought English-language studies published from 1986 to 2007 that examined the effects of either therapeutic reference pricing or tiered formularies, reported on outcomes relevant to patient care and cost-effectiveness, and employed quantitative study designs that included concurrent or historical comparison groups. We abstracted and assessed potentially appropriate articles using a modified version of the data abstraction form developed by the Cochrane Effective Practice and Organisation of Care Group. From an initial list of 2964 citations, 12 citations (representing 11 studies) were deemed eligible for inclusion in our review: 3 studies (reported in 4 articles) of reference pricing and 8 studies of tiered formularies. The introduction of reference pricing was associated with reduced plan spending, switching to preferred medicines, reduced overall drug utilization and short-term increases in the use of physician services. Reference pricing was not associated with adverse health impacts. The introduction of tiered formularies was associated with reduced plan expenditures, greater patient costs and increased rates of non-compliance with

  5. Can the dynamics of the term structure of petroleum futures be forecasted? Evidence from major markets

    International Nuclear Information System (INIS)

    Skiadopoulos, George; Chantziara, Thalia

    2008-01-01

    We investigate whether the daily evolution of the term structure of petroleum futures can be forecasted. To this end, the principal components analysis is employed. The retained principal components describe the dynamics of the term structure of futures prices parsimoniously and are used to forecast the subsequent daily changes of futures prices. Data on the New York Mercantile Exchange (NYMEX) crude oil, heating oil, gasoline, and the International Petroleum Exchange (IPE) crude oil futures are used. We find that the retained principal components have small forecasting power both in-sample and out-of-sample. Similar results are obtained from standard univariate and vector autoregression models. Spillover effects between the four petroleum futures markets are also detected. (author)

  6. Forecasting Inflation Using Interest-Rate and Time-Series Models: Some International Evidence.

    OpenAIRE

    Hafer, R W; Hein, Scott E

    1990-01-01

    It has been suggested that inflation forecasts derived from short-term interest rates are as accurate as time-series forecasts. Previous analyses of this notion have focused on U.S. data, providing mixed results. In this article, the authors extend previous work by testing the hypothesis using data taken from the United States and five other countries. Using monthly Eurocurrency rates and the consumer price index for the period 1967-86, their results indicate that time-series forecasts of inf...

  7. Short-term uranium price formation: a methodology

    International Nuclear Information System (INIS)

    Hsieh, L.Y.; de Graffenried, C.L.

    1987-01-01

    One of the major problems in analyzing the short-term uranium market is the lack of a well-defined spot market price. The two primary sources of price data covering the US uranium market are the series published by the US Dept. of Energy (DOE) and by the Nuclear Exchange Corporation (NUEXCO), a private brokerage firm. Because of the differences in both definition and coverage, these two series are not directly comparable. In this study, an econometric model was developed for analyzing the interrelationship between short-term uranium price (NUEXCO exchange value), supply, demand, and future price expectations formed by market participants. The validity of this model has been demonstrated by the fact that all simulation statistics derived are highly significant. Three forecasting scenarios were developed in this study

  8. The ENSO Impact on Predicting World Cocoa Prices

    OpenAIRE

    Ubilava, David; Helmers, Claes Gustav

    2011-01-01

    Cocoa beans are produced in equatorial and sub-equatorial regions of West Africa, Southeast Asia and South America. These are also the regions most affected by El Nino Southern Oscillation (ENSO) -- a climatic anomaly affecting temperature and precipitation in many parts of the world. Thus, ENSO, has a potential of affecting cocoa production and, subsequently, prices on the world market. This study investigates the benefits of using a measure of ENSO variable in world cocoa price forecasting ...

  9. Essays in financial transmission rights pricing

    Science.gov (United States)

    Posner, Barry

    This work examines issues in the pricing of financial transmission rights in the PJM market region. The US federal government is advocating the creation of large-scale, not-for-profit regional transmission organizations to increase the efficiency of the transmission of electricity. As a non-profit entity, PJM needs to allocate excess revenues collected as congestion rents, and the participants in the transmission markets need to be able to hedge their exposure to congestion rents. For these purposes, PJM has developed an instrument known as the financial transmission right (FTR). This research, utilizing a new data set assembled by the author, looks at two aspects of the FTR market. The first chapter examines the problem of forecasting congestion in a transmission grid. In the PJM FTR system firms bid in a competitive auction for FTRs that cover a period of one month. The auctions take place in the middle of the previous month; therefore firms have to forecast congestion rents for the period two to six weeks after the auction. The common methods of forecasting congestion are either time-series models or full-information engineering studies. In this research, the author develops a forecasting system that is more economically grounded than a simple time-series model, but requires less information than an engineering model. This method is based upon the arbitrage-cost methodology, whereby congesting is calculated as the difference of two non-observable variables: the transmission price difference that would exist in the total absence of transmission capacity between two nodes, and the ability of the existing transmission to reduced that price difference. If the ability to reduce the price difference is greater than the price difference, then the cost of electricity at each node will be the same, and congestion rent will be zero. If transmission capacity limits are binding on the flow of power, then a price difference persists and congestion rents exist. Three

  10. Application of empirical mode decomposition with local linear quantile regression in financial time series forecasting.

    Science.gov (United States)

    Jaber, Abobaker M; Ismail, Mohd Tahir; Altaher, Alsaidi M

    2014-01-01

    This paper mainly forecasts the daily closing price of stock markets. We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ). We use the proposed technique, EMD-LLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposed method, in which EMD-LPQ, EMD, and Holt-Winter methods are compared. The proposed EMD-LPQ model is determined to be superior to the EMD and Holt-Winter methods in predicting the stock closing prices.

  11. Navy Mobility Fuels Forecasting System. Phase I report

    Energy Technology Data Exchange (ETDEWEB)

    Davis, R.M.; Hadder, G.R.; Singh, S.P.N.; Whittle, C.

    1985-07-01

    The Department of the Navy (DON) requires an improved capability to forecast mobility fuel availability and quality. The changing patterns in fuel availability and quality are important in planning the Navy's Mobility Fuels R and D Program. These changes come about primarily because of the decline in the quality of crude oil entering world markets as well as the shifts in refinery capabilities domestically and worldwide. The DON requested ORNL's assistance in assembling and testing a methodology for forecasting mobility fuel trends. ORNL reviewed and analyzed domestic and world oil reserve estimates, production and price trends, and recent refinery trends. Three publicly available models developed by the Department of Energy were selected as the basis of the Navy Mobility Fuels Forecasting System. The system was used to analyze the availability and quality of jet fuel (JP-5) that could be produced on the West Coast of the United States under an illustrative business-as-usual and a world oil disruption scenario in 1990. Various strategies were investigated for replacing the lost JP-5 production. This exercise, which was strictly a test case for the forecasting system, suggested that full recovery of lost fuel production could be achieved by relaxing the smoke point specifications or by increasing the refiners' gate price for the jet fuel. A more complete analysis of military mobility fuel trends is currently under way.

  12. The estimation of risk-premium implicit in oil prices

    International Nuclear Information System (INIS)

    Luis, J.B.

    2001-01-01

    The futures price can be seen as the sum of the expected value of the underlying asset price and a risk-premium. In order to disentangle these two components of the futures price, one can try to model the relationship between spot and futures prices, in order to obtain a closed expression for the risk-premium, or to use information from spot and option prices to estimate risk-aversion functions. Given the high volatility of the ratios between futures and spot prices, we opted for the latter, estimating risk-neutral and subjective probability density functions, respectively, from observed option and spot prices. looking at the prices of Brent and West Texas Intermediate light/sweet crude oil options, the obtained evidence suggests that risk-aversion is typically very low for levels near the futures prices. However, due to price volatility and, consequently, to the tails of distribution, the risk-aversion functions are badly behaved in extreme prices and futures prices do not anticipate sharp movements in oil spot prices. Therefore, futures oil prices seem to be useful in forecasting spot prices only when moderate price changes occur. (author)

  13. Air Pollution Forecasts: An Overview.

    Science.gov (United States)

    Bai, Lu; Wang, Jianzhou; Ma, Xuejiao; Lu, Haiyan

    2018-04-17

    Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.

  14. Air Pollution Forecasts: An Overview

    Directory of Open Access Journals (Sweden)

    Lu Bai

    2018-04-01

    Full Text Available Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies.

  15. Air Pollution Forecasts: An Overview

    Science.gov (United States)

    Bai, Lu; Wang, Jianzhou; Lu, Haiyan

    2018-01-01

    Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies. PMID:29673227

  16. Peering into Alberta’s Darkening Future: How Oil Prices Impact Alberta’s Royalty Revenues

    Directory of Open Access Journals (Sweden)

    Sarah Dobson

    2015-03-01

    Full Text Available The price of oil just keeps collapsing — and the fate of Alberta’s revenues is buckling with it. Going into March 2015, it seemed as if prices might have finally found a bottom, somewhere between US$48 and US$52. By the second week of March, they began falling again, to the low forties. These are prices the Alberta government had not even ventured to fathom when first putting together its forecasts for the impact of falling oil prices on the province’s finances. Come the fourth quarter of the Alberta government’s 2014/15 fiscal year, the province’s finances will begin to really feel the blow from the plunge in oil, as royalty payments dry up significantly. Come the 2015/16 fiscal year, the situation becomes even bleaker. In fact, the current fiscal year will seem pleasant compared to the next one. Due to a stronger than expected first half of the year, actual bitumen and crude oil royalties collected in Alberta from April to September 2014 exceeded estimates by $1.3 billion. That will mitigate some of the damage that the continuing slide in prices will cause by the year’s end, with the government’s third quarter update showing expected year-end crude oil and bitumen royalty revenues falling short of the budget target by $549 million. So severe has the fall in oil prices been that, in March 2015, the number of barrels of conventional oil that the government collects in royalties could plummet by up to 53,000 barrels from the 2014/15 budget forecast, declining to just 4,100 barrels per day. This suggests that prices may be nearing a point where royalty collection from conventional crude oil production is at risk of being virtually eliminated. Bitumen royalties are not faring much better. Relative to July 2014, per barrel royalties in February 2015 have potentially declined by 60 to 90 per cent. All told, the combined effect of the changing exchange rate, lower prices, and the lower royalty rates that take effect in this low-price

  17. Artificial neural networks applied to the prediction of spot prices in the market of electric energy

    International Nuclear Information System (INIS)

    Rodrigues, Alcantaro Lemes; Grimoni, Jose Aquiles Baesso

    2010-01-01

    The commercialization of electricity in Brazil as well as in the world has undergone several changes over the past 20 years. In order to achieve an economic balance between supply and demand of the good called electricity, stakeholders in this market follow both rules set by society (government, companies and consumers) and set by the laws of nature (hydrology). To deal with such complex issues, various studies have been conducted in the area of computational heuristics. This work aims to develop a software to forecast spot market prices in using artificial neural networks (ANN). ANNs are widely used in various applications especially in computational heuristics, where non-linear systems have computational challenges difficult to overcome because of the effect named 'curse of dimensionality'. This effect is due to the fact that the current computational power is not enough to handle problems with such a high combination of variables. The challenge of forecasting prices depends on factors such as: (a) foresee the demand evolution (electric load); (b) the forecast of supply (reservoirs, hydrology and climate), capacity factor; and (c) the balance of the economy (pricing, auctions, foreign markets influence, economic policy, government budget and government policy). These factors are considered be used in the forecasting model for spot market prices and the results of its effectiveness are tested and huge presented. (author)

  18. Forecasting Interest Rates Using Geostatistical Techniques

    Directory of Open Access Journals (Sweden)

    Giuseppe Arbia

    2015-11-01

    Full Text Available Geostatistical spatial models are widely used in many applied fields to forecast data observed on continuous three-dimensional surfaces. We propose to extend their use to finance and, in particular, to forecasting yield curves. We present the results of an empirical application where we apply the proposed method to forecast Euro Zero Rates (2003–2014 using the Ordinary Kriging method based on the anisotropic variogram. Furthermore, a comparison with other recent methods for forecasting yield curves is proposed. The results show that the model is characterized by good levels of predictions’ accuracy and it is competitive with the other forecasting models considered.

  19. Price Intransparency, Consumer Decision Making and European Consumer Law

    OpenAIRE

    Boom, Willem

    2011-01-01

    textabstractPrice comparison is a basic element of competition. For comparison to work, at least prices need to be transparent. Moreover, price is usually a focal point in consumer thinking and deciding on transactions. Hence, obfuscating prices can be detrimental to consumers. Therefore, it is vital for policymakers to know how transparent pricing is in reality. Commercial practices involving price intransparency can be detrimental to consumer decision making and may be associated with marke...

  20. Methodology for forecasting in the Swedish–Norwegian market for el-certificates

    International Nuclear Information System (INIS)

    Wolfgang, Ove; Jaehnert, Stefan; Mo, Birger

    2015-01-01

    In this paper we describe a novel methodology for forecasting in the Swedish–Norwegian el-certificate market, which is a variant of a tradable green certificate scheme. For the forecasting, the el-certificate market is integrated in the electricity-market model EMPS, which has weekly to hourly time-step length, whereas the planning horizon can be several years. Strategies for the certificate inventory are calculated by stochastic dynamic programming, whereas penalty-rates for non-compliance during the annual settlement of certificates are determined endogenously. In the paper the methodology is described, and we show the performance of the model under different cases that can occur in the el-certificate market. The general results correspond to theoretical findings in previous studies for tradable green certificate markets, in particular that price-scenarios spread out in such a way that the unconditional expected value of certificates is relatively stable throughout the planning period. In addition the presented methodologies allows to assess the actual dynamics of the certificate price due to climatic uncertainty. Finally, special cases are indentified where the certificate price becomes excessively high respectively zero, due the design-specific dynamics of the penalty rate. - Highlights: • A method for forecasting in the Swedish–Norwegian el-certificate market is proposed. • The developed model integrates the el-certificate and the power market. • Banking of certificates and the endogenously calculated penalty rate are included. • The certificate value is calculated using Stochastic-Dynamic-Programming. • Price dynamics due to climatic weather uncertainties are assessed and illustrated

  1. Forecasting oilfield economic performance

    International Nuclear Information System (INIS)

    Bradley, M.E.; Wood, A.R.O.

    1994-01-01

    This paper presents a general method for forecasting oilfield economic performance that integrates cost data with operational, reservoir, and financial information. Practices are developed for determining economic limits for an oil field and its components. The economic limits of marginal wells and the role of underground competition receive special attention. Also examined is the influence of oil prices on operating costs. Examples illustrate application of these concepts. Categorization of costs for historical tracking and projections is recommended

  2. Data Mining on Romanian Stock Market Using Neural Networks for Price Prediction

    Directory of Open Access Journals (Sweden)

    Magdalena Daniela NEMES

    2013-01-01

    Full Text Available Predicting future prices by using time series forecasting models has become a relevant trading strategy for most stock market players. Intuition and speculation are no longer reliable as many new trading strategies based on artificial intelligence emerge. Data mining represents a good source of information, as it ensures data processing in a convenient manner. Neural networks are considered useful prediction models when designing forecasting strategies. In this paper we present a series of neural networks designed for stock exchange rates forecasting applied on three Romanian stocks traded on the Bucharest Stock Exchange (BSE. A multistep ahead strategy was used in order to predict short-time price fluctuations. Later, the findings of our study can be integrated with an intelligent multi-agent system model which uses data mining and data stream processing techniques for helping users in the decision making process of buying or selling stocks.

  3. Who Is the Big Spender? Price Index Effects in Comparisons of Educational Expenditures between Countries and Over Time

    Science.gov (United States)

    Arneberg, Marie; Bowitz, Einar

    2006-01-01

    International comparisons of data on expenditure on education use purchasing power parities for currency conversion and adjustment for price differences between countries to allow for volume comparisons. The resulting indicators are commonly interpreted as differences between countries in input volumes to the education sector-teachers, materials,…

  4. Comparing Predictive Accuracy under Long Memory - With an Application to Volatility Forecasting

    DEFF Research Database (Denmark)

    Kruse, Robinson; Leschinski, Christian; Will, Michael

    This paper extends the popular Diebold-Mariano test to situations when the forecast error loss differential exhibits long memory. It is shown that this situation can arise frequently, since long memory can be transmitted from forecasts and the forecast objective to forecast error loss differentials....... The nature of this transmission mainly depends on the (un)biasedness of the forecasts and whether the involved series share common long memory. Further results show that the conventional Diebold-Mariano test is invalidated under these circumstances. Robust statistics based on a memory and autocorrelation...... extensions of the heterogeneous autoregressive model. While we find that forecasts improve significantly if jumps in the log-price process are considered separately from continuous components, improvements achieved by the inclusion of implied volatility turn out to be insignificant in most situations....

  5. Pricing transmission services

    International Nuclear Information System (INIS)

    Haaden, E.

    1995-01-01

    The price structure for transmission of electric power through the main lines in Sweden is analyzed. After deregulation of the electricity market, the main transmission lines are owned by a separate national company, with no interests from the power producers. Comparisons are made to ideal marginal price structures. 6 refs

  6. Natural gas pricing policies in Southeast Asia

    International Nuclear Information System (INIS)

    Pacudan, R.B.

    1998-01-01

    The very dynamic economies of Southeast Asia have recently been experiencing a rapid increase in energy demand. Parallel to this development, there has been an increase in the utilization of indigenous natural gas resources. This article reviews gas-pricing policies in the region, which partly explain the rise in gas utilization. Although diverse, energy pricing policies in Southeast Asia address the common objective of enhancing domestic gas production and utilization. The article concludes that a more rational gas-pricing policy framework is emerging in the region. In global terms, gas pricing in the region tends to converge in a market-related framework, despite the many different pricing objectives of individual countries, and the predominance of non-economic pricing objectives in certain countries (especially gas-rich nations). Specifically, governments have been flexible enough to follow global trends and initiate changes in contractual agreements (pricing and profit-sharing), giving oil companies more favourable terms, and encouraging continued private investment in gas development. At the same time, promotional pricing has also been used to increase utilization of gas, through set prices and adjusted taxes achieving a lower price level compared to substitute fuels. For an efficient gas-pricing mechanism, refinements in the pricing framework should be undertaken, as demand for gas approaches existing and/or forecast production capacities. (author)

  7. Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price

    Directory of Open Access Journals (Sweden)

    Kaijian He

    2016-04-01

    Full Text Available Recent empirical studies reveal evidence of the co-existence of heterogeneous data characteristics distinguishable by time scale in the movement crude oil prices. In this paper we propose a new multivariate Empirical Mode Decomposition (EMD-based model to take advantage of these heterogeneous characteristics of the price movement and model them in the crude oil markets. Empirical studies in benchmark crude oil markets confirm that more diverse heterogeneous data characteristics can be revealed and modeled in the projected time delayed domain. The proposed model demonstrates the superior performance compared to the benchmark models.

  8. 76 FR 67037 - Energy Conservation Program: Energy Conservation Standards for Residential Furnaces and...

    Science.gov (United States)

    2011-10-31

    ..., including trends derived using the experience curve approach. AGA and APGA stated that DOE's experience... and APGA using DOE's model, DOE has concluded that its model (which remains unchallenged in terms of... forecast has been trending downward over the last several years; (5) DOE's use of the AEO 2010 Reference...

  9. EU pharmaceutical expenditure forecast.

    Science.gov (United States)

    Urbinati, Duccio; Rémuzat, Cécile; Kornfeld, Åsa; Vataire, Anne-Lise; Cetinsoy, Laurent; Aballéa, Samuel; Mzoughi, Olfa; Toumi, Mondher

    2014-01-01

    With constant incentives for healthcare payers to contain their pharmaceutical budgets, forecasting has become critically important. Some countries have, for instance, developed pharmaceutical horizon scanning units. The objective of this project was to build a model to assess the net effect of the entrance of new patented medicinal products versus medicinal products going off-patent, with a defined forecast horizon, on selected European Union (EU) Member States' pharmaceutical budgets. This model took into account population ageing, as well as current and future country-specific pricing, reimbursement, and market access policies (the project was performed for the European Commission; see http://ec.europa.eu/health/healthcare/key_documents/index_en.htm). In order to have a representative heterogeneity of EU Member States, the following countries were selected for the analysis: France, Germany, Greece, Hungary, Poland, Portugal, and the United Kingdom. A forecasting period of 5 years (2012-2016) was chosen to assess the net pharmaceutical budget impact. A model for generics and biosimilars was developed for each country. The model estimated a separate and combined effect of the direct and indirect impacts of the patent cliff. A second model, estimating the sales development and the risk of development failure, was developed for new drugs. New drugs were reviewed individually to assess their clinical potential and translate it into commercial potential. The forecast was carried out according to three perspectives (healthcare public payer, society, and manufacturer), and several types of distribution chains (retail, hospital, and combined retail and hospital). Probabilistic and deterministic sensitivity analyses were carried out. According to the model, all countries experienced drug budget reductions except Poland (+€41 million). Savings were expected to be the highest in the United Kingdom (-€9,367 million), France (-€5,589 million), and, far behind them

  10. Forecasting Fire Insurance Loss Ratio in Misr Insurance Company

    Directory of Open Access Journals (Sweden)

    Tarek TAHA

    2017-06-01

    Full Text Available Loss ratio is one of the most important indicator that has many strategic decisions applications, such as pricing, underwriting, investment, reinsurance and reserving decisions. It serves as an early warning of financial solvency of insurance companies and it can be judged on the strength of the financial position of these companies. The aim of this study is to identify the reliable time series-forecasting model to forecast loss ratio estimates of fire segment in Misr insurance company. Box-Jenkins Analysis is applied on actual reported loss ratios data for Misr insurance company for the period 1980/1981– 2013/2014. The study concludes that the best forecasting model is ARMA(1,1.

  11. Forecasting Dry Bulk Freight Index with Improved SVM

    Directory of Open Access Journals (Sweden)

    Qianqian Han

    2014-01-01

    Full Text Available An improved SVM model is presented to forecast dry bulk freight index (BDI in this paper, which is a powerful tool for operators and investors to manage the market trend and avoid price risking shipping industry. The BDI is influenced by many factors, especially the random incidents in dry bulk market, inducing the difficulty in forecasting of BDI. Therefore, to eliminate the impact of random incidents in dry bulk market, wavelet transform is adopted to denoise the BDI data series. Hence, the combined model of wavelet transform and support vector machine is developed to forecast BDI in this paper. Lastly, the BDI data in 2005 to 2012 are presented to test the proposed model. The 84 prior consecutive monthly BDI data are the inputs of the model, and the last 12 monthly BDI data are the outputs of model. The parameters of the model are optimized by genetic algorithm and the final model is conformed through SVM training. This paper compares the forecasting result of proposed method and three other forecasting methods. The result shows that the proposed method has higher accuracy and could be used to forecast the short-term trend of the BDI.

  12. Forecasting winds over nuclear power plants statistics

    International Nuclear Information System (INIS)

    Marais, Ch.

    1997-01-01

    In the event of an accident at nuclear power plant, it is essential to forecast the wind velocity at the level where the efflux occurs (about 100 m). At present meteorologists refine the wind forecast from the coarse grid of numerical weather prediction (NWP) models. The purpose of this study is to improve the forecasts by developing a statistical adaptation method which corrects the NWP forecasts by using statistical comparisons between wind forecasts and observations. The Multiple Linear Regression method is used here to forecast the 100 m wind at 12 and 24 hours range for three Electricite de France (EDF) sites. It turns out that this approach gives better forecasts than the NWP model alone and is worthy of operational use. (author)

  13. Price trends and project viability

    International Nuclear Information System (INIS)

    Olsen, W.H.

    1999-01-01

    The paper discusses some of the remarkable changes that have occurred in the oil and gas industry over about the past 25 years. In the past ten years alone, technology and politics have brought new ways of working together and the recovery of resources once thought of as impossible. Cooperation appears to be a key word. The paper was presented under the sub-headings of the environment, global energy outlook, technological drivers and the challenge ahead. The current low price of oil will inevitably slow down exploration and field development but the author remains optimistic despite the tough challenges. The paper contains many diagrams relating to production, costs, efficiency, exploration, reserves, price forecasts and exploration technology

  14. Are internet prices sticky?

    OpenAIRE

    Lünnemann, Patrick; Wintr, Ladislav

    2006-01-01

    This paper studies the behaviour of Internet prices. It compares price rigidities on the Internet and in traditional brick-and-mortar stores and provides a cross-country perspective. The data set covers a broad range of items typically sold over the Internet. It includes more than 5 million daily price quotes downloaded from price comparison web sites in France, Germany, Italy, the UK and the US. The following results emerge from our analysis. First, and contrary to the recent findings for co...

  15. A mathematical model for stock price forecasting | Ogwuche | West ...

    African Journals Online (AJOL)

    ) and the covariance (the volatility) of the change were computed leading to the formulation of the system of linear stochastic differential equations. To fit data to the model, changes in the prices of the stocks were studied for an average of 30 ...

  16. Bayesian Option Pricing Using Mixed Normal Heteroskedasticity Models

    DEFF Research Database (Denmark)

    Rombouts, Jeroen V.K.; Stentoft, Lars Peter

    While stochastic volatility models improve on the option pricing error when compared to the Black-Scholes-Merton model, mispricings remain. This paper uses mixed normal heteroskedasticity models to price options. Our model allows for significant negative skewness and time varying higher order...... moments of the risk neutral distribution. Parameter inference using Gibbs sampling is explained and we detail how to compute risk neutral predictive densities taking into account parameter uncertainty. When forecasting out-of-sample options on the S&P 500 index, substantial improvements are found compared...

  17. Perceived benefits of adopting Standard – Based pricing mechanism for mechanical and electrical services installations

    Directory of Open Access Journals (Sweden)

    Ganiyu Amuda Yusuf

    2014-06-01

    Full Text Available Cost is an important measure of project success and clients will expect a reliable forecast at the early stage of construction projects to inform their business decision. This study was undertaken to investigate the current practices in managing cost of mechanical and electrical (M&E services in buildings. The perceptions of practitioners on the benefits of adopting Standard – Based Pricing Mechanism for M&E services as used for building fabrics and finishes was ascertained. The methodology adopted for the study was semi – structure interview and questionnaire survey.  Inferential statistics technique was used to analyse the data collected. The results revealed that, M&E services tender documents are often based on lump sum contract. Practitioners are of the opinion that the adoption of Standard – Based Pricing Mechanism (SBPM could enhance the quality of M&E services price forecast; ensure active post contract cost monitoring and control; encourage collaborative working relationship; enhance efficient whole life cycle cost management; improve risk management and facilitate efficient tendering process. The study suggested the development of local Standard Method of Measurement for M&E services and proposed strategies to facilitate the adoption of SBPM as basis for forecasting contract price of mechanical and electrical services in buildings.

  18. A comparison of alternative methods for measuring cigarette prices.

    Science.gov (United States)

    Chaloupka, Frank J; Tauras, John A; Strasser, Julia H; Willis, Gordon; Gibson, James T; Hartman, Anne M

    2015-05-01

    Government agencies, public health organisations and tobacco control researchers rely on accurate estimates of cigarette prices for a variety of purposes. Since the 1950s, the Tax Burden on Tobacco (TBOT) has served as the most widely used source of this price data despite its limitations. This paper compares the prices and collection methods of the TBOT retail-based data and the 2003 and 2006/2007 waves of the population-based Tobacco Use Supplement to the Current Population Survey (TUS-CPS). From the TUS-CPS, we constructed multiple state-level measures of cigarette prices, including weighted average prices per pack (based on average prices for single-pack purchases and average prices for carton purchases) and compared these with the weighted average price data reported in the TBOT. We also constructed several measures of tax avoidance from the TUS-CPS self-reported data. For the 2003 wave, the average TUS-CPS price was 71 cents per pack less than the average TBOT price; for the 2006/2007 wave, the difference was 47 cents. TUS-CPS and TBOT prices were also significantly different at the state level. However, these differences varied widely by state due to tax avoidance opportunities, such as cross-border purchasing. The TUS-CPS can be used to construct valid measures of cigarette prices. Unlike the TBOT, the TUS-CPS captures the effect of price-reducing marketing strategies, as well as tax avoidance practices and non-traditional types of purchasing. Thus, self-reported data like TUS-CPS appear to have advantages over TBOT in estimating the 'real' price that smokers face. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  19. DOES VOLATILITY IN CRUDE OIL PRICE PRECIPITATE MACROECONOMIC PERFORMANCE IN NIGERIA?

    Directory of Open Access Journals (Sweden)

    Joseph Ayoola Omojolaibi

    2013-01-01

    Full Text Available This study examines the effects of crude oil price changes on economic activity in an oil dependent economy-Nigeria. A small open economy structural vector autoregressive (SVAR technique is employed to study the macroeconomic dynamics of domestic price level, economic output, money supply and oil price in Nigeria. The sample covers the data from 1985:q1 to 2010:q4. The Impulse Response Functions (IRFs and the Forecast Error Variance Decompositions (FEVDs results suggest that domestic policies, instead of oil-boom should be blamed for inflation. Also, oil price variations are driven mostly by oil shocks, however, domestic shocks are responsible for a reasonable portion of oil price variations.

  20. Comparison of Statistical Post-Processing Methods for Probabilistic Wind Speed Forecasting

    Science.gov (United States)

    Han, Keunhee; Choi, JunTae; Kim, Chansoo

    2018-02-01

    In this study, the statistical post-processing methods that include bias-corrected and probabilistic forecasts of wind speed measured in PyeongChang, which is scheduled to host the 2018 Winter Olympics, are compared and analyzed to provide more accurate weather information. The six post-processing methods used in this study are as follows: mean bias-corrected forecast, mean and variance bias-corrected forecast, decaying averaging forecast, mean absolute bias-corrected forecast, and the alternative implementations of ensemble model output statistics (EMOS) and Bayesian model averaging (BMA) models, which are EMOS and BMA exchangeable models by assuming exchangeable ensemble members and simplified version of EMOS and BMA models. Observations for wind speed were obtained from the 26 stations in PyeongChang and 51 ensemble member forecasts derived from the European Centre for Medium-Range Weather Forecasts (ECMWF Directorate, 2012) that were obtained between 1 May 2013 and 18 March 2016. Prior to applying the post-processing methods, reliability analysis was conducted by using rank histograms to identify the statistical consistency of ensemble forecast and corresponding observations. Based on the results of our study, we found that the prediction skills of probabilistic forecasts of EMOS and BMA models were superior to the biascorrected forecasts in terms of deterministic prediction, whereas in probabilistic prediction, BMA models showed better prediction skill than EMOS. Even though the simplified version of BMA model exhibited best prediction skill among the mentioned six methods, the results showed that the differences of prediction skills between the versions of EMOS and BMA were negligible.

  1. Sustainable energy prices and growth. Comparing macroeconomic and backcasting scenarios

    International Nuclear Information System (INIS)

    Ahlroth, Sofia; Hoejer, Mattias

    2007-01-01

    How do results from the sustainability research world of backcasting relate to the macroeconomic scenarios used for policy evaluation and planning? The answer is that they do not, mostly - they come from different scientific traditions and are not used in the same contexts. Yet they often deal with the same issues. We believe that much can be gained by bringing the two systems of thinking together. This paper is a first attempt to do so, by making qualitative comparisons between different scenarios and highlighting benefits and limitations to each of them. Why are the pictures we get of the energy future so different if we use a macroeconomic model from when using a backcasting approach based on sustainable energy use? It is evident that the methods for producing those two kinds of scenarios differ a lot, but the main reason behind the different results are found in the starting points rather than in the methods. Baseline assumptions are quite different, as well as the interpretations and importance attached to signals about the future. In this paper, it is discussed how those two types of scenarios differ and how they approach issues such as energy prices and growth. The discussion is based on a comparison between Swedish economic and sustainability scenarios. The economic scenarios aim at being forecasts of the future and are used as decision support for long-term policies. But are the assumptions in the economic scenarios reasonable? The sustainability scenarios are explicitly normative backcasting scenarios. They do not take the issue of growth and consumption fully into account. Could they be developed in this respect? The comparison between the scenarios is also used to look closer at the issue of energy prices in a society with sustainable energy use. One of the questions raised is if a low energy society calls for high energy prices. Moreover, the effects of tradable permits versus energy taxes is analysed in the context of how energy use could be kept low

  2. Oil Market and Prices Prospects for 2014

    Directory of Open Access Journals (Sweden)

    Mariana Papatulica

    2013-10-01

    Full Text Available The international crude oil prices started the year 2014 within parameters comparable to those of the precedent year: WTI (USA recorded 92 $/barrel, on the American spot market, considered a minimum value for the last 5 weeks, while Brent (Great Britain had a more stable evolution, on the spot Rotterdam market, staying around a value of 107,50 $/barrel. Despite analysts’ forecasts, which during the last 3 years staked on a lower oil price, as a consequence of the spectacular increase in non-OPEC oil production, namely of shale oil, the international oil price, namely that of Brent, closed each of the last 3 years around the same level, of 108 $/barrel. As for 2014, the great majority of oil analysts estimates again a decline of oil prices, as a result of a significant rise of oil offer globally, which will greatly surpass the demand rise.

  3. Comment on the UPS (and past and future downs) of the oil price

    International Nuclear Information System (INIS)

    Walde, T.

    2000-01-01

    Crude oil has been rising to levels over 35 US$ per barrel from the very low prices of early 1999 - close to 10 $. In real, inflation-adjusted terms, this leaves it still at a third of the prices prevailing during the peak of 1981. This trend has been accelerated currently by short-term influences market factors. Who could have forecasted such price evolution by January 1999, when crude prices were collapsing, following the series of financial crises in Asia, Russia and Brazil? The current oil price surge has been breaking once again every 'crystal ball' and mathematical model designed to predict short-or-long-term oil price evolution - foremost the models used by the international oil companies and their advisers, chastened by the embarrassment of earlier optimism. Old ghosts that used to scare the world during the energy crisis of the 1970's and 1980's are waking up again. Traditional forces that have since 1985 and throughout the whole 1990's given economic rationality to crude price behaviour, seem to be losing ground and are unable to restore a more sustainable level of oil prices. Political forces, silent since the price collapse of 1985/86, have again raised their head and bringing to the fore historic contradictions and problems never solved. This paper covers this new reality. We are too cautions to dare to forecast, but rather identify factors that have to be considered in speculating about the future evolution of oil prices. The changing weight of those factors will continue to influence the future of the oil price - without much interest (apart from the producers) when low but again greatly debated when, as now, up again. (authors)

  4. Which way the natural gas price. An attempt to predict the direction of natural gas spot price movements using trader positions

    International Nuclear Information System (INIS)

    Buchanan, W.K.; Hodges, P.; Theis, J.

    2001-01-01

    This research provides a method of predicting direction of spot price movements in the natural gas market for the month succeeding from market participants positions in the futures market. Cumby and Modest (Cumby, R.E., Modest, D.M., 1987. Testing for market timing ability: a framework for forecast evaluation. Journal of Financial Economics 19, 169-189) provide the backdrop for analyzing the futures market positions of large hedgers and speculators to arrive at conclusions of market price movements in the spot market. This methodology is suggested as a means for municipalities entering the natural gas market to improve upon their ordering of quantities of gas for the ensuing months in order to take advantage of possibly foreseeable price trends

  5. The Dynamics of Food Price Convergence in Ethiopia

    African Journals Online (AJOL)

    Optiplex 7010 Pro

    wholesale and retail prices at supply and destination markers in multivariate forecasting ... analytical methods in Part 3, and presentation and discussion of empirical findings in Part 4. ... maximum eigenvalue and trace test statistics are based on a pure unit-root .... (2009) used a bivariate instrumental variables method to.

  6. A method for the correction of the feed-in tariff price for cogeneration based on a comparison between Croatia and EU Member States

    International Nuclear Information System (INIS)

    Uran, Vedran; Krajcar, Slavko

    2009-01-01

    The European Commission has adopted Directive 2004/8/EC on the promotion of cogeneration, which the EU Member States, as well as candidates including Croatia, were obliged to accept. Among other terms and conditions, the Directive requires certain support mechanisms, such as feed-in tariff prices and premiums added to market electricity prices. In this paper, the cost effectiveness of selling electricity at the feed-in tariff prices in the selected EU Member States is compared to selling it on the European electricity market, with or without premiums. The results of this comparison will indicate whether correction of the Croatian feed-in tariff price to a higher value would be justified. The cost effectiveness ratio of a cogeneration unit upgraded with mean reverting and jump diffusion processes is used for comparison. At the end of this paper, a method is suggested for the correction of feed-in tariff prices, with examples of corrected prices for the years 2008 and 2009. Such corrections have been proven to be justified and are compared to the feed-in tariff prices in most of the selected EU Member States.

  7. A national econometric forecasting model of the dental sector.

    Science.gov (United States)

    Feldstein, P J; Roehrig, C S

    1980-01-01

    The Econometric Model of the the Dental Sector forecasts a broad range of dental sector variables, including dental care prices; the amount of care produced and consumed; employment of hygienists, dental assistants, and clericals; hours worked by dentists; dental incomes; and number of dentists. These forecasts are based upon values specified by the user for the various factors which help determine the supply an demand for dental care, such as the size of the population, per capita income, the proportion of the population covered by private dental insurance, the cost of hiring clericals and dental assistants, and relevant government policies. In a test of its reliability, the model forecast dental sector behavior quite accurately for the period 1971 through 1977. PMID:7461974

  8. Comparison on the forecast model of landfill surface

    International Nuclear Information System (INIS)

    Zhou Xiaozhi; Sang Shuxun; Cao Liwen; Ji Xiaoyan

    2010-01-01

    Using four large-scale simulated landfill equipments, indoor parallel simulation landfill experiment was carried out. By monitoring the cumulative settlement of MSW, comparable researches indicate the actual effects of 'empirical model' and 'biodegradation model' on landfill surface settlement forecast, and the optimization measures are proposed on the basis of model defects analysis. Research leaded to following results: To the short-term prediction of MSW settlement, two types of models all have satisfactory predictive validity. When performing medium and long-term prediction, 'empirical model' predicted a significant deviation from the actual, and the forecasting error of 'biodegradation model' is also gradually enlarge with the extending forecast period. For optimizing these two types of model, long-term surface settlement monitoring is fundamental method, and constantly modify the model parameters is the key according to the dynamic monitoring data. (authors)

  9. The European used-car market at a glance: Hedonic resale price valuation in automotive leasing industry

    OpenAIRE

    Sylvain M. Prado

    2009-01-01

    In the leasing industry, the risk of loss on sales at the end of the contract term, as well as pricing are critically impacted by the forecasted resale price of the asset (residual value). We apply the Hedonic methodology to European auto lease portfolios, in order to estimate the resale price distribution. The Hedonic approach estimates the price of a good through the valuation of its attributes. Following a discussion on Hedonic prices, we propose an operational model for the automobile res...

  10. Study on Competitive Exporting Price-forecast of the SMART in the U.S

    International Nuclear Information System (INIS)

    Kim, In Su; Kim, Tae Ryong

    2014-01-01

    In line with this, the U.S. has a renewed interest in SMRs rather than large reactors. Nothing, however, has been implemented yet. The only SMRs under construction are in Russia: the first floating nuclear plants. For the most part, the primary candidates to be the first land-based counterparts of Russia's are the SMART (System integrated Modular Advanced Reactor) reactors. The Korean SMART has been developed and licensed for standard design. In addition, the SMART reactor may be suited to countries, which have a small grid capacity, low population density, and decentralization power system such as the U.S. Therefore, the purpose of this paper is to develop a target price for the SMR market opportunities in the U.S., competing against the CCGT (Combined Cycle Gas Turbine) which is currently a very attractive option for generating due to the shale innovation. Even though detailed cost estimates are not available, target price can be derived based on generally determining market price. This paper demonstrates the target exporting price of the SMART in the U.S. ranging from 3,091 - 4,011$/kWe depending on the scaling factor and carbon tax, assuming that discount rates are fixed. This value could be a target cost of construction, developing the U.S market whose demand of the SMART is potentially 4 units 2015 - 2035. Sensitivity analysis shows that the price goes up in proportion to the gas price, the capacity factor of the SMART, the overnight cost of CCGT, etc. More than anything else, this study reveals that carbon tax does not have much influence on the target price compared with those listed above. On the other hand, the price goes up in inverse proportion to the interest of the SMART, the capacity factor of CCGT, O and M costs of the SMART, and so on. For the price competitiveness, construction cost should first be reduced because construction cost is the largest component of LCOE as well as the effect of interest rate is the most sensitive for target price

  11. Study on Competitive Exporting Price-forecast of the SMART in the U.S

    Energy Technology Data Exchange (ETDEWEB)

    Kim, In Su; Kim, Tae Ryong [KEPCO International Nuclear Graduate School, Ulsan (Korea, Republic of)

    2014-10-15

    In line with this, the U.S. has a renewed interest in SMRs rather than large reactors. Nothing, however, has been implemented yet. The only SMRs under construction are in Russia: the first floating nuclear plants. For the most part, the primary candidates to be the first land-based counterparts of Russia's are the SMART (System integrated Modular Advanced Reactor) reactors. The Korean SMART has been developed and licensed for standard design. In addition, the SMART reactor may be suited to countries, which have a small grid capacity, low population density, and decentralization power system such as the U.S. Therefore, the purpose of this paper is to develop a target price for the SMR market opportunities in the U.S., competing against the CCGT (Combined Cycle Gas Turbine) which is currently a very attractive option for generating due to the shale innovation. Even though detailed cost estimates are not available, target price can be derived based on generally determining market price. This paper demonstrates the target exporting price of the SMART in the U.S. ranging from 3,091 - 4,011$/kWe depending on the scaling factor and carbon tax, assuming that discount rates are fixed. This value could be a target cost of construction, developing the U.S market whose demand of the SMART is potentially 4 units 2015 - 2035. Sensitivity analysis shows that the price goes up in proportion to the gas price, the capacity factor of the SMART, the overnight cost of CCGT, etc. More than anything else, this study reveals that carbon tax does not have much influence on the target price compared with those listed above. On the other hand, the price goes up in inverse proportion to the interest of the SMART, the capacity factor of CCGT, O and M costs of the SMART, and so on. For the price competitiveness, construction cost should first be reduced because construction cost is the largest component of LCOE as well as the effect of interest rate is the most sensitive for target price

  12. On Volatility Swaps for Stock Market Forecast: Application Example CAC 40 French Index

    Directory of Open Access Journals (Sweden)

    Halim Zeghdoudi

    2014-01-01

    Full Text Available This paper focuses on the pricing of variance and volatility swaps under Heston model (1993. To this end, we apply this model to the empirical financial data: CAC 40 French Index. More precisely, we make an application example for stock market forecast: CAC 40 French Index to price swap on the volatility using GARCH(1,1 model.

  13. Estimating temporary and permanent stock price innovations on Croatian capital market

    Directory of Open Access Journals (Sweden)

    Tihana Škrinjarić

    2014-03-01

    Full Text Available This paper evaluates the size and duration of temporary and permanent stock price innovations on Croatian capital market in the structural VAR (vector autoregression framework with Blanchard and Quah (1989 decomposition. The purpose is to identify the effects of temporary price innovations in order to determine to which extent future stock prices can be predicted. Temporary components present in stock prices are explained throughout the mean-reversion hypothesis. This means that stock prices deviate from the fundamental values, but they will revert to their mean. In that way, to some extent, it is possible to predict future price movements. The results show that for the observed period from January 2000 to September 2013, temporary innovations account for only 2.62% of price variability over a two-year horizon. This means that forecasting the future movements of stock prices on Zagreb Stock Exchange is a difficult task.

  14. Comparison of Adaline and Multiple Linear Regression Methods for Rainfall Forecasting

    Science.gov (United States)

    Sutawinaya, IP; Astawa, INGA; Hariyanti, NKD

    2018-01-01

    Heavy rainfall can cause disaster, therefore need a forecast to predict rainfall intensity. Main factor that cause flooding is there is a high rainfall intensity and it makes the river become overcapacity. This will cause flooding around the area. Rainfall factor is a dynamic factor, so rainfall is very interesting to be studied. In order to support the rainfall forecasting, there are methods that can be used from Artificial Intelligence (AI) to statistic. In this research, we used Adaline for AI method and Regression for statistic method. The more accurate forecast result shows the method that used is good for forecasting the rainfall. Through those methods, we expected which is the best method for rainfall forecasting here.

  15. Price comparison of high-cost originator medicines in European countries.

    Science.gov (United States)

    Vogler, Sabine; Zimmermann, Nina; Babar, Zaheer-Ud-Din

    2017-04-01

    In recent years, high-cost medicines have increasingly been challenging the public health budget in all countries including high-income economies. In this context, this study aims to survey, analyze and compare prices of medicines that likely contribute to high expenditure for the public payers in high-income countries. We chose the following 16 European countries: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, the Netherlands, Portugal, Sweden, Slovakia, Spain and United Kingdom. The ex-factory price data of 30 medicines in these countries were collected in national databases accessible through the Pharmaceutical Price Information (PPI) service of Gesundheit Österreich GmbH (Austrian Public Health Institute). The ex-factory prices (median) per unit (e.g. per tablet, vial) ranged from 10.67 cent (levodopa + decarboxylase inhibitor) to 17,000 euro (ipilimumab). A total of 53% of the medicines surveyed had a unit ex-factory price (median) above 200 Euro. For two thirds of the medicines, price differences between the highest-priced country and lowest-priced country ranged between 25 and 100%; the remaining medicines, mainly low-priced medicines, had higher price differential, up to 251%. Medicines with unit prices of a few euros or less were medicines for the treatment of diseases in the nervous system (anti-depressants, medicines to treat Parkinson and for the management of neuropathic pain), of obstructive airway diseases and cardio-vascular medicines (lipid modifying agents). High-priced medicines were particularly cancer medicines. Medicine prices of Greece, Hungary, Slovakia and UK were frequently at the lower end, German and Swedish, as well as Danish and Irish prices at the upper end. For high-priced medicines, actual paid prices are likely to be lower due to confidential discounts and similar funding arrangements between industry and public payers. Pricing authorities refer to the higher undiscounted prices when they use

  16. Inflow forecasting at BPA

    Energy Technology Data Exchange (ETDEWEB)

    McManamon, A. [Bonneville Power Administration, Portland, OR (United States)

    2007-07-01

    The Columbia River Power System operates with consideration for flood control, endangered species, navigation, irrigation, water supply, recreation, other fish and wildlife concerns and power production. The Bonneville Power Association (BPA) located in Portland, Oregon is responsible for 35-40 per cent of the power consumed within the region. This presentation discussed inflow power concerns at BPA. The presentation illustrated elevational relief of projects; annual and daily variability; the hydrologic cycle; national river service weather forecasting service (NRSWFS); components of NRSWFS; and hydrologic forecast locations. Project operations and inventory were included along with a comparison of the 71-year average unregulated flow with regulated flow at the Dalles. Consistency between short-term and long-term forecasts and long-term streamflow forecasts were also illustrated in graphical format. The presentation also discussed the issue of reducing model and parameter uncertainty; reducing initial conditions uncertainty; snow updating; and reducing meteorological uncertainty. tabs., figs.

  17. Forecasting volatility for options valuation

    International Nuclear Information System (INIS)

    Belaifa, M.; Morimune, K.

    2006-01-01

    The petroleum sector plays a neuralgic role in the basement of world economies, and market actors (producers, intermediates, as well as consumers) are continuously subjected to the dynamics of unstable oil market. Huge amounts are being invested along the production chain to make one barrel of crude oil available to the end user. Adding to that are the effect of geopolitical dynamics as well as geological risks as expressed in terms of low chances of successful discoveries. In addition, fiscal regimes and regulations, technology and environmental concerns are also among some of the major factors that contribute to the substantial risk in the oil industry and render the market structure vulnerable to crises. The management of these vulnerabilities require modern tools to reduce risk to a certain level, which unfortunately is a non-zero value. The aim of this paper is, therefore, to provide a modern technique to capture the oil price stochastic volatility that can be implemented to value the exposure of an investor, a company, a corporate or a Government. The paper first analyses the regional dependence on oil prices, through a historical perspective and then looks at the evolution of pricing environment since the large price jumps of the 1970s. The main causes of oil prices volatility are treated in the third part of the paper. The rest of the article deals with volatility models and forecasts used in risk management, with an implication for pricing derivatives. (author)

  18. Operational hydrological forecasting in Bavaria. Part I: Forecast uncertainty

    Science.gov (United States)

    Ehret, U.; Vogelbacher, A.; Moritz, K.; Laurent, S.; Meyer, I.; Haag, I.

    2009-04-01

    In Bavaria, operational flood forecasting has been established since the disastrous flood of 1999. Nowadays, forecasts based on rainfall information from about 700 raingauges and 600 rivergauges are calculated and issued for nearly 100 rivergauges. With the added experience of the 2002 and 2005 floods, awareness grew that the standard deterministic forecast, neglecting the uncertainty associated with each forecast is misleading, creating a false feeling of unambiguousness. As a consequence, a system to identify, quantify and communicate the sources and magnitude of forecast uncertainty has been developed, which will be presented in part I of this study. In this system, the use of ensemble meteorological forecasts plays a key role which will be presented in part II. Developing the system, several constraints stemming from the range of hydrological regimes and operational requirements had to be met: Firstly, operational time constraints obviate the variation of all components of the modeling chain as would be done in a full Monte Carlo simulation. Therefore, an approach was chosen where only the most relevant sources of uncertainty were dynamically considered while the others were jointly accounted for by static error distributions from offline analysis. Secondly, the dominant sources of uncertainty vary over the wide range of forecasted catchments: In alpine headwater catchments, typically of a few hundred square kilometers in size, rainfall forecast uncertainty is the key factor for forecast uncertainty, with a magnitude dynamically changing with the prevailing predictability of the atmosphere. In lowland catchments encompassing several thousands of square kilometers, forecast uncertainty in the desired range (usually up to two days) is mainly dependent on upstream gauge observation quality, routing and unpredictable human impact such as reservoir operation. The determination of forecast uncertainty comprised the following steps: a) From comparison of gauge

  19. Comparison of extended mean-reversion and time series models for electricity spot price simulation considering negative prices

    International Nuclear Information System (INIS)

    Keles, Dogan; Genoese, Massimo; Möst, Dominik; Fichtner, Wolf

    2012-01-01

    This paper evaluates different financial price and time series models, such as mean reversion, autoregressive moving average (ARMA), integrated ARMA (ARIMA) and general autoregressive conditional heteroscedasticity (GARCH) process, usually applied for electricity price simulations. However, as these models are developed to describe the stochastic behaviour of electricity prices, they are extended by a separate data treatment for the deterministic components (trend, daily, weekly and annual cycles) of electricity spot prices. Furthermore price jumps are considered and implemented within a regime-switching model. Since 2008 market design allows for negative prices at the European Energy Exchange, which also occurred for several hours in the last years. Up to now, only a few financial and time series approaches exist, which are able to capture negative prices. This paper presents a new approach incorporating negative prices. The evaluation of the different approaches presented points out that the mean reversion and the ARMA models deliver the lowest mean root square error between simulated and historical electricity spot prices gained from the European Energy Exchange. These models posses also lower mean average errors than GARCH models. Hence, they are more suitable to simulate well-fitting price paths. Furthermore it is shown that the daily structure of historical price curves is better captured applying ARMA or ARIMA processes instead of mean-reversion or GARCH models. Another important outcome of the paper is that the regime-switching approach and the consideration of negative prices via the new proposed approach lead to a significant improvement of the electricity price simulation. - Highlights: ► Considering negative prices improves the results of time-series and financial models for electricity prices. ► Regime-switching approach captures the jumps and base prices quite well. ► Removing and separate modelling of deterministic annual, weekly and daily

  20. How much are you prepared to PAY for a forecast?

    Science.gov (United States)

    Arnal, Louise; Coughlan, Erin; Ramos, Maria-Helena; Pappenberger, Florian; Wetterhall, Fredrik; Bachofen, Carina; van Andel, Schalk Jan

    2015-04-01

    Probabilistic hydro-meteorological forecasts are a crucial element of the decision-making chain in the field of flood prevention. The operational use of probabilistic forecasts is increasingly promoted through the development of new novel state-of-the-art forecast methods and numerical skill is continuously increasing. However, the value of such forecasts for flood early-warning systems is a topic of diverging opinions. Indeed, the word value, when applied to flood forecasting, is multifaceted. It refers, not only to the raw cost of acquiring and maintaining a probabilistic forecasting system (in terms of human and financial resources, data volume and computational time), but also and most importantly perhaps, to the use of such products. This game aims at investigating this point. It is a willingness to pay game, embedded in a risk-based decision-making experiment. Based on a ``Red Cross/Red Crescent, Climate Centre'' game, it is a contribution to the international Hydrologic Ensemble Prediction Experiment (HEPEX). A limited number of probabilistic forecasts will be auctioned to the participants; the price of these forecasts being market driven. All participants (irrespective of having bought or not a forecast set) will then be taken through a decision-making process to issue warnings for extreme rainfall. This game will promote discussions around the topic of the value of forecasts for decision-making in the field of flood prevention.

  1. Modelling world gold prices and USD foreign exchange relationship using multivariate GARCH model

    Science.gov (United States)

    Ping, Pung Yean; Ahmad, Maizah Hura Binti

    2014-12-01

    World gold price is a popular investment commodity. The series have often been modeled using univariate models. The objective of this paper is to show that there is a co-movement between gold price and USD foreign exchange rate. Using the effect of the USD foreign exchange rate on the gold price, a model that can be used to forecast future gold prices is developed. For this purpose, the current paper proposes a multivariate GARCH (Bivariate GARCH) model. Using daily prices of both series from 01.01.2000 to 05.05.2014, a causal relation between the two series understudied are found and a bivariate GARCH model is produced.

  2. Measuring and Forecasting Financial Market Volatility using High-Frequency Data

    NARCIS (Netherlands)

    K. Bannouh (Karim)

    2013-01-01

    textabstractThis dissertation consists of three studies on the use of intraday asset price data for accurate measurement and forecasting of financial market volatility. Chapter 2 proposes a refined heuristic bias-correction for the two time scales realized range-based volatility estimator in the

  3. State Prices and Implementation of the Recovery Theorem

    Directory of Open Access Journals (Sweden)

    Alex Backwell

    2015-01-01

    Full Text Available It is generally held that derivative prices do not contain useful predictive information, that is, information relating to the distribution of future financial variables under the real-world measure. This is because the market’s implicit forecast of the future becomes entangled with market risk preferences during derivative price formation. A result derived by Ross [1], however, recovers the real-world distribution of an equity index, requiring only current prices and mild restrictions on risk preferences. In addition to being of great interest to the theorist, the potential practical value of the result is considerable. This paper addresses implementation of the Ross Recovery Theorem. The theorem is formalised, extended, proved and discussed. Obstacles to application are identified and a workable implementation methodology is developed.

  4. Energy prices, volatility, and the stock market. Evidence from the Eurozone

    International Nuclear Information System (INIS)

    Oberndorfer, Ulrich

    2009-01-01

    This paper constitutes a first analysis on stock returns of energy corporations from the Eurozone. It focuses on the relationship between energy market developments and the pricing of European energy stocks. According to our results, oil price hikes negatively impact on stock returns of European utilities. However, they lead to an appreciation of oil and gas stocks. Interestingly, forecastable oil market volatility negatively affects European oil and gas stocks, implying profit opportunities for strategic investors. In contrast, the gas market does not play a role for the pricing of Eurozone energy stocks. Coal price developments affect the stock returns of European utilities. However, this effect is small compared to oil price impacts, although oil is barely used for electricity generation in Europe. This suggests that for the European stock market, the oil price is the main indicator for energy price developments as a whole. (author)

  5. CHARCOAL PRICE ANALYSIS IN FOUR REGIONS OF MINAS GERAIS STATE-BRAZIL

    Directory of Open Access Journals (Sweden)

    José Luiz Pereira de Rezende

    2005-09-01

    Full Text Available The State of Minas Gerais is the largest producer and consumer of charcoal, that is used as term-reducer of iron ore,for producing pig iron. This study analyzed the time series of charcoal prices in four regions of Minas Gerais State. For the analysisof the price series, the SARIMA model was used, for finding a model that better forecasts prices for the four studied areas. The mostappropriate models were chosen using graphical analyses of the standardized residues, autocorrelation functions and partialautocorrelations, stochastic tests and criteria of evaluation of the order of the model. It concluded that: the differences of charcoalprices occur, basically, due to the geographical location; the analyses of domain of the time and domain of the frequency showed thatthere is difference in the price series of the four studied areas; the areas of Sete Lagoas and Belo Horizonte, giving that they are closelylocated, possess similar prices and they generated similar model; the studied areas presented differentiated models and supplied goodadjustments for the observed series. The best models were SARIMA (2,1,1x(1,0,012, for Belo Horizonte; SARIMA (2,0,0x(2,1,212,for Divinópolis; SARIMA (2,1,1x(1,0,012, for Sete Lagoas and SARIMA (1,1,1x(1,1,112, for Vertentes. Such models presented in aparsimonious way, containing a small number of parameters. All models SARIMA (p,d,q (P,D,Qs, for the four studied areas,presented white noise and supplied adequate price forecast.

  6. Comparison of ensemble post-processing approaches, based on empirical and dynamical error modelisation of rainfall-runoff model forecasts

    Science.gov (United States)

    Chardon, J.; Mathevet, T.; Le Lay, M.; Gailhard, J.

    2012-04-01

    In the context of a national energy company (EDF : Electricité de France), hydro-meteorological forecasts are necessary to ensure safety and security of installations, meet environmental standards and improve water ressources management and decision making. Hydrological ensemble forecasts allow a better representation of meteorological and hydrological forecasts uncertainties and improve human expertise of hydrological forecasts, which is essential to synthesize available informations, coming from different meteorological and hydrological models and human experience. An operational hydrological ensemble forecasting chain has been developed at EDF since 2008 and is being used since 2010 on more than 30 watersheds in France. This ensemble forecasting chain is characterized ensemble pre-processing (rainfall and temperature) and post-processing (streamflow), where a large human expertise is solicited. The aim of this paper is to compare 2 hydrological ensemble post-processing methods developed at EDF in order improve ensemble forecasts reliability (similar to Monatanari &Brath, 2004; Schaefli et al., 2007). The aim of the post-processing methods is to dress hydrological ensemble forecasts with hydrological model uncertainties, based on perfect forecasts. The first method (called empirical approach) is based on a statistical modelisation of empirical error of perfect forecasts, by streamflow sub-samples of quantile class and lead-time. The second method (called dynamical approach) is based on streamflow sub-samples of quantile class and streamflow variation, and lead-time. On a set of 20 watersheds used for operational forecasts, results show that both approaches are necessary to ensure a good post-processing of hydrological ensemble, allowing a good improvement of reliability, skill and sharpness of ensemble forecasts. The comparison of the empirical and dynamical approaches shows the limits of the empirical approach which is not able to take into account hydrological

  7. Estimating deficit probabilities with price-responsive demand in contract-based electricity markets

    International Nuclear Information System (INIS)

    Galetovic, Alexander; Munoz, Cristian M.

    2009-01-01

    Studies that estimate deficit probabilities in hydrothermal systems have generally ignored the response of demand to changing prices, in the belief that such response is largely irrelevant. We show that ignoring the response of demand to prices can lead to substantial over or under estimation of the probability of an energy deficit. To make our point we present an estimation of deficit probabilities in Chile's Central Interconnected System between 2006 and 2010. This period is characterized by tight supply, fast consumption growth and rising electricity prices. When the response of demand to rising prices is acknowledged, forecasted deficit probabilities and marginal costs are shown to be substantially lower

  8. Time series ARIMA models for daily price of palm oil

    Science.gov (United States)

    Ariff, Noratiqah Mohd; Zamhawari, Nor Hashimah; Bakar, Mohd Aftar Abu

    2015-02-01

    Palm oil is deemed as one of the most important commodity that forms the economic backbone of Malaysia. Modeling and forecasting the daily price of palm oil is of great interest for Malaysia's economic growth. In this study, time series ARIMA models are used to fit the daily price of palm oil. The Akaike Infromation Criterion (AIC), Akaike Infromation Criterion with a correction for finite sample sizes (AICc) and Bayesian Information Criterion (BIC) are used to compare between different ARIMA models being considered. It is found that ARIMA(1,2,1) model is suitable for daily price of crude palm oil in Malaysia for the year 2010 to 2012.

  9. Combining Unsupervised and Supervised Statistical Learning Methods for Currency Exchange Rate Forecasting

    OpenAIRE

    Vasiljeva, Polina

    2016-01-01

    In this thesis we revisit the challenging problem of forecasting currency exchange rate. We combine machine learning methods such as agglomerative hierarchical clustering and random forest to construct a two-step approach for predicting movements in currency exchange prices of the Swedish krona and the US dollar. We use a data set with over 200 predictors comprised of different financial and macro-economic time series and their transformations. We perform forecasting for one week ahead with d...

  10. Analysis of forestry region when changing energy prices

    International Nuclear Information System (INIS)

    Loenner, G.; Nilsson, S.; Nordvall, H.O.

    1986-10-01

    Altered energy prices will reflect a possible nuclear power phaseout. The aim has been to elucidate the situation of the year 2000. The so called world bank model has been used. The forecasting is summarized and commented upon. The results are considering cutting, sawmills, board industry, pulp and paper industry and the social consequences

  11. Is the economic value of hydrological forecasts related to their quality? Case study of the hydropower sector.

    Science.gov (United States)

    Cassagnole, Manon; Ramos, Maria-Helena; Thirel, Guillaume; Gailhard, Joël; Garçon, Rémy

    2017-04-01

    The improvement of a forecasting system and the evaluation of the quality of its forecasts are recurrent steps in operational practice. However, the evaluation of forecast value or forecast usefulness for better decision-making is, to our knowledge, less frequent, even if it might be essential in many sectors such as hydropower and flood warning. In the hydropower sector, forecast value can be quantified by the economic gain obtained with the optimization of operations or reservoir management rules. Several hydropower operational systems use medium-range forecasts (up to 7-10 days ahead) and energy price predictions to optimize hydropower production. Hence, the operation of hydropower systems, including the management of water in reservoirs, is impacted by weather, climate and hydrologic variability as well as extreme events. In order to assess how the quality of hydrometeorological forecasts impact operations, it is essential to first understand if and how operations and management rules are sensitive to input predictions of different quality. This study investigates how 7-day ahead deterministic and ensemble streamflow forecasts of different quality might impact the economic gains of energy production. It is based on a research model developed by Irstea and EDF to investigate issues relevant to the links between quality and value of forecasts in the optimisation of energy production at the short range. Based on streamflow forecasts and pre-defined management constraints, the model defines the best hours (i.e., the hours with high energy prices) to produce electricity. To highlight the link between forecasts quality and their economic value, we built several synthetic ensemble forecasts based on observed streamflow time series. These inputs are generated in a controlled environment in order to obtain forecasts of different quality in terms of accuracy and reliability. These forecasts are used to assess the sensitivity of the decision model to forecast quality

  12. The effects of forecast errors on the merchandising of wind power

    International Nuclear Information System (INIS)

    Roon, Serafin von

    2012-01-01

    A permanent balance between consumption and generation is essential for a stable supply of electricity. In order to ensure this balance, all relevant load data have to be announced for the following day. Consequently, a day-ahead forecast of the wind power generation is required, which also forms the basis for the sale of the wind power at the wholesale market. The main subject of the study is the short-term power supply, which compensates errors in wind power forecasting for balancing the wind power forecast errors at short notice. These forecast errors effects the revenues and the expenses by selling and buying power in the day-ahead, intraday and balance energy market. These price effects resulting from the forecast errors are derived from an empirical analysis. In a scenario for the year 2020 the potential of conventional power plants to supply power at short notice is evaluated from a technical and economic point of view by a time series analysis and a unit commitment simulation.

  13. Results of verification and investigation of wind velocity field forecast. Verification of wind velocity field forecast model

    International Nuclear Information System (INIS)

    Ogawa, Takeshi; Kayano, Mitsunaga; Kikuchi, Hideo; Abe, Takeo; Saga, Kyoji

    1995-01-01

    In Environmental Radioactivity Research Institute, the verification and investigation of the wind velocity field forecast model 'EXPRESS-1' have been carried out since 1991. In fiscal year 1994, as the general analysis, the validity of weather observation data, the local features of wind field, and the validity of the positions of monitoring stations were investigated. The EXPRESS which adopted 500 m mesh so far was improved to 250 m mesh, and the heightening of forecast accuracy was examined, and the comparison with another wind velocity field forecast model 'SPEEDI' was carried out. As the results, there are the places where the correlation with other points of measurement is high and low, and it was found that for the forecast of wind velocity field, by excluding the data of the points with low correlation or installing simplified observation stations to take their data in, the forecast accuracy is improved. The outline of the investigation, the general analysis of weather observation data and the improvements of wind velocity field forecast model and forecast accuracy are reported. (K.I.)

  14. Stock prices forecasting based on wavelet neural networks with PSO

    OpenAIRE

    Wang Kai-Cheng; Yang Chi-I; Chang Kuei-Fang

    2017-01-01

    This research examines the forecasting performance of wavelet neural network (WNN) model using published stock data obtained from Financial Times Stock Exchange (FTSE) Taiwan Stock Exchange (TWSE) 50 index, also known as Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), hereinafter referred to as Taiwan 50. Our WNN model uses particle swarm optimization (PSO) to choose the appropriate initial network values for different companies. The findings come with two advantages. First...

  15. The Information Content of Treasury Bond Options Concerning Future Volatility and Price Jumps

    DEFF Research Database (Denmark)

    Busch, Thomas; Christensen, Bent Jesper; Nielsen, Morten Ørregaard

    We study the relation between realized and implied volatility in the bond market. Realizedvolatility is constructed from high-frequency (5-minute) returns on 30 year Treasury bond futures.Implied volatility is backed out from prices of associated bond options. Recent nonparametric statisticaltech......We study the relation between realized and implied volatility in the bond market. Realizedvolatility is constructed from high-frequency (5-minute) returns on 30 year Treasury bond futures.Implied volatility is backed out from prices of associated bond options. Recent nonparametric...... components. We also introduce a new vector HAR (VecHAR) modelfor the resulting simultaneous system, controlling for possible endogeneity of implied volatility inthe forecasting equations. We show that implied volatility is a biased and inefficient forecast in thebond market. However, implied volatility does...

  16. Enhancing medicine price transparency through price information mechanisms.

    Science.gov (United States)

    Hinsch, Michael; Kaddar, Miloud; Schmitt, Sarah

    2014-05-08

    Medicine price information mechanisms provide an essential tool to countries that seek a better understanding of product availability, market prices and price compositions of individual medicines. To be effective and contribute to cost savings, these mechanisms need to consider prices in their particular contexts when comparing between countries. This article discusses in what ways medicine price information mechanisms can contribute to increased price transparency and how this may affect access to medicines for developing countries. We used data collected during the course of a WHO project focusing on the development of a vaccine price and procurement information mechanism. The project collected information from six medicine price information mechanisms and interviewed data managers and technical experts on key aspects as well as observed market effects of these mechanisms.The reviewed mechanisms were broken down into categories including objective and target audience, as well as the sources, types and volumes of data included. Information provided by the mechanisms was reviewed according to data available on medicine prices, product characteristics, and procurement modalities. We found indications of positive effects on access to medicines resulting from the utilization of the reviewed mechanisms. These include the uptake of higher quality medicines, more favorable results from contract negotiations, changes in national pricing policies, and the decrease of prices in certain segments for countries participating in or deriving data from the various mechanisms. The reviewed mechanisms avoid the methodological challenges observed for medicine price comparisons that only use national price databases. They work with high quality data and display prices in the appropriate context of procurement modalities as well as the peculiarities of purchasing countries. Medicine price information mechanisms respond to the need for increased medicine price transparency and have the

  17. Reply to "Comment on 'Nonparametric forecasting of low-dimensional dynamical systems' ".

    Science.gov (United States)

    Berry, Tyrus; Giannakis, Dimitrios; Harlim, John

    2016-03-01

    In this Reply we provide additional results which allow a better comparison of the diffusion forecast and the "past-noise" forecasting (PNF) approach for the El Niño index. We remark on some qualitative differences between the diffusion forecast and PNF, and we suggest an alternative use of the diffusion forecast for the purposes of forecasting the probabilities of extreme events.

  18. A Comparison of the Performance of Advanced Statistical Techniques for the Refinement of Day-ahead and Longer NWP-based Wind Power Forecasts

    Science.gov (United States)

    Zack, J. W.

    2015-12-01

    Predictions from Numerical Weather Prediction (NWP) models are the foundation for wind power forecasts for day-ahead and longer forecast horizons. The NWP models directly produce three-dimensional wind forecasts on their respective computational grids. These can be interpolated to the location and time of interest. However, these direct predictions typically contain significant systematic errors ("biases"). This is due to a variety of factors including the limited space-time resolution of the NWP models and shortcomings in the model's representation of physical processes. It has become common practice to attempt to improve the raw NWP forecasts by statistically adjusting them through a procedure that is widely known as Model Output Statistics (MOS). The challenge is to identify complex patterns of systematic errors and then use this knowledge to adjust the NWP predictions. The MOS-based improvements are the basis for much of the value added by commercial wind power forecast providers. There are an enormous number of statistical approaches that can be used to generate the MOS adjustments to the raw NWP forecasts. In order to obtain insight into the potential value of some of the newer and more sophisticated statistical techniques often referred to as "machine learning methods" a MOS-method comparison experiment has been performed for wind power generation facilities in 6 wind resource areas of California. The underlying NWP models that provided the raw forecasts were the two primary operational models of the US National Weather Service: the GFS and NAM models. The focus was on 1- and 2-day ahead forecasts of the hourly wind-based generation. The statistical methods evaluated included: (1) screening multiple linear regression, which served as a baseline method, (2) artificial neural networks, (3) a decision-tree approach called random forests, (4) gradient boosted regression based upon an decision-tree algorithm, (5) support vector regression and (6) analog ensemble

  19. Annual Energy Outlook 2013 with Projections to 2040

    Energy Technology Data Exchange (ETDEWEB)

    None

    2013-04-01

    The Annual Energy Outlook 2013 (AEO2013), prepared by the U.S. Energy Information Administration (EIA), presents long-term projections of energy supply, demand, and prices through 2040, based on results from EIA’s National Energy Modeling System. The report begins with an “Executive summary” that highlights key aspects of the projections. It is followed by a “Legislation and regulations” section that discusses evolving legislative and regulatory issues, including a summary of recently enacted legislation and regulations, such as: Updated handling of the U.S. Environmental Protection Agency’s (EPA) National Emissions Standards for Hazardous Air Pollutants for industrial boilers and process heaters; New light-duty vehicle (LDV) greenhouse gas (GHG) and corporate average fuel economy (CAFE) standards for model years 2017 to 2025; Reinstatement of the Clean Air Interstate Rule (CAIR) after the court’s announcement of intent to vacate the Cross-State Air Pollution Rule (CSAPR); and Modeling of California’s Assembly Bill 32, the Global Warming Solutions Act (AB 32), which allows for representation of a cap-and-trade program developed as part of California’s GHG reduction goals for 2020. The “Issues in focus” section contains discussions of selected energy topics, including a discussion of the results in two cases that adopt different assumptions about the future course of existing policies, with one case assuming the elimination of sunset provisions in existing policies and the other case assuming the elimination of the sunset provisions and the extension of a selected group of existing public policies—CAFE standards, appliance standards, and production tax credits. Other discussions include: oil price and production trends in AEO2013; U.S. reliance on imported liquids under a range of cases; competition between coal and natural gas in electric power generation; high and low nuclear scenarios through 2040; and the impact of growth in natural gas

  20. As good as it gets : Alberta economic profile and forecast

    International Nuclear Information System (INIS)

    Hirsch, T.

    2006-04-01

    This economic profile and forecast report for the province of Alberta revealed that nearly every sector of the economy is operating at full, or near full capacity. Alberta's strong economy is a result of high energy prices, rapid population growth and rising employment. Increased provincial government spending along with tax reductions for businesses also contribute to a strong economy. However, the the province still faces some economic challenges, including a general labour shortage. Forestry and agriculture are under pressure of low commodity prices and high input costs, and the province has not articulated a long-term strategy for managing non-renewable natural resource revenue. In addition, the provincial economy is highly dependent upon volatile energy prices. Despite these challenges, the report states that the momentum is on the side of continued economic growth in Alberta. The Canada West Foundation is forecasting real growth in the gross domestic product of 5.2 per cent for 2006 and 4.7 per cent for 2007. The strong energy sector is largely responsible for much of Alberta's economic growth. An estimated 20,000 wells will be drilled in the province in 2006, and high oil prices will lead to record drilling and oil sands investment. This report also highlighted the economic activity in other sectors, including manufacturing, tourism, international exports, hi-tech, forestry, agriculture, and construction. Information regarding interprovincial migration and population growth was included along with public finances. 16 figs

  1. Application of Neural Network Technologies for Price Forecasting in the Liberalized Electricity Market

    Science.gov (United States)

    Gerikh, Valentin; Kolosok, Irina; Kurbatsky, Victor; Tomin, Nikita

    2009-01-01

    The paper presents the results of experimental studies concerning calculation of electricity prices in different price zones in Russia and Europe. The calculations are based on the intelligent software "ANAPRO" that implements the approaches based on the modern methods of data analysis and artificial intelligence technologies.

  2. Electricity spot price forecasting in free power market

    International Nuclear Information System (INIS)

    Lilleberg, J.; Laitinen, E.K.

    1998-01-01

    Deregulation has brought many changes to the electricity market. Freedom of choice has been granted to both the consumers and the utilities. Consumers may choose the seller of their energy. Utilities have a wider array of sources to acquire their electricity from. Also the types of sales contracts used are changing to fill the needs of this new situation. The consumers' right to choose has introduced a new risk uncertainty of volume, which was not true during the times of monopoly. As sold volume is unsure and the energy is not sold on same terms as it is bought, a price risk has to be dealt with also. The electric utility has to realize this, select a risk level that suits its business strategy and optimize its actions according to the selected risk level. The number of participants will grow as the electricity market integrates into a common market for Scandinavia and even Europe. Big customers are also taking a more active role in the market, further increasing the number of participants. This makes old bilateral arrangements outdated. New tools are needed to control the new business environment. The goal of this project has been to develop a theoretical model to predict the price in the Finnish electricity exchange, El-Ex Oy. An extensive literature review was conducted in order to (1) examine the solutions in deregulation of electricity markets in other countries, esp. in Norway and UK, (2) find similarities and differences in electricity exchange and exchanges generally and (3) find major sources of problems and inefficiency in the market

  3. Electricity spot price forecasting in free power market

    Energy Technology Data Exchange (ETDEWEB)

    Lilleberg, J; Laitinen, E K [Vaasa Univ. (Finland)

    1998-08-01

    Deregulation has brought many changes to the electricity market. Freedom of choice has been granted to both the consumers and the utilities. Consumers may choose the seller of their energy. Utilities have a wider array of sources to acquire their electricity from. Also the types of sales contracts used are changing to fill the needs of this new situation. The consumers` right to choose has introduced a new risk uncertainty of volume, which was not true during the times of monopoly. As sold volume is unsure and the energy is not sold on same terms as it is bought, a price risk has to be dealt with also. The electric utility has to realize this, select a risk level that suits its business strategy and optimize its actions according to the selected risk level. The number of participants will grow as the electricity market integrates into a common market for Scandinavia and even Europe. Big customers are also taking a more active role in the market, further increasing the number of participants. This makes old bilateral arrangements outdated. New tools are needed to control the new business environment. The goal of this project has been to develop a theoretical model to predict the price in the Finnish electricity exchange, El-Ex Oy. An extensive literature review was conducted in order to (1) examine the solutions in deregulation of electricity markets in other countries, esp. in Norway and UK, (2) find similarities and differences in electricity exchange and exchanges generally and (3) find major sources of problems and inefficiency in the market

  4. Dynamic pricing of network goods with boundedly rational consumers.

    Science.gov (United States)

    Radner, Roy; Radunskaya, Ami; Sundararajan, Arun

    2014-01-07

    We present a model of dynamic monopoly pricing for a good that displays network effects. In contrast with the standard notion of a rational-expectations equilibrium, we model consumers as boundedly rational and unable either to pay immediate attention to each price change or to make accurate forecasts of the adoption of the network good. Our analysis shows that the seller's optimal price trajectory has the following structure: The price is low when the user base is below a target level, is high when the user base is above the target, and is set to keep the user base stationary once the target level has been attained. We show that this pricing policy is robust to a number of extensions, which include the product's user base evolving over time and consumers basing their choices on a mixture of a myopic and a "stubborn" expectation of adoption. Our results differ significantly from those that would be predicted by a model based on rational-expectations equilibrium and are more consistent with the pricing of network goods observed in practice.

  5. Modelling prices in competitive electricity markets

    International Nuclear Information System (INIS)

    Bunn, D.W.

    2004-04-01

    Electricity markets are structurally different to other commodities, and the real-time dynamic balancing of the electricity network involves many external factors. Because of this, it is not a simple matter to transfer conventional models of financial time series analysis to wholesale electricity prices. The rationale for this compilation of chapters from international authors is, therefore, to provide econometric analysis of wholesale power markets around the world, to give greater understanding of their particular characteristics, and to assess the applicability of various methods of price modelling. Researchers and professionals in this sector will find the book an invaluable guide to the most important state-of-the-art modelling techniques which are converging to define the special approaches necessary for unravelling and forecasting the behaviour of electricity prices. It is a high-quality synthesis of the work of financial engineering, industrial economics and power systems analysis, as they relate to the behaviour of competitive electricity markets. (author)

  6. Gold prices: Analyzing its cyclical behavior

    Directory of Open Access Journals (Sweden)

    Martha Gutiérrez

    2013-07-01

    Full Text Available Gold is a commodity that is seen as a safe haven when a financial crisis strikes, but when stock markets are prosperous, these are more attractive investment alternatives, and so the gold cycle goes on and on. The DJIA/GF (Dow Jones Industrial Average and Gold Fix ratio is chosen to establish the evolution of gold prices in relation to the NYSE. This paper has two goals: to prove that the DJIA/GF ratio is strongly cyclical by using Fourier analysis and to set a predictive neural networks model to forecast the behavior of this ratio during 2011-2020. To this end, business cycle events like the Great Depression along with the 1970s crisis, and the 1950s boom along with the world economic recovery of the 1990s are contrasted in light of the mentioned ratio. Gold prices are found to evolve cyclically with a dominant period of 37 years and are mainly affected by energy prices, financial markets and macroeconomic indicators.

  7. Volatility in energy prices

    International Nuclear Information System (INIS)

    Duffie, D.

    1999-01-01

    This chapter with 58 references reviews the modelling and empirical behaviour of volatility in energy prices. Constant volatility and stochastic volatility are discussed. Markovian models of stochastic volatility are described and the different classes of Markovian stochastic volatility model are examined including auto-regressive volatility, option implied and forecasted volatility, Garch volatility, Egarch volatility, multivariate Garch volatility, and stochastic volatility and dynamic hedging policies. Other volatility models and option hedging are considered. The performance of several stochastic volatility models as applied to heating oil, light oil, natural gas, electricity and light crude oil are compared

  8. Expectations of oil prices in international market: some considerations; Algumas consideracoes sobre as expectativas dos precos do petroleo no mercado internacional

    Energy Technology Data Exchange (ETDEWEB)

    Salles, Andre A. de [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil)

    2008-07-01

    The petroleum market belongs to the more volatile when compared to other markets of financial active. Thus, market academic and professionals recognize the difficulty, and complexity, to obtain a prices forecast model when talking about oil. This work has for objective introduce an alternative methodology to the models which have been presented in the literature, with regard to the procedures of statistical inference. This methodology takes in consideration, in the construction of the prices forecast models, the violation of homoscedasticity, purpose of the classical econometric methods, and the occurrence of abnormal information in the temporal series of the prices or of the returns. This methodology, with Bayesian focus, for forecast models construction of the returns of the petroleum prices, takes in to account the heteroscedasticity of the returns and not the normality. The used information in this work was closing quotations of the market spot of petroleum of the type WTI negotiated in NYMEX. From the market daily prices of oil WTI (West Texas Intermediate), from the period of June 2006 to March 2007, were calculated returns, performing 202 information to be used in the introduced models. The used alternative models introduced presents a superior performance to the available models in the literature and commented in this work. (author)

  9. The oil price; Le prix du petrole

    Energy Technology Data Exchange (ETDEWEB)

    Alba, P. [Institut Francais du Petrole (IFP), 92 - Rueil-Malmaison (France)

    2000-05-01

    Statistical analysis cannot, alone, provide an oil price forecast. So, one needs to understand the fundamental phenomena which control the past trends since the end of world war II After a first period during which oil, thanks to its abundance, was able to increase its market share at the expense of other energies, the first oil shock reflects the rarefaction of oil resource with the tilting of the US production curve from growth to decline. Since then, the new situation is that of a ''cohabitation'' between oil and the other energies with the oil price, extremely volatile, reflecting the trial and error adjustment of the market share left to the other energies. Such a context may explain the recent oil price surge but the analogy between the US oil situation at the time of the first shock and that existing today for the world outside Middle East suggest another possibility, that of a structural change with higher future oil prices. The authors examine these two possibilities, think that the oil price will reflect both as long as one or the other will not become proven, and conclude with a series of political recommendations. (authors)

  10. An empirical exploration of the world oil price under the target zone model

    International Nuclear Information System (INIS)

    Linghui Tang; Shawkat Hammoudeh

    2002-01-01

    This paper investigates the behavior of the world oil price based on the first-generation target zone model. Using anecdotal data during the period of 1988-1999, we found that OPEC has tried to maintain a weak target zone regime for the oil price. Our econometric tests suggest that the movement of the oil price is not only manipulated by actual and substantial interventions by OPEC but also tempered by market participants' expectations of interventions. As a consequence, the non-linear model based on the target zone theory has very good forecasting ability when the oil price approaches the upper or lower limit of the band. (author)

  11. An empirical exploration of the world oil price under the target zone model

    International Nuclear Information System (INIS)

    Tang, Linghui; Hammoudeh, Shawkat

    2002-01-01

    This paper investigates the behavior of the world oil price based on the first-generation target zone model. Using anecdotal data during the period of 1988-1999, we found that OPEC has tried to maintain a weak target zone regime for the oil price. Our econometric tests suggest that the movement of the oil price is not only manipulated by actual and substantial interventions by OPEC but also tempered by market participants' expectations of interventions. As a consequence, the non-linear model based on the target zone theory has very good forecasting ability when the oil price approaches the upper or lower limit of the band

  12. Assistive technology pricing in Australia: is it efficient and equitable?

    Science.gov (United States)

    Summers, Michael P; Verikios, George

    2018-02-01

    Objective To examine available systematically collected evidence regarding prices for assistive technology (AT; e.g. disability aids and equipment) in Australia with other comparable countries. Issues of appropriate AT pricing are coming to the fore as a consequence of efforts to move to consumer-centric purchasing decisions with the National Disability Insurance Scheme (NDIS) and also in the recent aged care reforms. Methods We identified and present three sets of AT price comparisons. Two comparisons were based solely on the lowest prices advertised on the internet, and one comparison examined recommended retail prices. Variables essential to ensuring accurate comparisons, as well as significant supply-chain issues were also examined and considered in the analyses. Results The first internet-only price comparison found that overall AT prices were 38% higher in Australia compared to other countries, but did not factor in shipping and other related costs that are essential to include given that most AT is imported. The second internet-only price comparison found that overall Australian prices were 24% lower when shipping and related costs were included. The recommended retail price comparisons found that Australian prices were between 14% and 27% lower. Prices for internet-only retailers (those with no bricks-and-mortar presence) are consistently lower for all products than those sold by retailers with actual shop-fronts. Further, there is no evidence of suppliers earning supranormal profits in Australia. Conclusions The results indicate that AT prices in Australia are efficient and equitable, with no significant indicators of market failure which would require government intervention. Efforts to reduce prices through the excessive use of large-scale government procurement programs are likely to reduce diversity and innovation in AT and raise AT prices over time. Open markets and competition with centralised tracking of purchases and providers to minimise possible

  13. Analysis on the choice of the most suitable metal prices in a mining investment project

    International Nuclear Information System (INIS)

    Torre, L. de la; Espi, J. a.

    2014-01-01

    The mineral price assigned in mining project design is critical to determining the economic feasibility of a project. Nevertheless, although it is not difficult to find literature about market metal prices, it is much more complicated to achieve a specific methodology for calculating the value or which justifications are appropriate to include. This study presents an analysis of various methods for selecting metal prices and investigates the mechanisms and motives underlying price selections. The results describe various attitudes adopted by the designers of mining investment project, and how the price can be determined not just by means of forecasting also by consideration of other relevant parameters. (Author)

  14. What Factors Affect the Prices of Low-Priced U.S. Solar PV Systems?

    Energy Technology Data Exchange (ETDEWEB)

    Nemet, Gregory F. [Univ. of Wisconsin, Madison, WI (United States); Mercator Research Inst. on Global Commons and Climate Change, Berlin (Germany); O' Shaughnessy, Eric [National Renewable Energy Lab. (NREL), Golden, CO (United States); Wiser, Ryan [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Darghouth, Naïm R. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Barbose, Galen [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Gillingham, Ken [Yale Univ., New Haven, CT (United States); Rai, Varun [Univ. of Texas, Austin, TX (United States)

    2016-08-01

    The price of solar PV systems has declined rapidly, yet there are some much lower-priced systems than others. This study explores the factors leading some systems to be so much lower priced than others. Using a data set of 42,611 residential-scale PV systems installed in the U.S. in 2013, we use quantile regressions to estimate the importance of factors affecting the installed prices for low-priced (LP) systems (those at the 10th percentile) in comparison to median-priced systems. We find that the value of solar to consumers–a variable that accounts for subsidies, electric rates, and PV generation levels–is associated with lower prices for LP systems but higher prices for median priced systems. Conversely, systems installed in new home construction are associated with lower prices at the median but higher prices for LP. Other variables have larger cost-reducing effects on LP than on median priced systems: systems installed in Arizona and Florida, as well as commercial and thin film systems. In contrast, the following have a smaller effect on prices for LP systems than median priced systems: tracking systems, self-installations, systems installed in Massachusetts, the system size, and installer experience. These results highlight the complex factors at play that lead to LP systems and shed light into how such LP systems can come about.

  15. An Empirical Comparison of Default Swap Pricing Models

    NARCIS (Netherlands)

    P. Houweling (Patrick); A.C.F. Vorst (Ton)

    2002-01-01

    textabstractAbstract: In this paper we compare market prices of credit default swaps with model prices. We show that a simple reduced form model with a constant recovery rate outperforms the market practice of directly comparing bonds' credit spreads to default swap premiums. We find that the

  16. Enterprise systems in financial sector - an application in precious metal trading forecasting

    Science.gov (United States)

    Chen, Xiaozhu; Fang, Yiwei

    2013-11-01

    The use of enterprise systems has become increasingly popular in the financial service industry. This paper discusses the applications of enterprise systems in the financial sectors and presents an application in gold price forecasting. We carefully examine the impacts of a few most widely assumed factors that have significant impact on the long-term gold price using statistical regression techniques. The analysis on our proposed linear regression mode indicates that the United States ultra scale of M2 money supply has been the most important catalyst for the rising price of gold, and the CRB index upward trend has also been the weighty factor for pushing up the gold price. In addition, the gold price has a low negative correlation with the Dow Jones Industrial Average, and low positive correlations with the US dollar index and the gold ETFs holdings.

  17. Hydro-economic assessment of hydrological forecasting systems

    Science.gov (United States)

    Boucher, M.-A.; Tremblay, D.; Delorme, L.; Perreault, L.; Anctil, F.

    2012-01-01

    SummaryAn increasing number of publications show that ensemble hydrological forecasts exhibit good performance when compared to observed streamflow. Many studies also conclude that ensemble forecasts lead to a better performance than deterministic ones. This investigation takes one step further by not only comparing ensemble and deterministic forecasts to observed values, but by employing the forecasts in a stochastic decision-making assistance tool for hydroelectricity production, during a flood event on the Gatineau River in Canada. This allows the comparison between different types of forecasts according to their value in terms of energy, spillage and storage in a reservoir. The motivation for this is to adopt the point of view of an end-user, here a hydroelectricity production society. We show that ensemble forecasts exhibit excellent performances when compared to observations and are also satisfying when involved in operation management for electricity production. Further improvement in terms of productivity can be reached through the use of a simple post-processing method.

  18. Demand Forecasting at Low Aggregation Levels using Factored Conditional Restricted Boltzmann Machine

    DEFF Research Database (Denmark)

    Mocanu, Elena; Nguyen, Phuong H.; Gibescu, Madeleine

    2016-01-01

    electric power consumption, local price and meteorological data collected from 1900 customers. The households are equipped with local generation and smart appliances capable of responding to realtime pricing signals. The results show that for the short-term (5 minute to 1 day ahead) prediction problems......The electrical demand forecasting problem can be regarded as a nonlinear time series prediction problem depending on many complex factors since it is required at various aggregation levels and at high temporal resolution. To solve this challenging problem, various time series and machine learning...... developed deep learning model for time series prediction, namely Factored Conditional Restricted Boltzmann Machine (FCRBM), and extend it for electrical demand forecasting. The assessment is made on the EcoGrid dataset, originating from the Bornholm island experiment in Denmark, consisting of aggregated...

  19. The Carbon Trading Price and Trading Volume Forecast in Shanghai City by BP Neural Network

    OpenAIRE

    Liu Zhiyuan; Sun Zongdi

    2017-01-01

    In this paper, the BP neural network model is established to predict the carbon trading price and carbon trading volume in Shanghai City. First of all, we find the data of carbon trading price and carbon trading volume in Shanghai City from September 30, 2015 to December 23, 2016. The carbon trading price and trading volume data were processed to get the average value of each 5, 10, 20, 30, and 60 carbon trading price and trading volume. Then, these data are used as input of BP neural network...

  20. How Should they Affect Pricing Decisions? Difficult Comparison Effect

    Directory of Open Access Journals (Sweden)

    Angela Eliza MICU

    2007-01-01

    Full Text Available In most companies, there is ongoing conflict between managers in charge of covering costs (finance and accounting and managers in charge of satisfying customers (marketing and sales. Accounting journals warn against prices that fail to cover full costs, while marketing journals argue that customer willingness-to-pay must be the sole driver of prices. The conflict between these views wastes company resources and leads to pricing decisions that are imperfect compromises. Profitable pricing involves an integration of costs and customer value. To achieve that integration, however, both need to let go of misleading ideas and form a common vision of what drives profitability.

  1. Forecasting the Seasonal Timing of Maine's Lobster Fishery

    Directory of Open Access Journals (Sweden)

    Katherine E. Mills

    2017-11-01

    Full Text Available The fishery for American lobster is currently the highest-valued commercial fishery in the United States, worth over US$620 million in dockside value in 2015. During a marine heat wave in 2012, the fishery was disrupted by the early warming of spring ocean temperatures and subsequent influx of lobster landings. This situation resulted in a price collapse, as the supply chain was not prepared for the early and abundant landings of lobsters. Motivated by this series of events, we have developed a forecast of when the Maine (USA lobster fishery will shift into its high volume summer landings period. The forecast uses a regression approach to relate spring ocean temperatures derived from four NERACOOS buoys along the coast of Maine to the start day of the high landings period of the fishery. Tested against conditions in past years, the forecast is able to predict the start day to within 1 week of the actual start, and the forecast can be issued 3–4 months prior to the onset of the high-landings period, providing valuable lead-time for the fishery and its associated supply chain to prepare for the upcoming season. Forecast results are conveyed in a probabilistic manner and are updated weekly over a 6-week forecasting period so that users can assess the certainty and consistency of the forecast and factor the uncertainty into their use of the information in a given year. By focusing on the timing of events, this type of seasonal forecast provides climate-relevant information to users at time scales that are meaningful for operational decisions. As climate change alters seasonal phenology and reduces the reliability of past experience as a guide for future expectations, this type of forecast can enable fishing industry participants to better adjust to and prepare for operating in the context of climate change.

  2. Limits to oil pricing: scenario planning as a device to understand oil price developments

    International Nuclear Information System (INIS)

    Austvik, O.G.

    1992-01-01

    This paper underlines that the politicizing of the oil market makes economics, politics and even pure warfare important elements in the formation of the price of oil. The disagreement about which theory to use to analyze the market and the bad record of oil price forecasting indicates that conventional oil market models should be critically re-assessed. The scenario planning methodology presented in this paper may be one alternative approach. SP does not overthrow any other theories of the market. But it claims that no single discipline is able to tell the whole truth about the market. The SP approach stresses and clarifies the role of uncertainty in the development of oil prices and underlines the importance of the understanding of the functioning of the market. It argues that without a cross-disciplinary approach, with an adequate choice of parameters, at the right level of in-depth discussion, the analysis may lose essential input or drown in detail. As an example of the methodology, an analysis of development of oil prices in the nineties is presented. It is shown that lower (indicated as 15-20 S/bbl) and upper (indicated as 30-40 S/bbl) limits of the price in the long run can be constructed, based on economic, political and strategic reasoning. It is also argued that short run 'shocks' outside these limits may have become less likely, because: (1) the strategic petroleum reserves (SPR) will cut off the most extreme prices above the upper limit and (2) the existence of a supply side regulator, like OPEC, will prevent prices from dropping below the lower limit for any longer period of time. Sensitivity analysis tests the 'robustness' of the approach. 10 refs., 1 fig

  3. Oil price prospects

    International Nuclear Information System (INIS)

    Toalster, J.

    1992-01-01

    In this paper, four different, popular approaches to the analysis of oil price movements will be considered and an alternative method will be proposed. Whilst we await the development of a rigorous theoretical framework within which to evaluate the phenomenon of oil price movements some progress may be effected by an amalgam of approaches, with the traditional supply and demand model being supplemented by observations regarding political and social developments in particular countries or regions, together with an assessment of emerging and prospective technological achievements. In this way it should be possible to identify the critical influences at work, from which it should also be possible to select either the single most important variable or combination of variables, affecting the oil price. Moreover, it is my belief that the crucial variables influencing the oil price almost certainly, are more likely to be political and social, rather than economic. In this context and notwithstanding the fact that there is only a minimal level of surplus productive capacity in the world oil industry at present (perhaps 1-2 million b/d albeit rising rapidly), it is reasonable to conclude that oil prices will average around $18-19 a barrel for North Sea Brent in 1992 and 1993, with oscillations of $2-4 a barrel either side, rising slightly in 1994 to $19-20 a barrel and to $20-21 a barrel in 1995. Thereafter, the most likely outcome is for a rise in line with inflation (say $ a barrel/annum) with no prospect of an upward spike, because demand will be weaker than most commentators expect up to the year 2000, whilst OPEC oil supplies will be substantially higher than the consensus forecast. (author)

  4. Oil prices and stocks in the second quarter of 2004

    International Nuclear Information System (INIS)

    2004-01-01

    Notwithstanding forecasting difficulties, the oil supply and demand balance has proved to be a good indicator of the state of the market and stock levels, which, in turn, influence price behaviour. In periods where OECD commercial stock levels lie within a certain range, currently around 2,450-2,650 million barrels, the range of prices is larger than when stock levels are very high or very low. In both the latter extreme situations, prices are prone to rapid movements, undermining market stability. Other factors, of course, also influence price fluctuations. The general opinion among regularly published oil market reports points to the inevitability of a higher-than-normal build in stocks in the second quarter of 2004. If the resulting surplus is not handled in a timely and effective manner, there is likely to be excessive downward pressure on prices, which, if left unattended, would lead to a protracted spell of volatility. (Author)

  5. Pricing Vulnerable Options with Market Prices of Common Jump Risks under Regime-Switching Models

    Directory of Open Access Journals (Sweden)

    Miao Han

    2018-01-01

    Full Text Available This paper investigates the valuation of vulnerable European options considering the market prices of common systematic jump risks under regime-switching jump-diffusion models. The way of regime-switching Esscher transform is adopted to identify an equivalent martingale measure for pricing vulnerable European options. Explicit analytical pricing formulae for vulnerable European options are derived by risk-neutral pricing theory. For comparison, the other two cases are also considered separately. The first case considers all jump risks as unsystematic risks while the second one assumes all jumps risks to be systematic risks. Numerical examples for the valuation of vulnerable European options are provided to illustrate our results and indicate the influence of the market prices of jump risks on the valuation of vulnerable European options.

  6. Forecasting Costa Rican Quarterly Growth with Mixed-frequency Models

    Directory of Open Access Journals (Sweden)

    Adolfo Rodríguez Vargas

    2014-11-01

    Full Text Available We assess the utility of mixed-frequency models to forecast the quarterly growth rate of Costa Rican real GDP: we estimate bridge and MiDaS models with several lag lengths using information of the IMAE and compute forecasts (horizons of 0-4 quarters which are compared between themselves, with those of ARIMA models and with those resulting from forecast combinations. Combining the most accurate forecasts is most useful when forecasting in real time, whereas MiDaS forecasts are the best-performing overall: as the forecasting horizon increases, their precisionis affected relatively little; their success rates in predicting the direction of changes in the growth rate are stable, and several forecastsremain unbiased. In particular, forecasts computed from simple MiDaS with 9 and 12 lags are unbiased at all horizons and information sets assessed, and show the highest number of significant differences in forecasting ability in comparison with all other models.

  7. North American natural gas liquids pricing and convergence : an energy market assessment

    International Nuclear Information System (INIS)

    2001-05-01

    A background on natural gas liquids (NGL) pricing was presented along with a discussion regarding the impact of energy price convergence. The high energy prices in the fall of 2000 were a result of many factors, including the high price of NGLs. All NGL components such as ethane, propane and butane can be used as petrochemical feedstock. In the winter of 2000/2001 the relationship between liquids and crude oil prices collapsed when high energy prices led to a situation where, for a short while, extraction of liquids from natural gas became uneconomic since producers got more value for NGLs left in the gas stream. As a result, when the supply and demand balances for NGL tightened in many regions of North America, NGL prices were reflecting the unprecedented high natural gas prices. This paper also explained how the four major North American NGL trading hubs in Alberta, Ontario, Kansas and Texas operate. The pricing events of 2000 have impacted on the NGL industry and energy prices remain an issue since both crude oil and natural gas price are forecasted to remain strong in the near future. 5 figs

  8. A Feature Fusion Based Forecasting Model for Financial Time Series

    Science.gov (United States)

    Guo, Zhiqiang; Wang, Huaiqing; Liu, Quan; Yang, Jie

    2014-01-01

    Predicting the stock market has become an increasingly interesting research area for both researchers and investors, and many prediction models have been proposed. In these models, feature selection techniques are used to pre-process the raw data and remove noise. In this paper, a prediction model is constructed to forecast stock market behavior with the aid of independent component analysis, canonical correlation analysis, and a support vector machine. First, two types of features are extracted from the historical closing prices and 39 technical variables obtained by independent component analysis. Second, a canonical correlation analysis method is utilized to combine the two types of features and extract intrinsic features to improve the performance of the prediction model. Finally, a support vector machine is applied to forecast the next day's closing price. The proposed model is applied to the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better in the area of prediction than other two similar models. PMID:24971455

  9. PRICE FAIRNESS PERCEPTION ON ASIATIC MARKETS

    OpenAIRE

    Elisabeta Ioanăș

    2012-01-01

    The purpose of this paper is to show the perception of the price fairness in the literature. I made a small comparison between the European and the Asiatic markets. Price is an important key of the marketing mix. On the daily basis, the price fairness is perceived differently from one user to another. Also other researchers states that the price has a great influence on the perceived value and on buying intentions for a product and through this paper can be seen that the culture influences th...

  10. The integration of Price Responsive Demand into Regional Transmission Organization (RTO) wholesale power markets and system operations

    International Nuclear Information System (INIS)

    Centolella, Paul

    2010-01-01

    A number of states and utilities are pursuing demand response based on dynamic and time-differentiated retail prices and utility investments in Advanced Metering Infrastructure (AMI), often as part of Smart Grid initiatives. These developments could produce large amounts of Price Responsive Demand, demand that predictably responds to changes in wholesale prices. Price Responsive Demand could provide significant reliability and economic benefits. However, existing RTO tariffs present potential barriers to the development of Price Responsive Demand. Effectively integrating Price Responsive Demand into RTO markets and operations will require changes in demand forecasting, scarcity pricing reform, synchronization of scarcity pricing with capacity markets, tracking voluntary hedging by price responsive loads, and a non-discriminatory approach in curtailments in capacity emergencies. The article describes changes in RTO policies and systems needed incorporate Price Responsive Demand. (author)

  11. Functional dynamic factor models with application to yield curve forecasting

    KAUST Repository

    Hays, Spencer

    2012-09-01

    Accurate forecasting of zero coupon bond yields for a continuum of maturities is paramount to bond portfolio management and derivative security pricing. Yet a universal model for yield curve forecasting has been elusive, and prior attempts often resulted in a trade-off between goodness of fit and consistency with economic theory. To address this, herein we propose a novel formulation which connects the dynamic factor model (DFM) framework with concepts from functional data analysis: a DFM with functional factor loading curves. This results in a model capable of forecasting functional time series. Further, in the yield curve context we show that the model retains economic interpretation. Model estimation is achieved through an expectation- maximization algorithm, where the time series parameters and factor loading curves are simultaneously estimated in a single step. Efficient computing is implemented and a data-driven smoothing parameter is nicely incorporated. We show that our model performs very well on forecasting actual yield data compared with existing approaches, especially in regard to profit-based assessment for an innovative trading exercise. We further illustrate the viability of our model to applications outside of yield forecasting.

  12. Forecasting parameters of a the monuclear power plant with a torsatron reactor

    International Nuclear Information System (INIS)

    Artyugina, I.M.; Semenov, A.A.; Smirnov, A.N.

    1982-01-01

    A number of problems related to forecasting technical economical factors of thermonuclear electric plant (TNPP) based on the torsatron reactor is considered. Possible methodic approaches to the estimation of TNPP nonstandard equipment construction-mounting works and the results of forecasting the investment structure in TNPP are analysed. The influence of TP basic systems on the total investment value depending on accepted price level is shown. Quantitative estimations of specific investments and electric energy production cost permit to estimate rather optimistically the considered TNPP type and to draw a conclusion on advisability of the further study

  13. Power density forecasting device for nuclear power plant

    International Nuclear Information System (INIS)

    Fukuzaki, Takaharu; Kiguchi, Takashi.

    1978-01-01

    Purpose: To attain effective reactor operation in a bwr type reactor by forecasting the power density of the reactor after adjustment and comparing the same with the present status of the reactor by the on-line calculation in a short time. Constitution: The present status for the reactor is estimated in a present status decision section based on a measurement signal from the reactor and it is stored in an operation result collection section. The reactor status after the forecasting is estimated in a forecasting section based on a setting signal from a forecasting condition setting section and it is compared with the result value from the operation results collection section. If the forecast value does not coincide with the result value in the above comparison, the setting value in the forecast condition setting section is changed in the control section. The above procedures are repeated so as to minimize the difference between the forecast value and the result value to thereby exactly forecast the reactor status and operate the reactor effectively. (Moriyama, K.)

  14. Price elasticity matrix of demand in power system considering demand response programs

    Science.gov (United States)

    Qu, Xinyao; Hui, Hongxun; Yang, Shengchun; Li, Yaping; Ding, Yi

    2018-02-01

    The increasing renewable energy power generations have brought more intermittency and volatility to the electric power system. Demand-side resources can improve the consumption of renewable energy by demand response (DR), which becomes one of the important means to improve the reliability of power system. In price-based DR, the sensitivity analysis of customer’s power demand to the changing electricity prices is pivotal for setting reasonable prices and forecasting loads of power system. This paper studies the price elasticity matrix of demand (PEMD). An improved PEMD model is proposed based on elasticity effect weight, which can unify the rigid loads and flexible loads. Moreover, the structure of PEMD, which is decided by price policies and load types, and the calculation method of PEMD are also proposed. Several cases are studied to prove the effectiveness of this method.

  15. Short-term Consumer Benefits of Dynamic Pricing

    OpenAIRE

    Dupont, Benjamin; De Jonghe, Cedric; Kessels, Kris; Belmans, Ronnie

    2011-01-01

    Consumer benefits of dynamic pricing depend on a variety of factors. Consumer characteristics and climatic circumstances widely differ, which forces a regional comparison. This paper presents a general overview of demand response programs and focuses on the short-term benefits of dynamic pricing for an average Flemish residential consumer. It reaches a methodology to develop a cost reflective dynamic pricing program and to estimate short-term bill savings. Participating in a dynamic pricing p...

  16. 36 CFR 1210.45 - Cost and price analysis.

    Science.gov (United States)

    2010-07-01

    ... 36 Parks, Forests, and Public Property 3 2010-07-01 2010-07-01 false Cost and price analysis. 1210.45 Section 1210.45 Parks, Forests, and Public Property NATIONAL ARCHIVES AND RECORDS ADMINISTRATION... in various ways, including the comparison of price quotations submitted, market prices and similar...

  17. Wind energy and electricity prices. Exploring the 'merit order effect'

    International Nuclear Information System (INIS)

    Morthost, P.E.; Ray, S.; Munksgaard, J.; Sinner, A.F.

    2010-04-01

    This report focuses on the effect of wind energy on the electricity price in the power market. As the report will discuss, adding wind into the power mix has a significant influence on the resulting price of electricity, the so called merit order effect (MOE). The merit order effect has been quantified and discussed in many scientific publications. This report ends the first phase of a study on the MOE, evaluating the impact of EWEA's 2020 scenarios on future European electricity prices. The basic principles of the merit order effect are provided in the first part of the document. The literature review itself contains methods and tools not only to quantify the merit order effect but also in order to forecast its future range and volume.

  18. Forecasting Exchange Rate Volatility in the Presence of Jumps

    DEFF Research Database (Denmark)

    Busch, Thomas; Christensen, Bent Jesper; Nielsen, Morten Ørregaard

    We study measures of foreign exchange rate volatility based on high-frequency (5-minute) $/DM exchange rate returns using recent nonparametric statistical techniquesto compute realized return volatility and its separate continuous sample path and jumpcomponents, and measures based on prices...... of exchange rate futures options, allowingcalculation of option implied volatility. We find that implied volatility is an informationallyefficient but biased forecast of future realized exchange rate volatility. Furthermore,we show that log-normality is an even better distributional approximation...... for impliedvolatility than for realized volatility in this market. Finally, we show that the jump componentof future realized exchange rate volatility is to some extent predictable, and thatoption implied volatility is the dominant forecast of the future jump component....

  19. Forecasting volatility and spillovers in crude oil spot, forward and future markets

    NARCIS (Netherlands)

    C-L. Chang (Chia-Lin); M.J. McAleer (Michael); R. Tansuchat (Roengchai)

    2009-01-01

    textabstractCrude oil price volatility has been analyzed extensively for organized spot, forward and futures markets for well over a decade, and is crucial for forecasting volatility and Value-at-Risk (VaR). There are four major benchmarks in the international oil market, namely West Texas

  20. FACTORS INFLUENCING THE ACCURACY OF PRE-CONTRACT STAGE ESTIMATION OF FINAL CONTRACT PRICE IN NEW ZEALAND

    Directory of Open Access Journals (Sweden)

    Cong Ji

    2014-12-01

    Full Text Available Establishing and prioritising the factors that may influence the final contract price when responding to a call for tenders is crucial for proper risk analysis and reliable forecasting; it could make or mar the ability to achieve expected profit margin in an era of lump sum fixed price contracts where clients often contest variation claims. In New Zealand, these factors have not been researched; hence estimators rely only on judgement to ‘guess-estimate’ in their price forecasting. This study aimed to fill the knowledge gap by investigating the priority factors. 150 responses from professional members of the New Zealand Institute of Quantity Surveyors were analysed using multi-attribute method. Results showed thirty-seven factors which could influence the final contract price; the three most influential being poor tender documentation, complexity of design & construction, and completeness of project information. Other factors relating to project, client and contractor characteristics, design consultants and tendering conditions, estimating practice and external factors were reported. Concordance analysis indicated high level of agreement amongst survey participants in the rank-ordering of the relative importance of the identified factors. The findings could assist quantity surveyors to prepare more reliable contract price estimates at the pre-contract stage. It would also improve construction-stage cost control.