Using extreme value theory to measure value-at-risk for daily electricity spot prices

Chan, Kam Fong and Gray, Philip (2006) Using extreme value theory to measure value-at-risk for daily electricity spot prices. International Journal of Forecasting, 22 2: 283-300. doi:10.1016/j.ijforecast.2005.10.002


Author Chan, Kam Fong
Gray, Philip
Title Using extreme value theory to measure value-at-risk for daily electricity spot prices
Journal name International Journal of Forecasting   Check publisher's open access policy
ISSN 0169-2070
1872-8200
Publication date 2006-04
Sub-type Article (original research)
DOI 10.1016/j.ijforecast.2005.10.002
Volume 22
Issue 2
Start page 283
End page 300
Total pages 18
Editor R. J. Hyndman
Place of publication Amsterdam, Netherlands
Publisher Elsevier BV
Collection year 2006
Language eng
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. (c) 2005 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keyword Economics
Management
Extreme Value Theory
Value-at-risk
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes Journal issue: April-June 2006

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
2007 Higher Education Research Data Collection
UQ Business School Publications
 
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Created: Wed, 15 Aug 2007, 07:54:59 EST