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Modeling highly volatile and seasonal markets: evidence from the Nord Pool electricity market

Author

Listed:
  • Rafal Weron

    (Hugo Steinhaus Center)

  • Ingve Simonsen

    (The Norwegian University of Science and Technology)

  • Piotr Wilman

    (Wroclaw University of Technology)

Abstract

In this paper we address the issue of modeling spot electricity prices. After analyzing factors leading to the unobservable in other financial or commodity markets price dynamics we propose a mean reverting jump diffusion model. We fit the model to data from the Nord Pool power exchange and find that it nearly duplicates the spot price's main characteristics. The model can thus be used for risk management and pricing derivatives written on the spot electricity price.

Suggested Citation

  • 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.
    • Handle: RePEc:wpa:wuwpem:0303007
    Note: Type of Document - PDF; prepared on IBM PC - PC-TEX; pages: 10 ; figures: 3 included. Appeared in "The Application of Econophysics", H. Takayasu (ed.), Springer, Tokyo, 2004, pp. 182-191.
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/em/papers/0303/0303007.pdf
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    References listed on IDEAS

    as
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    8. Weron, R. & Kozłowska, B. & Nowicka-Zagrajek, J., 2001. "Modeling electricity loads in California: a continuous-time approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 344-350.
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    Citations

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    Cited by:

    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. 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.
    3. 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.
    4. Rafal Weron, 2005. "Market price of risk implied by Asian-style electricity options," Econometrics 0502003, University Library of Munich, Germany.
    5. Abdou Kâ Diongue & Dominique Guegan, 2008. "The k-factor Gegenbauer asymmetric Power GARCH approach for modelling electricity spot price dynamics," Post-Print halshs-00259225, HAL.
    6. Michael Bierbrauer & Stefan Trueck & Rafal Weron, 2005. "Modeling electricity prices with regime switching models," Econometrics 0502005, University Library of Munich, Germany.
    7. Niels Haldrup & Oskar Knapik & Tommaso Proietti, 2016. "A generalized exponential time series regression model for electricity prices," CREATES Research Papers 2016-08, Department of Economics and Business Economics, Aarhus University.
    8. Benjamin R Auer, 2016. "Pure return persistence, Hurst exponents and hedge fund selection – A practical note," Journal of Asset Management, Palgrave Macmillan, vol. 17(5), pages 319-330, September.
    9. Simonsen, Ingve, 2005. "Volatility of power markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 10-20.
    10. 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.
    11. Parail, V., 2010. "Properties of Electricity Prices and the Drivers of Interconnector Revenue," Cambridge Working Papers in Economics 1059, Faculty of Economics, University of Cambridge.
    12. Serati, Massimiliano & Manera, Matteo & Plotegher, Michele, 2008. "Modeling Electricity Prices: From the State of the Art to a Draft of a New Proposal," International Energy Markets Working Papers 44426, Fondazione Eni Enrico Mattei (FEEM).
    13. Sandro Sapio, 2004. "Markets Design, Bidding Rules, and Long Memory in Electricity Prices," Revue d'Économie Industrielle, Programme National Persée, vol. 107(1), pages 151-170.
    14. Björn Lutz, 2010. "Pricing of Derivatives on Mean-Reverting Assets," Lecture Notes in Economics and Mathematical Systems, Springer, number 978-3-642-02909-7, July.
    15. 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.
    16. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    17. Hipòlit Torró & Julio Lucia, 2008. "Short-term electricity futures prices: Evidence on the time-varying risk premium," Working Papers. Serie EC 2008-08, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    18. Auer, Benjamin R., 2016. "On the performance of simple trading rules derived from the fractal dynamics of gold and silver price fluctuations," Finance Research Letters, Elsevier, vol. 16(C), pages 255-267.
    19. Josep Perello & Miquel Montero & Luigi Palatella & Ingve Simonsen & Jaume Masoliver, 2006. "Entropy of the Nordic electricity market: anomalous scaling, spikes, and mean-reversion," Papers physics/0609066, arXiv.org.
    20. Auer, Benjamin R., 2016. "On time-varying predictability of emerging stock market returns," Emerging Markets Review, Elsevier, vol. 27(C), pages 1-13.
    21. Martin Rypdal & Ola L{o}vsletten, 2012. "Modeling electricity spot prices using mean-reverting multifractal processes," Papers 1201.6137, arXiv.org.
    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.

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    More about this item

    Keywords

    electricity price; mean reversion; wavelet transform; jump diffusion model;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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