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Forecasting extreme electricity spot prices

  • Volodymyr Korniichuk
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    We propose a model for forecasting extreme electricity prices in real time (high frequency) settings. The unique feature of our model is its ability to forecast electricity price exceedances over very high thresholds, where only a few (if any) observations are available. The model can also be applied for simulating times of occurrence and magnitudes of the extreme prices. We employ a copula with a changing dependence parameter for capturing serial dependence in the extreme prices and the censored GPD for modelling their marginal distributions. For modelling times of the extreme price occurrences we propose an approach based on a negative binomial distribution. The model is applied to electricity spot prices from Australia's national electricity market.

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    File URL: http://www.cgs.uni-koeln.de/fileadmin/wiso_fak/cgs/pdf/working_paper/cgswp_03-14.pdf
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    Paper provided by Cologne Graduate School in Management, Economics and Social Sciences in its series Cologne Graduate School Working Paper Series with number 03-14.

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    Date of creation: 27 Dec 2012
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    Handle: RePEc:cgr:cgsser:03-14
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    Web page: http://www.cgs.uni-koeln.de/
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    1. 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).
    2. Clive G. Bowsher, 2005. "Modelling Security Market Events in Continuous Time: Intensity Based, Multivariate Point Process Models," Economics Papers 2005-W26, Economics Group, Nuffield College, University of Oxford.
    3. Timothy Christensen & Stan Hurn & Kenneth Lindsay, 2009. "It Never Rains but it Pours: Modeling the Persistence of Spikes in Electricity Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 25-48.
    4. V. Chavez-Demoulin & A. C. Davison & A. J. McNeil, 2005. "Estimating value-at-risk: a point process approach," Quantitative Finance, Taylor & Francis Journals, vol. 5(2), pages 227-234.
    5. repec:sae:ecolab:v:16:y:2006:i:2:p:1-2 is not listed on IDEAS
    6. Benoit Mandelbrot, 1963. "The Variation of Certain Speculative Prices," The Journal of Business, University of Chicago Press, vol. 36, pages 394.
    7. Diebold, Francis X & Gunther, Todd A & Tay, Anthony S, 1998. "Evaluating Density Forecasts with Applications to Financial Risk Management," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 863-83, November.
    8. Yacine Aït-Sahalia & Julio Cacho-Diaz & Roger J.A. Laeven, 2010. "Modeling Financial Contagion Using Mutually Exciting Jump Processes," NBER Working Papers 15850, National Bureau of Economic Research, Inc.
    9. Richard A. Davis & Rongning Wu, 2009. "A negative binomial model for time series of counts," Biometrika, Biometrika Trust, vol. 96(3), pages 735-749.
    10. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    11. Ralf Becker & Stan Hurn & Vlad Pavlov, 2007. "Modelling Spikes in Electricity Prices," The Economic Record, The Economic Society of Australia, vol. 83(263), pages 371-382, December.
    12. Joachim Grammig & Kai-Oliver Maurer, 2000. "Non-monotonic hazard functions and the autoregressive conditional duration model," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 16-38.
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