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Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models

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  • Weron, Rafal
  • Misiorek, Adam

Abstract

This empirical paper compares the accuracy of 12 time series methods for short-term (day-ahead) spot price forecasting in auction-type electricity markets. The methods considered include standard autoregression (AR) models, their extensions – spike preprocessed, threshold and semiparametric autoregressions (i.e. AR models with nonparametric innovations), as well as, mean-reverting jump diffusions. The methods are compared using a time series of hourly spot prices and system-wide loads for California and a series of hourly spot prices and air temperatures for the Nordic market. We find evidence that (i) models with system load as the exogenous variable generally perform better than pure price models, while this is not necessarily the case when air temperature is considered as the exogenous variable, and that (ii) semiparametric models generally lead to better point and interval forecasts than their competitors, more importantly, they have the potential to perform well under diverse market conditions.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 10428.

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Date of creation: 10 Jun 2008
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Handle: RePEc:pra:mprapa:10428

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Keywords: Electricity market; Price forecast; Autoregressive model; Nonparametric maximum likelihood; Interval forecast; Conditional coverage;

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References

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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  1. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
  2. Huisman, R. & Mahieu, R.J., 2003. "Regime jumps in electricity prices," Open Access publications from Tilburg University urn:nbn:nl:ui:12-3131736, Tilburg University.
  3. Panagiotelis, Anastasios & Smith, Michael, 2008. "Bayesian density forecasting of intraday electricity prices using multivariate skew t distributions," International Journal of Forecasting, Elsevier, Elsevier, vol. 24(4), pages 710-727.
  4. Marie Bessec & Othman Bouabdallah, 2005. "What causes the forecasting failure of Markov-Switching models? A Monte Carlo study," Econometrics, EconWPA 0503018, EconWPA.
  5. Chan, Kam Fong & Gray, Philip & van Campen, Bart, 2008. "A new approach to characterizing and forecasting electricity price volatility," International Journal of Forecasting, Elsevier, Elsevier, vol. 24(4), pages 728-743.
  6. Misiorek Adam & Trueck Stefan & Weron Rafal, 2006. "Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, De Gruyter, vol. 10(3), pages 1-36, September.
  7. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
  8. Karakatsani, Nektaria V. & Bunn, Derek W., 2008. "Forecasting electricity prices: The impact of fundamentals and time-varying coefficients," International Journal of Forecasting, Elsevier, Elsevier, vol. 24(4), pages 764-785.
  9. Ricardo Cao, 1999. "An overview of bootstrap methods for estimating and predicting in time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, Springer, vol. 8(1), pages 95-116, June.
  10. Bierbrauer, Michael & Menn, Christian & Rachev, Svetlozar T. & Truck, Stefan, 2007. "Spot and derivative pricing in the EEX power market," Journal of Banking & Finance, Elsevier, Elsevier, vol. 31(11), pages 3462-3485, November.
  11. Wolfgang HÄRDLE & H. LÜTKEPOHL & R. CHEN, 1996. "A Review of Nonparametric Time Series Analysis," SFB 373 Discussion Papers 1996,48, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  12. Ball, Clifford A. & Torous, Walter N., 1983. "A Simplified Jump Process for Common Stock Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, Cambridge University Press, vol. 18(01), pages 53-65, March.
  13. Weron, Rafal, 2008. "Market price of risk implied by Asian-style electricity options and futures," Energy Economics, Elsevier, Elsevier, vol. 30(3), pages 1098-1115, May.
  14. Weron, Rafal, 2009. "Forecasting wholesale electricity prices: A review of time series models," MPRA Paper 21299, University Library of Munich, Germany.
  15. Conejo, Antonio J. & Contreras, Javier & Espinola, Rosa & Plazas, Miguel A., 2005. "Forecasting electricity prices for a day-ahead pool-based electric energy market," International Journal of Forecasting, Elsevier, Elsevier, vol. 21(3), pages 435-462.
  16. Fong Chan, Kam & Gray, Philip, 2006. "Using extreme value theory to measure value-at-risk for daily electricity spot prices," International Journal of Forecasting, Elsevier, Elsevier, vol. 22(2), pages 283-300.
  17. Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, Elsevier, vol. 27(5), pages 791-817, September.
  18. Crespo Cuaresma, Jesús & Hlouskova, Jaroslava & Kossmeier, Stephan & Obersteiner, Michael, 2004. "Forecasting electricity spot-prices using linear univariate time-series models," Applied Energy, Elsevier, Elsevier, vol. 77(1), pages 87-106, January.
  19. Bouabdallah, Othman & Bessec, Marie, 2005. "What causes the forecasting failure of Markov-switching models ? A Monte Carlo study," Economics Papers from University Paris Dauphine 123456789/6064, Paris Dauphine University.
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Citations

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Cited by:
  1. Fouquau, Julien & Bessec, Marie & Méritet, Sophie, 2014. "Forecasting electricity spot prices using time-series models with a double temporal segmentation," Economics Papers from University Paris Dauphine 123456789/13532, Paris Dauphine University.
  2. Schlueter, Stephan, 2010. "A long-term/short-term model for daily electricity prices with dynamic volatility," Energy Economics, Elsevier, Elsevier, vol. 32(5), pages 1074-1081, September.
  3. Zafirakis, Dimitrios & Chalvatzis, Konstantinos J. & Baiocchi, Giovanni & Daskalakis, George, 2013. "Modeling of financial incentives for investments in energy storage systems that promote the large-scale integration of wind energy," Applied Energy, Elsevier, Elsevier, vol. 105(C), pages 138-154.
  4. Jakub Nowotarski & Rafal Weron, 2014. "Merging quantile regression with forecast averaging to obtain more accurate interval forecasts of Nord Pool spot prices," HSC Research Reports, Hugo Steinhaus Center, Wroclaw University of Technology HSC/14/03, Hugo Steinhaus Center, Wroclaw University of Technology.
  5. Jakub Nowotarski & Rafal Weron, 2013. "Computing electricity spot price prediction intervals using quantile regression and forecast averaging," HSC Research Reports, Hugo Steinhaus Center, Wroclaw University of Technology HSC/13/12, Hugo Steinhaus Center, Wroclaw University of Technology.
  6. Khosravi, Abbas & Nahavandi, Saeid & Creighton, Doug, 2013. "Quantifying uncertainties of neural network-based electricity price forecasts," Applied Energy, Elsevier, Elsevier, vol. 112(C), pages 120-129.
  7. Simon Hagemann, 2013. "Price Determinants in the German Intraday Market for Electricity: An Empirical Analysis," EWL Working Papers, University of Duisburg-Essen, Chair for Management Science and Energy Economics 1318, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Oct 2013.
  8. Gro Klaeboe & Anders Lund Eriksrud & Stein-Erik Fleten, 2013. "Benchmarking time series based forecasting models for electricity balancing market prices," Working Papers 2013-006, The George Washington University, Department of Economics, Research Program on Forecasting.
  9. Carlo Lucheroni, 2012. "A hybrid SETARX model for spikes in tight electricity markets," Operations Research and Decisions, Wroclaw University of Technology, Institute of Organization and Management, Wroclaw University of Technology, Institute of Organization and Management, vol. 1, pages 13-49.
  10. Joanna Janczura & Rafał Weron, 2013. "Goodness-of-fit testing for the marginal distribution of regime-switching models with an application to electricity spot prices," AStA Advances in Statistical Analysis, Springer, Springer, vol. 97(3), pages 239-270, July.
  11. Chan, Kam Fong & Gray, Philip & van Campen, Bart, 2008. "A new approach to characterizing and forecasting electricity price volatility," International Journal of Forecasting, Elsevier, Elsevier, vol. 24(4), pages 728-743.
  12. Angelica Gianfreda & Luigi Grossi, 2011. "Forecasting Italian Electricity Zonal Prices with Exogenous Variables," Working Papers, University of Verona, Department of Economics 01/2011, University of Verona, Department of Economics.
  13. Nomikos, Nikos & Andriosopoulos, Kostas, 2012. "Modelling energy spot prices: Empirical evidence from NYMEX," Energy Economics, Elsevier, Elsevier, vol. 34(4), pages 1153-1169.
  14. repec:dgr:uvatin:2013068 is not listed on IDEAS
  15. Katarzyna Maciejowska & Rafal Weron, 2013. "Forecasting of daily electricity prices with factor models: Utilizing intra-day and inter-zone relationships," HSC Research Reports, Hugo Steinhaus Center, Wroclaw University of Technology HSC/13/11, Hugo Steinhaus Center, Wroclaw University of Technology.
  16. Katarzyna Maciejowska & Rafal Weron, 2013. "Forecasting of daily electricity spot prices by incorporating intra-day relationships: Evidence form the UK power market," HSC Research Reports, Hugo Steinhaus Center, Wroclaw University of Technology HSC/13/01, Hugo Steinhaus Center, Wroclaw University of Technology, revised 15 Apr 2013.
  17. Katarzyna Maciejowska & Jakub Nowotarski & Rafal Weron, 2014. "Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging," HSC Research Reports, Hugo Steinhaus Center, Wroclaw University of Technology HSC/14/09, Hugo Steinhaus Center, Wroclaw University of Technology.
  18. repec:eco:journ4:2014-01-4 is not listed on IDEAS
  19. Eran Raviv & Kees E. Bouwman & Dick van Dijk, 2013. "Forecasting Day-Ahead Electricity Prices: Utilizing Hourly Prices," Tinbergen Institute Discussion Papers 13-068/III, Tinbergen Institute.
  20. Panagiotelis, Anastasios & Smith, Michael, 2008. "Bayesian density forecasting of intraday electricity prices using multivariate skew t distributions," International Journal of Forecasting, Elsevier, Elsevier, vol. 24(4), pages 710-727.
  21. Panagiotelis, Anastasios & Smith, Michael, 2010. "Bayesian skew selection for multivariate models," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 54(7), pages 1824-1839, July.

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