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Multi Mean Garch Approach to Evaluating Hedging Performance in the Crude Palm Oil Futures Market

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Author Info

  • Rozaimah Zainudin

    ()
    (Faculty of Business and Accountancy, University of Malaya, 50603 Kuala Lumpur)

  • Roselee Shah Shaharudin

    (Head of Research Department, 3, Persiaran Bukit Kiara, Bukit Kiara, 50490 Kuala Lumpur)

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    Abstract

    This paper provides evidence of hedging performance in the crude palm oil market using risk minimisation and the investor's utility function measurement. We use the spot and futures crude palm oil daily prices from the period of January 1996 to August 2008. Using a dynamic model, we estimate three different mean specifications that involve the intercept, Vector Autoregressive (VAR) and Vector Error Correction Model (VECM) within the Baba, Engle, Kraft and Kroner (BEKK) model. The risk minimisation results exhibit that the Intercept-BEKK and VAR-BEKK models tend to give the most variance reduction within the in-sample and out-sample analysis, respectively. However, Intercept-BEKK remains to outcast the other models in giving the most utility function. The empirical evidence shows that different mean specifications will generate varying hedging performance results, especially in relation to the risk minimisation result. However, the difference in the performance among the tested models is small, especially within the investor's utility function measurement. Since a more sophisticated model does not warrant better hedging performance results, we suggest that a parsimony model may be appropriate when improvising the hedging performance.

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    Bibliographic Info

    Article provided by Penerbit Universiti Sains Malaysia in its journal Asian Academy of Management Journal of Accounting and Finance.

    Volume (Year): 7 (2011)
    Issue (Month): 1 ()
    Pages: 111-130

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    Handle: RePEc:usm:journl:aamjaf00701_111-130

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    Web page: http://web.usm.my/aamj/
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    Keywords: Hedging performance; hedging ratio; BEKK model; minimum variance; mean variance;

    References

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    1. Wenling Yang & David E. Allen, 2005. "Multivariate GARCH hedge ratios and hedging effectiveness in Australian futures markets," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 45(2), pages 301-321.
    2. Cecchetti, Stephen G & Cumby, Robert E & Figlewski, Stephen, 1988. "Estimation of the Optimal Futures Hedge," The Review of Economics and Statistics, MIT Press, vol. 70(4), pages 623-30, November.
    3. Baillie, Richard T & Myers, Robert J, 1991. "Bivariate GARCH Estimation of the Optimal Commodity Futures Hedge," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(2), pages 109-24, April-Jun.
    4. Lien, Donald, 2004. "Cointegration and the optimal hedge ratio: the general case," The Quarterly Review of Economics and Finance, Elsevier, vol. 44(5), pages 654-658, December.
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    6. Ederington, Louis H. & Salas, Jesus M., 2008. "Minimum variance hedging when spot price changes are partially predictable," Journal of Banking & Finance, Elsevier, vol. 32(5), pages 654-663, May.
    7. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
    8. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(01), pages 122-150, February.
    9. Wilkinson, Katherine J & Rose, Lawrence C & Young, Martin R, 1999. "Comparing the Effectiveness of Traditional and Time Varying Hedge Ratios Using New Zealand and Australian Debt Futures Contracts," The Financial Review, Eastern Finance Association, vol. 34(3), pages 79-94, August.
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    11. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    12. Donald Lien & Y. K. Tse & Albert Tsui, 2002. "Evaluating the hedging performance of the constant-correlation GARCH model," Applied Financial Economics, Taylor & Francis Journals, vol. 12(11), pages 791-798.
    13. F. Douglas Foster & Charles H. Whiteman, 2002. "Bayesian Cross Hedging: An Example From the Soybean Market," Australian Journal of Management, Australian School of Business, vol. 27(2), pages 95-122, December.
    14. Anil K. Bera & Philip Garcia & Jae-Sun Roh, 1997. "Estimation of Time-Varying Hedge Ratios for Corn and Soybeans: BGARCH and Random Coefficient Approaches," Finance 9712007, EconWPA.
    15. Kroner, Kenneth F. & Sultan, Jahangir, 1993. "Time-Varying Distributions and Dynamic Hedging with Foreign Currency Futures," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 28(04), pages 535-551, December.
    16. Gagnon, Louis & Lypny, Gregory J. & McCurdy, Thomas H., 1998. "Hedging foreign currency portfolios," Journal of Empirical Finance, Elsevier, vol. 5(3), pages 197-220, September.
    17. Christos Floros & Dimitrios Vougas, 2004. "Hedge ratios in Greek stock index futures market," Applied Financial Economics, Taylor & Francis Journals, vol. 14(15), pages 1125-1136.
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