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Heavy tails and electricity prices

Author

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  • Rafal Weron

Abstract

In the first years after the emergence of deregulated power markets it became apparent that for the valuation of electricity derivatives we cannot simply rely on models developed for financial or other commodity markets. However, before adequate models can be put forward the unique characteristics of electricity (spot) prices have to be thoroughly analyzed. In particular, the extreme volatility and price spikes which lead to heavy-tailed distributions of returns. In this paper we first analyze the stylized facts of electricity prices, then present two modeling approaches: jump-diffusion and regime-switching, which to some extent address the pertinent issues.

Suggested Citation

  • Rafal Weron, 2005. "Heavy tails and electricity prices," HSC Research Reports HSC/05/02, Hugo Steinhaus Center, Wroclaw University of Technology.
    • Handle: RePEc:wuu:wpaper:hsc0502
    as

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    File URL: http://www.im.pwr.wroc.pl/~hugo/RePEc/wuu/wpaper/HSC_05_02.pdf
    File Function: Original version, 2005
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    References listed on IDEAS

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    1. Anna Chernobai & Krzysztof Burnecki & Svetlozar Rachev & Stefan Trueck & Rafal Weron, 2005. "Modeling catastrophe claims with left-truncated severity distributions (extended version)," HSC Research Reports HSC/05/01, Hugo Steinhaus Center, Wroclaw University of Technology.
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    Citations

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    Cited by:

    1. Lisi, Francesco & Pelagatti, Matteo M., 2018. "Component estimation for electricity market data: Deterministic or stochastic?," Energy Economics, Elsevier, vol. 74(C), pages 13-37.
    2. Deschatre, Thomas & Féron, Olivier & Gruet, Pierre, 2021. "A survey of electricity spot and futures price models for risk management applications," Energy Economics, Elsevier, vol. 102(C).
    3. Radu Porumb & Petru Postolache & George Serițan & Ramona Vatu & Oana Ceaki, 2013. "Load profiles analysis for electricity market," Computational Methods in Social Sciences (CMSS), "Nicolae Titulescu" University of Bucharest, Faculty of Economic Sciences, vol. 1(2), pages 30-38, December.
    4. Sandro Sapio, 2012. "Modeling the distribution of day-ahead electricity returns: a comparison," Quantitative Finance, Taylor & Francis Journals, vol. 12(12), pages 1935-1949, December.
    5. Moreno, Manuel & Serrano, Pedro & Stute, Winfried, 2011. "Statistical properties and economic implications of jump-diffusion processes with shot-noise effects," European Journal of Operational Research, Elsevier, vol. 214(3), pages 656-664, November.
    6. Ewa Broszkiewicz-Suwaj & Aleksander Weron, 2005. "Calibration of the multifactor HJM model for energy market," HSC Research Reports HSC/05/03, Hugo Steinhaus Center, Wroclaw University of Technology.
    7. Thilo Meyer-Brandis & Peter Tankov, 2008. "Multi-Factor Jump-Diffusion Models Of Electricity Prices," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 11(05), pages 503-528.
    8. Weron, Rafal & Misiorek, Adam, 2007. "Heavy tails and electricity prices: Do time series models with non-Gaussian noise forecast better than their Gaussian counterparts?," MPRA Paper 2292, University Library of Munich, Germany, revised Oct 2007.
    9. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
    10. Frestad, Dennis, 2008. "Common and unique factors influencing daily swap returns in the Nordic electricity market, 1997-2005," Energy Economics, Elsevier, vol. 30(3), pages 1081-1097, May.
    11. Palzer, Andreas & Westner, Günther & Madlener, Reinhard, 2013. "Evaluation of different hedging strategies for commodity price risks of industrial cogeneration plants," Energy Policy, Elsevier, vol. 59(C), pages 143-160.
    12. Lisi, Francesco & Nan, Fany, 2014. "Component estimation for electricity prices: Procedures and comparisons," Energy Economics, Elsevier, vol. 44(C), pages 143-159.
    13. Thomas Deschatre & Olivier F'eron & Pierre Gruet, 2021. "A survey of electricity spot and futures price models for risk management applications," Papers 2103.16918, arXiv.org, revised Jul 2021.
    14. Tat Andrejus Ngujen, 2018. "Electricity Price Forecasting Using Monte Carlo Simulation: The Case of Lithuania," Ekonomika (Economics), Sciendo, vol. 97(1), pages 76-86, January.

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    More about this item

    Keywords

    Heavy-tailed distribution; Electricity spot price; Seasonality; Volatility; Price spike;
    All these keywords.

    JEL classification:

    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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