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A new approach to characterizing and forecasting electricity price volatility

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  • Chan, Kam Fong
  • Gray, Philip
  • van Campen, Bart
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    Abstract

    There is a growing need to model the dynamics of electricity spot prices. While many studies have adopted the jump-diffusion model used successfully in traditional financial markets, the distinctive features of energy prices present non-trivial challenges. In particular, electricity price series feature extreme jumps of magnitudes rarely seen in financial markets, and occurring at greater frequency. Standard parametric approaches to estimating jump-diffusion models struggle to disentangle the jump and non-jump variation. This paper explores a recently-developed approach to separating the total variation into jump and non-jump components. Using quadratic variation theory, we non-parametrically estimate jump parameters for five power markets which are known to feature some important physical differences. The unique characteristics of the jump and non-jump components of the total variation are studied for each market. Given the evidence that the two sources of variation in spot prices have distinct dynamics, the paper explores whether volatility forecasts can be improved by explicitly incorporating the jump and non-jump components of the total variation.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 24 (2008)
    Issue (Month): 4 ()
    Pages: 728-743

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    Handle: RePEc:eee:intfor:v:24:y:2008:i:4:p:728-743

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

    Related research

    Keywords: Realized volatility Bipower variation Quadratic variation Jumps Volatility forecast;

    References

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    4. Taylor, James W. & de Menezes, Lilian M. & McSharry, Patrick E., 2006. "A comparison of univariate methods for forecasting electricity demand up to a day ahead," International Journal of Forecasting, Elsevier, vol. 22(1), pages 1-16.
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    22. Panagiotelis, Anastasios & Smith, Michael, 2008. "Bayesian density forecasting of intraday electricity prices using multivariate skew t distributions," International Journal of Forecasting, Elsevier, vol. 24(4), pages 710-727.
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    Citations

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    Cited by:
    1. Eichler, M. & Türk, D., 2013. "Fitting semiparametric Markov regime-switching models to electricity spot prices," Energy Economics, Elsevier, vol. 36(C), pages 614-624.
    2. Ullrich, Carl J., 2012. "Realized volatility and price spikes in electricity markets: The importance of observation frequency," Energy Economics, Elsevier, vol. 34(6), pages 1809-1818.
    3. Christensen, T.M. & Hurn, A.S. & Lindsay, K.A., 2012. "Forecasting spikes in electricity prices," International Journal of Forecasting, Elsevier, vol. 28(2), pages 400-411.
    4. Eichler Michael & Grothe Oliver & Tuerk Dennis & Manner Hans, 2012. "Modeling spike occurrences in electricity spot prices for forecasting," Research Memorandum 029, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    5. Narayan, Paresh Kumar & Wong, Philip, 2009. "A panel data analysis of the determinants of oil consumption: The case of Australia," Applied Energy, Elsevier, vol. 86(12), pages 2771-2775, December.
    6. Weron, Rafal & Misiorek, Adam, 2008. "Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 744-763.
    7. Haugom, Erik & Ullrich, Carl J., 2012. "Forecasting spot price volatility using the short-term forward curve," Energy Economics, Elsevier, vol. 34(6), pages 1826-1833.

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