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Tail risk of electricity futures

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  • Peña, Juan Ignacio
  • Rodríguez, Rosa
  • Mayoral, Silvia

Abstract

This paper compares the in-sample and out-of-sample performance of several models for computing the tail risk of one-month and one-year electricity futures contracts traded in the NordPool, French, German, and Spanish markets in 2008–2017. As measures of tail risk, we use the one-day-ahead Value-at-Risk (VaR) and the Expected Shortfall (ES). With VaR, the AR (1)-GARCH (1,1) model with Student-t distribution is the best-performing specification with 88% cases in which the Fisher test accepts the model, with a success rate of 94% in the left tail and of 81% in the right tail. The model passes the test of model adequacy in the 100% of the cases in the NordPool and German markets, but only in the 88% and 63% of the cases in the Spanish and French markets. With ES, this model passes the test of model adequacy in 100% of cases in all markets. Historical Simulation and Quantile Regression-based approaches misestimate tail risks. The right-hand tail of the returns is more difficult to model than the left-hand tail and therefore financial regulators and the administrators of futures markets should take these results into account when setting additional regulatory capital requirements and margin account regulations to short positions.

Suggested Citation

  • Peña, Juan Ignacio & Rodríguez, Rosa & Mayoral, Silvia, 2020. "Tail risk of electricity futures," Energy Economics, Elsevier, vol. 91(C).
    • Handle: RePEc:eee:eneeco:v:91:y:2020:i:c:s0140988320302267
    DOI: 10.1016/j.eneco.2020.104886
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    1. James W. Taylor, 2008. "Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall," Journal of Financial Econometrics, Oxford University Press, vol. 6(3), pages 382-406, Summer.
    2. Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September.
    3. Lars Ivar Hagfors & Hilde Hørthe Kamperud & Florentina Paraschiv & Marcel Prokopczuk & Alma Sator & Sjur Westgaard, 2016. "Prediction of extreme price occurrences in the German day-ahead electricity market," Quantitative Finance, Taylor & Francis Journals, vol. 16(12), pages 1929-1948, December.
    4. Boroumand, Raphaël Homayoun & Goutte, Stéphane & Porcher, Simon & Porcher, Thomas, 2015. "Hedging strategies in energy markets: The case of electricity retailers," Energy Economics, Elsevier, vol. 51(C), pages 503-509.
    5. 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, vol. 22(2), pages 283-300.
    6. Vehvilainen, Iivo & Keppo, Jussi, 2003. "Managing electricity market price risk," European Journal of Operational Research, Elsevier, vol. 145(1), pages 136-147, February.
    7. Fred Espen Benth & Jūratė Šaltytė Benth & Steen Koekebakker, 2008. "Stochastic Modeling of Electricity and Related Markets," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 6811, August.
    8. 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, vol. 39(4), pages 841-862, November.
    9. Pérignon, Christophe & Smith, Daniel R., 2010. "The level and quality of Value-at-Risk disclosure by commercial banks," Journal of Banking & Finance, Elsevier, vol. 34(2), pages 362-377, February.
    10. Hagfors, Lars Ivar & Bunn, Derek & Kristoffersen, Eline & Staver, Tiril Toftdahl & Westgaard, Sjur, 2016. "Modeling the UK electricity price distributions using quantile regression," Energy, Elsevier, vol. 102(C), pages 231-243.
    11. González-Pedraz, Carlos & Moreno, Manuel & Peña, Juan Ignacio, 2014. "Tail risk in energy portfolios," Energy Economics, Elsevier, vol. 46(C), pages 422-434.
    12. Acerbi, Carlo & Tasche, Dirk, 2002. "On the coherence of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1487-1503, July.
    13. Alvaro Escribano & J. Ignacio Peña & Pablo Villaplana, 2011. "Modelling Electricity Prices: International Evidence," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(5), pages 622-650, October.
    14. Sanda, Gaute Egeland & Olsen, Eirik Tandberg & Fleten, Stein-Erik, 2013. "Selective hedging in hydro-based electricity companies," Energy Economics, Elsevier, vol. 40(C), pages 326-338.
    15. Susanne Emmer & Marie Kratz & Dirk Tasche, 2013. "What is the best risk measure in practice? A comparison of standard measures," Papers 1312.1645, arXiv.org, revised Apr 2015.
    16. Yamai, Yasuhiro & Yoshiba, Toshinao, 2002. "Comparative Analyses of Expected Shortfall and Value-at-Risk: Their Estimation Error, Decomposition, and Optimization," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 20(1), pages 87-121, January.
    17. Boroumand, Raphaël Homayoun & Goutte, Stéphane & Porcher, Simon & Porcher, Thomas, 2015. "Hedging strategies in energy markets: The case of electricity retailers," Energy Economics, Elsevier, vol. 51(C), pages 503-509.
    18. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    19. Deng, S.J. & Oren, S.S., 2006. "Electricity derivatives and risk management," Energy, Elsevier, vol. 31(6), pages 940-953.
    20. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
    21. Pritsker, Matthew, 2006. "The hidden dangers of historical simulation," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 561-582, February.
    22. Fanone, Enzo & Gamba, Andrea & Prokopczuk, Marcel, 2013. "The case of negative day-ahead electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 22-34.
    23. Paraschiv, Florentina & Erni, David & Pietsch, Ralf, 2014. "The impact of renewable energies on EEX day-ahead electricity prices," Energy Policy, Elsevier, vol. 73(C), pages 196-210.
    24. Zaichao Du & Juan Carlos Escanciano, 2017. "Backtesting Expected Shortfall: Accounting for Tail Risk," Management Science, INFORMS, vol. 63(4), pages 940-958, April.
    25. Iván Blanco, Juan Ignacio Peña, and Rosa Rodriguez, 2018. "Modelling Electricity Swaps with Stochastic Forward Premium Models," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
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    More about this item

    Keywords

    Electricity markets; Futures markets; Value-at-risk; Expected shortfall; Backtesting;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General

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