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An empirical study of the impact of skewness and kurtosis on hedging decisions

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  • Jing-Yi Lai

Abstract

This study uses real price data rather than a simulation approach to investigate how hedging behaviours may change when hedgers consider skewness and excess kurtosis of hedging returns in their decision models. The study involves modelling the time-varying skewness and excess kurtosis of returns. The empirical results show that adding a preference for positively skewed returns to traditional mean-variance models may not lead to more speculative hedging/investment behaviours. Post-hedged return distributions suggest that the third moments of hedged portfolios have probably been well adjusted by mean-variance strategies, rendering three-moment decision models on a par with traditional mean-variance models. Additionally, considering the aversion to excess kurtosis will cause investors to hedge more. The research also provides empirical support for traditional minimum-variance strategies.

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  • Jing-Yi Lai, 2012. "An empirical study of the impact of skewness and kurtosis on hedging decisions," Quantitative Finance, Taylor & Francis Journals, vol. 12(12), pages 1827-1837, December.
  • Handle: RePEc:taf:quantf:v:12:y:2012:i:12:p:1827-1837
    DOI: 10.1080/14697688.2012.696677
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    1. Kimball, Miles S, 1990. "Precautionary Saving in the Small and in the Large," Econometrica, Econometric Society, vol. 58(1), pages 53-73, January.
    2. Leland L. Johnson, 1960. "The Theory of Hedging and Speculation in Commodity Futures," Review of Economic Studies, Oxford University Press, vol. 27(3), pages 139-151.
    3. Whitney K. Newey & Douglas G. Steigerwald, 1997. "Asymptotic Bias for Quasi-Maximum-Likelihood Estimators in Conditional Heteroskedasticity Models," Econometrica, Econometric Society, vol. 65(3), pages 587-600, May.
    4. Panayiotis Theodossiou, 1998. "Financial Data and the Skewed Generalized T Distribution," Management Science, INFORMS, vol. 44(12-Part-1), pages 1650-1661, December.
    5. Kai-Li Wang & Christopher Fawson & Christopher B. Barrett & James B. McDonald, 2001. "A flexible parametric GARCH model with an application to exchange rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(4), pages 521-536.
    6. Conine, Thomas E, Jr & Tamarkin, Maurry, J, 1981. "On Diversification Given Asymmetry in Returns," Journal of Finance, American Finance Association, vol. 36(5), pages 1143-1155, December.
    7. John Cotter & Jim Hanly, 2012. "Hedging effectiveness under conditions of asymmetry," The European Journal of Finance, Taylor & Francis Journals, vol. 18(2), pages 135-147, February.
    8. Hyun J. Jin, 2007. "Heavy‐tailed Behavior of Commodity Price Distribution and Optimal Hedging Demand," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 74(4), pages 863-881, December.
    9. Andrew J. Patton, 2004. "On the Out-of-Sample Importance of Skewness and Asymmetric Dependence for Asset Allocation," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(1), pages 130-168.
    10. Larry S. Karp, 1987. "Methods for Selecting the Optimal Dynamic Hedge When Production is Stochastic," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 69(3), pages 647-657.
    11. Chen, Sheng-Syan & Lee, Cheng-few & Shrestha, Keshab, 2008. "Do the pure martingale and joint normality hypotheses hold for futures contracts: Implications for the optimal hedge ratios," The Quarterly Review of Economics and Finance, Elsevier, vol. 48(1), pages 153-174, February.
    12. Harvey, Campbell R. & Siddique, Akhtar, 1999. "Autoregressive Conditional Skewness," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 34(4), pages 465-487, December.
    13. Scott Gilbert & Samuel Kyle Jones & Gay Hatfield Morris, 2006. "The impact of skewness in the hedging decision," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 26(5), pages 503-520, May.
    14. Pratt, John W & Zeckhauser, Richard J, 1987. "Proper Risk Aversion," Econometrica, Econometric Society, vol. 55(1), pages 143-154, January.
    15. Scott, Robert C & Horvath, Philip A, 1980. "On the Direction of Preference for Moments of Higher Order Than the Variance," Journal of Finance, American Finance Association, vol. 35(4), pages 915-919, September.
    16. McDonald, James B., 1996. "An application and comparison of some flexible parametric and semi-parametric qualitative response models," Economics Letters, Elsevier, vol. 53(2), pages 145-152, November.
    17. Robert F. Dittmar, 2002. "Nonlinear Pricing Kernels, Kurtosis Preference, and Evidence from the Cross Section of Equity Returns," Journal of Finance, American Finance Association, vol. 57(1), pages 369-403, February.
    18. Wing H. Chan & Denise Young, 2006. "Jumping hedges: An examination of movements in copper spot and futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 26(2), pages 169-188, February.
    19. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    20. 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 705-730, August.
    21. Sung Yong Park & Sang Young Jei, 2010. "Estimation and hedging effectiveness of time‐varying hedge ratio: Flexible bivariate garch approaches," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(1), pages 71-99, January.
    22. Choi, Pilsun & Nam, Kiseok, 2008. "Asymmetric and leptokurtic distribution for heteroscedastic asset returns: The SU-normal distribution," Journal of Empirical Finance, Elsevier, vol. 15(1), pages 41-63, January.
    23. Kimball, Miles S, 1993. "Standard Risk Aversion," Econometrica, Econometric Society, vol. 61(3), pages 589-611, May.
    24. Chris Brooks & Alešs Černý & Joëlle Miffre, 2012. "Optimal hedging with higher moments," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 32(10), pages 909-944, October.
    25. Markku Lanne & Saikkonen Pentti, 2007. "Modeling Conditional Skewness in Stock Returns," The European Journal of Finance, Taylor & Francis Journals, vol. 13(8), pages 691-704.
    26. Kane, Alex, 1982. "Skewness Preference and Portfolio Choice," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 17(1), pages 15-25, March.
    27. McDonald, James B., 1991. "Parametric models for partially adaptive estimation with skewed and leptokurtic residuals," Economics Letters, Elsevier, vol. 37(3), pages 273-278, November.
    28. Simkowitz, Michael A. & Beedles, William L., 1978. "Diversification in a Three-Moment World," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 13(5), pages 927-941, December.
    29. Kraus, Alan & Litzenberger, Robert H, 1976. "Skewness Preference and the Valuation of Risk Assets," Journal of Finance, American Finance Association, vol. 31(4), pages 1085-1100, September.
    30. Kavussanos, Manolis G. & Nomikos, Nikos K., 2000. "Constant vs. time-varying hedge ratios and hedging efficiency in the BIFFEX market," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 36(4), pages 229-248, December.
    31. 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.
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