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A long-term/short-term model for daily electricity prices with dynamic volatility

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  • Schlueter, Stephan
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    Abstract

    In this paper we introduce a new stochastic long-term/short-term model for short-term electricity prices, and apply it to four major European indices, namely to the German, Dutch, UK and Nordic one. We give evidence that all time series contain certain periodic (mostly annual) patterns, and show how to use the wavelet transform, a tool of multiresolution analysis, for filtering purpose. The wavelet transform is also applied to separate the long-term trend from the short-term oscillation in the seasonal-adjusted log-prices. In all time series we find evidence for dynamic volatility, which we incorporate by using a bivariate GARCH model with constant correlation. Eventually we fit various models from the existing literature to the data, and come to the conclusion that our approach performs best. For the error distribution, the Normal Inverse Gaussian distribution shows the best fit.

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    Bibliographic Info

    Article provided by Elsevier in its journal Energy Economics.

    Volume (Year): 32 (2010)
    Issue (Month): 5 (September)
    Pages: 1074-1081

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    Handle: RePEc:eee:eneeco:v:32:y:2010:i:5:p:1074-1081

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    Web page: http://www.elsevier.com/locate/eneco

    Related research

    Keywords: Electricity price model Wavelets Seasonal filter Relative wavelet energy Multivariate GARCH;

    References

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    1. Weron, Rafal & Misiorek, Adam, 2008. "Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models," International Journal of Forecasting, Elsevier, Elsevier, vol. 24(4), pages 744-763.
    2. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, Econometric Society, vol. 50(4), pages 987-1007, July.
    3. Alvaro Cartea & Marcelo_Gustavo Figueroa, 2005. "Pricing in Electricity Markets: a Mean Reverting Jump Diffusion Model with Seasonality," Finance, EconWPA 0501011, EconWPA, revised 10 Sep 2005.
    4. Makridakis, Spyros, 1993. "Accuracy measures: theoretical and practical concerns," International Journal of Forecasting, Elsevier, Elsevier, vol. 9(4), pages 527-529, December.
    5. M. T. Barlow, 2002. "A Diffusion Model For Electricity Prices," Mathematical Finance, Wiley Blackwell, Wiley Blackwell, vol. 12(4), pages 287-298.
    6. Serletis Apostolos & Shahmoradi Akbar, 2006. "Measuring and Testing Natural Gas and Electricity Markets Volatility: Evidence from Alberta's Deregulated Markets," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-20, September.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, Elsevier, vol. 31(3), pages 307-327, April.
    8. Eduardo Schwartz & James E. Smith, 2000. "Short-Term Variations and Long-Term Dynamics in Commodity Prices," Management Science, INFORMS, INFORMS, vol. 46(7), pages 893-911, July.
    9. Weidlich, Anke & Veit, Daniel, 2008. "A critical survey of agent-based wholesale electricity market models," Energy Economics, Elsevier, Elsevier, vol. 30(4), pages 1728-1759, July.
    10. 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.
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    Cited by:
    1. Jakub Nowotarski & Jakub Tomczyk & Rafal Weron, 2012. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," HSC Research Reports, Hugo Steinhaus Center, Wroclaw University of Technology HSC/12/06, Hugo Steinhaus Center, Wroclaw University of Technology.
    2. 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.
    3. Rafal Weron & Michal Zator, 2014. "A note on using the Hodrick-Prescott filter in electricity markets," HSC Research Reports, Hugo Steinhaus Center, Wroclaw University of Technology HSC/14/04, Hugo Steinhaus Center, Wroclaw University of Technology.
    4. 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, "Nicolae Titulescu" University of Bucharest, Faculty of Economic Sciences, vol. 1(2), pages 30-38, December.
    5. Sun, Qi & Xu, Weijun & Xiao, Weilin, 2013. "An empirical estimation for mean-reverting coal prices with long memory," Economic Modelling, Elsevier, vol. 33(C), pages 174-181.
    6. 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.
    7. Xu, Weijun & Sun, Qi & Xiao, Weilin, 2012. "A new energy model to capture the behavior of energy price processes," Economic Modelling, Elsevier, vol. 29(5), pages 1585-1591.

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