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Extreme Value Theory and Extremely Large Electricity Price Changes

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Abstract

Nord Pool, the first multinational exchange for electricity trading, has existed since January 1996. Typical characteristics of electricity prices on Nord Pool are a very high volatility and a large number of very large, or extreme, price changes. In this paper we look at hourly spot prices on NordPool and apply extreme value theory to investigate the tails of the price change distribution. We find a good fit of both the generalized extreme value distribution and the generalized Pareto distribution to AR-GARCH filtered price change series, and accurate estimates as well as forecasts of extreme quantiles are produced. Generally, our results suggest extreme value theory to be of interest to both risk managers and portfolio managers in the highly volatile electricity market.

Suggested Citation

  • Byström, Hans, 2001. "Extreme Value Theory and Extremely Large Electricity Price Changes," Working Papers 2001:19, Lund University, Department of Economics.
  • Handle: RePEc:hhs:lunewp:2001_019
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    1. 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.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Evis Këllezi & Manfred Gilli, 2000. "Extreme Value Theory for Tail-Related Risk Measures," FAME Research Paper Series rp18, International Center for Financial Asset Management and Engineering.
    4. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    More about this item

    Keywords

    electricity prices; conditional extreme value theory; GARCH; tail quantiles;
    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G19 - Financial Economics - - General Financial Markets - - - Other
    • Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other

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