Ruiqing Wang
School of Computer and Information Engineering, Anyang Normal University, Anyang, Henan 455000, China
ABSTRACT
How to effectively evaluate price of volatility risk is the basis of risk management in electricity market. An ARMAX-GARCH model imposing a skewedt-t distribution with time-varying skewness and degree of freedom over the error terms (ARMAX-GARCH-ST) is proposed and used to filter electricity price series in order to capture the dependencies, seasonalities, heteroscedasticities, skewnesses, leptokurtosises, volatility-clustering and relationship to system loads. In this way, an approximately independently and identically distributed residual series with better statistical properties is acquired. Then Extreme Value Theory (EVT) is adopted to explicitly model the tails of the normalized residuals of ARMAX-GARCH-ST model and accurate estimates of electricity market Value-at-Risk (VaR) can be produced. The empirical analysis shows that the ARMAX-GARCH-EVT models can be rapidly reflect the most recent and relevant changes of spot electricity prices and can produce accurate forecasts of VaR at all confidence levels, showing better dynamic characteristics. These results present several potential implications for electricity markets risk quantifications and hedging strategies.
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How to cite this article
Ruiqing Wang, 2013. Research on Risk Measure of Electricity Market Based on Armax-garch Model with Conditional Skewed-t Distribution and Extreme Value Theory. Information Technology Journal, 12: 6184-6190.
DOI: 10.3923/itj.2013.6184.6190
URL: https://scialert.net/abstract/?doi=itj.2013.6184.6190
DOI: 10.3923/itj.2013.6184.6190
URL: https://scialert.net/abstract/?doi=itj.2013.6184.6190
REFERENCES
- Bushnell, J., 2004. California's electricity crisis: a market apart? Energy Policy, 32: 1045-1052.
Direct Link - Bystrom, H.N.E., 2005. Extreme value theory and extremely large electricity price changes. Int. Rev. Econ. Finance, 14: 41-55.
CrossRef - Chan, K.F. and P. Gray, 2006. Using extreme value theory to measure value-at-risk for daily electricity spot prices. Int. J. Forecast., 2: 283-300.
CrossRefDirect Link - Coles, S., 2001. An Introduction to Statistical Modeling of Extreme Values. 1st Edn., Springer, New York, ISBN-13: 978-1852334598.
Direct Link - Gebizlioglu, O.L., B. Senoglu and Y.M. Kantar, 2011. Comparison of certain value-at-risk estimation methods for the two-parameter Weibull loss distribution. J. Comput. Applied Math., 235: 3304-3314.
CrossRefDirect Link - Gilli, M. and E. Kellezi, 2006. An application of extreme value theory for measuring financial risk. Comput. Econ., 27: 207-228.
CrossRef - Gong, X.S., X. Luo and J.J. Wu, 2009. Electricity auction market risk analysis based on EGARCH-EVT-CVaR model. Proceedings of the International Conference on Industrial Technology, February 10-13, 2009, Gippsland, VIC., pp: 1-5.
CrossRef - Hartz, C., S. Mittnik and M. Paolella, 2006. Accurate value-at-risk forecasting based on the normal-GARCH model. Comput. Stat. Data Anal., 51: 2295-2312.
CrossRefDirect Link - Huang, R.H., J. Zhang, L.Z. Zhang and Z.L. Li, 2009. Price risk forewarning of electricity market based on GARCH and VaR theory. Proc. Chaines Soc.Elect. Eng., 29: 85-91.
Direct Link - Kupiec, P., 1995. Techniques for verifying the accuracy of risk measurement models. J. Derivatives, 2: 174-184.
Direct Link - McNeil, A.J. and R. Frey, 2000. Estimation of tail-related risk measures for heteroscedastic financial time series: An extreme value approach. J. Empirical Finance, 7: 271-300.
CrossRef