Using Extreme Value Theory to Measure Value-at-Risk for Daily Electricity Spot Prices

Posted: 15 Aug 2009

See all articles by Kam Fong Chan

Kam Fong Chan

The University of Western Australia; Financial Research Network (FIRN)

Philip Gray

Department of Banking and Finance, Monash University

Date Written: August 13, 2009

Abstract

The recent deregulation in electricity markets worldwide has heightened the importance of risk management in energy markets. Assessing Value-at-Risk (VaR) in electricity markets is arguably more difficult than in traditional financial markets because the distinctive features of the former result in a highly unusual distribution of returns-electricity returns are highly volatile, display seasonalities in both their mean and volatility, exhibit leverage effects and clustering in volatility, and feature extreme levels of skewness and kurtosis. With electricity applications in mind, this paper proposes a model that accommodates autoregression and weekly seasonals in both the conditional mean and conditional volatility of returns, as well as leverage effects via an EGARCH specification. In addition, extreme value theory (EVT) is adopted to explicitly model the tails of the return distribution. Compared to a number of other parametric models and simple historical simulation based approaches, the proposed EVT-based model performs well in forecasting out-of-sample VaR. In addition, statistical tests show that the proposed model provides appropriate interval coverage in both unconditional and, more importantly, conditional contexts. Overall, the results are encouraging in suggesting that the proposed EVT-based model is a useful technique in forecasting VaR in electricity markets.

Keywords: Extreme value theory, Value-at-risk, Electricity, EGARCH, Conditional interval coverage

JEL Classification: C14, C16, C53, G11

Suggested Citation

Chan, Kam Fong and Gray, Philip, Using Extreme Value Theory to Measure Value-at-Risk for Daily Electricity Spot Prices (August 13, 2009). International Journal of Forecasting, Vol. 22, No. 2, 2006. Available at SSRN: https://ssrn.com/abstract=1448565

Kam Fong Chan (Contact Author)

The University of Western Australia ( email )

35 Stirling Highway
Crawley, Western Australia 6009
Australia

Financial Research Network (FIRN) ( email )

C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia

Philip Gray

Department of Banking and Finance, Monash University ( email )

Building H
Caulfield, Victoria 3141
Australia

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