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Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling

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  • Janczura, Joanna
  • Trück, Stefan
  • Weron, Rafał
  • Wolff, Rodney C.
Abstract
An important issue in fitting stochastic models to electricity spot prices is the estimation of a component to deal with trends and seasonality in the data. Unfortunately, estimation routines for the long-term and short-term seasonal pattern are usually quite sensitive to extreme observations, known as electricity price spikes. Improved robustness of the model can be achieved by (a) filtering the data with some reasonable procedure for outlier detection, and then (b) using estimation and testing procedures on the filtered data. In this paper we examine the effects of different treatments of extreme observations on model estimation and on determining the number of spikes (outliers). In particular we compare results for the estimation of the seasonal and stochastic components of electricity spot prices using either the original or filtered data. We find significant evidence for a superior estimation of both the seasonal short-term and long-term components when the data have been treated carefully for outliers. Overall, our findings point out the substantial impact the treatment of extreme observations may have on these issues and, therefore, also on the pricing of electricity derivatives like futures and option contracts. An added value of our study is the ranking of different filtering techniques used in the energy economics literature, suggesting which methods could be and which should not be used for spike identification.

Suggested Citation

  • Janczura, Joanna & Trück, Stefan & Weron, Rafał & Wolff, Rodney C., 2013. "Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling," Energy Economics, Elsevier, vol. 38(C), pages 96-110.
    • Handle: RePEc:eee:eneeco:v:38:y:2013:i:c:p:96-110
    DOI: 10.1016/j.eneco.2013.03.013
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    1. Weron, R & Bierbrauer, M & Trück, S, 2004. "Modeling electricity prices: jump diffusion and regime switching," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(1), pages 39-48.
    2. Max Stevenson, 2001. "Filtering and Forecasting Spot Electricity Prices in the Increasingly Deregulated Australian Electricity Market," Research Paper Series 63, Quantitative Finance Research Centre, University of Technology, Sydney.
    3. Koopman, Siem Jan & Ooms, Marius & Carnero, M. Angeles, 2007. "Periodic Seasonal Reg-ARFIMAGARCH Models for Daily Electricity Spot Prices," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 16-27, March.
    4. Kanamura, Takashi & O[combining macron]hashi, Kazuhiko, 2008. "On transition probabilities of regime switching in electricity prices," Energy Economics, Elsevier, vol. 30(3), pages 1158-1172, May.
    5. Rafał Weron, 2009. "Heavy-tails and regime-switching in electricity prices," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 69(3), pages 457-473, July.
    6. Mount, Timothy D. & Ning, Yumei & Cai, Xiaobin, 2006. "Predicting price spikes in electricity markets using a regime-switching model with time-varying parameters," Energy Economics, Elsevier, vol. 28(1), pages 62-80, January.
    7. Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September.
    8. 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, vol. 10(3), pages 1-36, September.
    9. Nomikos, Nikos K. & Soldatos, Orestes A., 2010. "Analysis of model implied volatility for jump diffusion models: Empirical evidence from the Nordpool market," Energy Economics, Elsevier, vol. 32(2), pages 302-312, March.
    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, vol. 31(11), pages 3462-3485, November.
    11. Haldrup, Niels & Nielsen, Frank S. & Nielsen, Morten Ørregaard, 2010. "A vector autoregressive model for electricity prices subject to long memory and regime switching," Energy Economics, Elsevier, vol. 32(5), pages 1044-1058, September.
    12. De Jong Cyriel, 2006. "The Nature of Power Spikes: A Regime-Switch Approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-28, September.
    13. Helyette Geman & A. Roncoroni, 2006. "Understanding the Fine Structure of Electricity Prices," Post-Print halshs-00144198, HAL.
    14. Jakub Nowotarski & Jakub Tomczyk & Rafal Weron, 2013. "Modeling and forecasting of the long-term seasonal component of the EEX and Nord Pool spot prices," HSC Research Reports HSC/13/02, Hugo Steinhaus Center, Wroclaw University of Technology.
    15. Nowotarski, Jakub & Tomczyk, Jakub & Weron, Rafał, 2013. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," Energy Economics, Elsevier, vol. 39(C), pages 13-27.
    16. Simonsen, Ingve, 2005. "Volatility of power markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 10-20.
    17. Erlwein, Christina & Benth, Fred Espen & Mamon, Rogemar, 2010. "HMM filtering and parameter estimation of an electricity spot price model," Energy Economics, Elsevier, vol. 32(5), pages 1034-1043, September.
    18. Alvaro Cartea & Marcelo Figueroa, 2005. "Pricing in Electricity Markets: A Mean Reverting Jump Diffusion Model with Seasonality," Applied Mathematical Finance, Taylor & Francis Journals, vol. 12(4), pages 313-335.
    19. Rafal Weron & Ingve Simonsen & Piotr Wilman, 2003. "Modeling highly volatile and seasonal markets: evidence from the Nord Pool electricity market," Econometrics 0303007, University Library of Munich, Germany.
    20. M. T. Barlow, 2002. "A Diffusion Model For Electricity Prices," Mathematical Finance, Wiley Blackwell, vol. 12(4), pages 287-298, October.
    21. Adam Misiorek & Rafal Weron, 2006. "Interval forecasting of spot electricity prices," HSC Research Reports HSC/06/05, Hugo Steinhaus Center, Wroclaw University of Technology.
    22. Jan Seifert & Marliese Uhrig-Homburg, 2007. "Modelling jumps in electricity prices: theory and empirical evidence," Review of Derivatives Research, Springer, vol. 10(1), pages 59-85, January.
    23. Ben Hambly & Sam Howison & Tino Kluge, 2009. "Modelling spikes and pricing swing options in electricity markets," Quantitative Finance, Taylor & Francis Journals, vol. 9(8), pages 937-949.
    24. Unknown, 2005. "Forward," 2005 Conference: Slovenia in the EU - Challenges for Agriculture, Food Science and Rural Affairs, November 10-11, 2005, Moravske Toplice, Slovenia 183804, Slovenian Association of Agricultural Economists (DAES).
    25. Joanna Janczura & Rafał Weron, 2012. "Efficient estimation of Markov regime-switching models: An application to electricity spot prices," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(3), pages 385-407, July.
    26. Fanone, Enzo & Gamba, Andrea & Prokopczuk, Marcel, 2013. "The case of negative day-ahead electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 22-34.
    27. Merton, Robert C., 1976. "Option pricing when underlying stock returns are discontinuous," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 125-144.
    28. Weron, Rafal, 2008. "Market price of risk implied by Asian-style electricity options and futures," Energy Economics, Elsevier, vol. 30(3), pages 1098-1115, May.
    29. Redl, Christian & Haas, Reinhard & Huber, Claus & Böhm, Bernhard, 2009. "Price formation in electricity forward markets and the relevance of systematic forecast errors," Energy Economics, Elsevier, vol. 31(3), pages 356-364, May.
    30. Kim, Chang-Jin, 1994. "Dynamic linear models with Markov-switching," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 1-22.
    31. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70.
    32. repec:dau:papers:123456789/1433 is not listed on IDEAS
    33. Weron, Rafal & Janczura, Joanna, 2010. "Efficient estimation of Markov regime-switching models: An application to electricity wholesale market prices," MPRA Paper 26628, University Library of Munich, Germany.
    34. Huisman, Ronald & Mahieu, Ronald, 2003. "Regime jumps in electricity prices," Energy Economics, Elsevier, vol. 25(5), pages 425-434, September.
    35. Janczura, Joanna & Weron, Rafal, 2010. "An empirical comparison of alternate regime-switching models for electricity spot prices," Energy Economics, Elsevier, vol. 32(5), pages 1059-1073, September.
    36. Hélyette Geman & Andrea Roncoroni, 2006. "Understanding the Fine Structure of Electricity Prices," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1225-1262, May.
    37. Higgs, Helen & Worthington, Andrew, 2008. "Stochastic price modeling of high volatility, mean-reverting, spike-prone commodities: The Australian wholesale spot electricity market," Energy Economics, Elsevier, vol. 30(6), pages 3172-3185, November.
    38. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601, December.
    39. Trueck, Stefan & Weron, Rafal & Wolff, Rodney, 2007. "Outlier Treatment and Robust Approaches for Modeling Electricity Spot Prices," MPRA Paper 4711, University Library of Munich, Germany.
    40. Ralf Becker & Stan Hurn & Vlad Pavlov, 2007. "Modelling Spikes in Electricity Prices," The Economic Record, The Economic Society of Australia, vol. 83(263), pages 371-382, December.
    41. Michael Bierbrauer & Stefan Trueck & Rafal Weron, 2005. "Modeling electricity prices with regime switching models," Econometrics 0502005, University Library of Munich, Germany.
    42. Karakatsani, Nektaria V. & Bunn, Derek W., 2008. "Intra-day and regime-switching dynamics in electricity price formation," Energy Economics, Elsevier, vol. 30(4), pages 1776-1797, July.
    43. Stevenson Maxwell J & Moreira do Amaral Luiz Felipe & Peat Maurice, 2006. "Risk Management and the Role of Spot Price Predictions in the Australian Retail Electricity Market," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-25, September.
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    More about this item

    Keywords

    Electricity spot price; Outlier treatment; Price spike; Robust modeling; Seasonality;
    All these keywords.

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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