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Skewness and leptokurtosis in GARCH-typed VaR estimation of petroleum and metal asset returns

  • Cheng, Wan-Hsiu
  • Hung, Jui-Cheng
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    This paper utilizes the most flexible skewed generalized t (SGT) distribution for describing petroleum and metal volatilities that are characterized by leptokurtosis and skewness in order to provide better approximations of the reality. The empirical results indicate that the forecasted Value-at-Risk (VaR) obtained using the SGT distribution provides the most accurate out-of-sample forecasts for both the petroleum and metal markets. With regard to the unconditional and conditional coverage tests, the SGT distribution produces the most appropriate VaR estimates in terms of the total number of rejections; this is followed by the nonparametric distribution, generalized error distribution (GED), and finally the normal distribution. Similarly, in the dynamic quantile test, the VaR estimates generated by the SGT and nonparametric distributions perform better than that generated by other distributions. Finally, in the superior predictive test, the SGT distribution has significantly lower capital requirements than the nonparametric distribution for most commodities.

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    Article provided by Elsevier in its journal Journal of Empirical Finance.

    Volume (Year): 18 (2011)
    Issue (Month): 1 (January)
    Pages: 160-173

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    Handle: RePEc:eee:empfin:v:18:y:2011:i:1:p:160-173
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    1. Engle, Robert F & Manganelli, Simone, 1999. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," University of California at San Diego, Economics Working Paper Series qt06m3d6nv, Department of Economics, UC San Diego.
    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. L.A. Sjaastad & F. Scacciavillani, 1995. "The Price of Gold and the Exchange Rates," Economics Discussion / Working Papers 95-14, The University of Western Australia, Department of Economics.
    4. Richard D. F. Harris & C. Coskun Küçüközmen, 2001. "The Empirical Distribution of UK and US Stock Returns," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 28(5-6), pages 715-740.
    5. Sadeghi, Mehdi & Shavvalpour, Saeed, 2006. "Energy risk management and value at risk modeling," Energy Policy, Elsevier, vol. 34(18), pages 3367-3373, December.
    6. Regnier, Eva, 2007. "Oil and energy price volatility," Energy Economics, Elsevier, vol. 29(3), pages 405-427, May.
    7. Pierre Giot and S»bastien Laurent, 2001. "Value-At-Risk For Long And Short Trading Positions," Computing in Economics and Finance 2001 94, Society for Computational Economics.
    8. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
    9. 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.
    10. Plourde, André & Watkins, G. C., 1998. "Crude oil prices between 1985 and 1994: how volatile in relation to other commodities?," Resource and Energy Economics, Elsevier, vol. 20(3), pages 245-262, September.
    11. Kaufmann, Thomas D. & Winters, Richard A., 1989. "The price of gold : A simple model," Resources Policy, Elsevier, vol. 15(4), pages 309-313, December.
    12. Hansen, Bruce E, 1994. "Autoregressive Conditional Density Estimation," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-30, August.
    13. Pritsker, Matthew, 2006. "The hidden dangers of historical simulation," Journal of Banking & Finance, Elsevier, vol. 30(2), pages 561-582, February.
    14. 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.
    15. 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).
    16. Richard Harris & C. Coskun Kucukozmen & Fatih Yilmaz, 2004. "Skewness in the conditional distribution of daily equity returns," Applied Financial Economics, Taylor & Francis Journals, vol. 14(3), pages 195-202.
    17. Nicholas Taylor, 1998. "Precious metals and inflation," Applied Financial Economics, Taylor & Francis Journals, vol. 8(2), pages 201-210.
    18. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    19. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    20. Sadorsky, Perry, 1999. "Oil price shocks and stock market activity," Energy Economics, Elsevier, vol. 21(5), pages 449-469, October.
    21. Panayiotis Theodossiou, 1998. "Financial Data and the Skewed Generalized T Distribution," Management Science, INFORMS, vol. 44(12-Part-1), pages 1650-1661, December.
    22. Kausik Chaudhuri, 2001. "Long-run prices of primary commodities and oil prices," Applied Economics, Taylor & Francis Journals, vol. 33(4), pages 531-538.
    23. Solt, Michael E & Swanson, Paul J, 1981. "On the Efficiency of the Markets for Gold and Silver," The Journal of Business, University of Chicago Press, vol. 54(3), pages 453-78, July.
    24. 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.
    25. Bali, Turan G. & Mo, Hengyong & Tang, Yi, 2008. "The role of autoregressive conditional skewness and kurtosis in the estimation of conditional VaR," Journal of Banking & Finance, Elsevier, vol. 32(2), pages 269-282, February.
    26. Hang Chan, Ngai & Deng, Shi-Jie & Peng, Liang & Xia, Zhendong, 2007. "Interval estimation of value-at-risk based on GARCH models with heavy-tailed innovations," Journal of Econometrics, Elsevier, vol. 137(2), pages 556-576, April.
    27. Fan, Ying & Zhang, Yue-Jun & Tsai, Hsien-Tang & Wei, Yi-Ming, 2008. "Estimating 'Value at Risk' of crude oil price and its spillover effect using the GED-GARCH approach," Energy Economics, Elsevier, vol. 30(6), pages 3156-3171, November.
    28. McDonald, James B. & Newey, Whitney K., 1988. "Partially Adaptive Estimation of Regression Models via the Generalized T Distribution," Econometric Theory, Cambridge University Press, vol. 4(03), pages 428-457, December.
    29. Peter Ferderer, J., 1996. "Oil price volatility and the macroeconomy," Journal of Macroeconomics, Elsevier, vol. 18(1), pages 1-26.
    30. Jaime Casassus & Pierre Collin-Dufresne, 2005. "Stochastic Convenience Yield Implied from Commodity Futures and Interest Rates," Journal of Finance, American Finance Association, vol. 60(5), pages 2283-2331, October.
    31. Sadorsky, Perry, 2006. "Modeling and forecasting petroleum futures volatility," Energy Economics, Elsevier, vol. 28(4), pages 467-488, July.
    32. Engle, Robert F & Gonzalez-Rivera, Gloria, 1991. "Semiparametric ARCH Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(4), pages 345-59, October.
    33. Christie-David, Rohan & Chaudhry, Mukesh & Koch, Timothy W., 2000. "Do macroeconomics news releases affect gold and silver prices?," Journal of Economics and Business, Elsevier, vol. 52(5), pages 405-421.
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