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The performance of composite forecast models of value-at-risk in the energy market

  • Chiu, Yen-Chen
  • Chuang, I-Yuan
  • Lai, Jing-Yi
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    This paper examines a comparative evaluation of the predictive performance of various Value-at-Risk (VaR) models in the energy market. This study extends the conventional research in literature, by proposing composite forecast models for applying to Brent and WTI crude oil prices. Forecasting techniques considered here include the EWMA, stable density, Kernel density, Hull and White, GARCH-GPD, plus composite forecasts from linearly combining two or more of the competing models above. Findings show Hull and White to be the most powerful approach for capturing downside risk in the energy market. Reasonable results are also available from carefully combining VaR forecasts.

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    Article provided by Elsevier in its journal Energy Economics.

    Volume (Year): 32 (2010)
    Issue (Month): 2 (March)
    Pages: 423-431

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    Handle: RePEc:eee:eneeco:v:32:y:2010:i:2:p:423-431
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    1. Jose A. Lopez, 1998. "Methods for evaluating value-at-risk estimates," Economic Policy Review, Federal Reserve Bank of New York, issue Oct, pages 119-124.
    2. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    3. Sadorsky, Perry, 2003. "The macroeconomic determinants of technology stock price volatility," Review of Financial Economics, Elsevier, vol. 12(2), pages 191-205.
    4. Sadorsky, Perry, 1999. "Oil price shocks and stock market activity," Energy Economics, Elsevier, vol. 21(5), pages 449-469, October.
    5. GIOT, Pierre & LAURENT, Sébastien, 2003. "Market risk in commodity markets: a VaR approach," CORE Discussion Papers 2003028, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
    7. David Cabedo, J. & Moya, Ismael, 2003. "Estimating oil price 'Value at Risk' using the historical simulation approach," Energy Economics, Elsevier, vol. 25(3), pages 239-253, May.
    8. Hibon, Michele & Evgeniou, Theodoros, 2005. "To combine or not to combine: selecting among forecasts and their combinations," International Journal of Forecasting, Elsevier, vol. 21(1), pages 15-24.
    9. Hung, Jui-Cheng & Lee, Ming-Chih & Liu, Hung-Chun, 2008. "Estimation of value-at-risk for energy commodities via fat-tailed GARCH models," Energy Economics, Elsevier, vol. 30(3), pages 1173-1191, May.
    10. Benoit Mandelbrot, 1963. "The Variation of Certain Speculative Prices," The Journal of Business, University of Chicago Press, vol. 36, pages 394.
    11. Susan Thomas & Mandira Sarma & Ajay Shah, 2003. "Selection of Value-at-Risk models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 337-358.
    12. 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.
    13. Shawkat Hammoudeh & Eisa Aleisa, 2004. "Dynamic Relationships among GCC Stock Markets and Nymex Oil Futures," Contemporary Economic Policy, Western Economic Association International, vol. 22(2), pages 250-269, 04.
    14. Papapetrou, Evangelia, 2001. "Oil price shocks, stock market, economic activity and employment in Greece," Energy Economics, Elsevier, vol. 23(5), pages 511-532, September.
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