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Optimal futures hedging under jump switching dynamics


  • Lee, Hsiang-Tai


The article develops a Markov regime switching Generalized Orthogonal GARCH model with conditional jump dynamics (JSGO) for optimal futures hedging. To the author's knowledge, there is no existing study on dynamic futures hedging investigating both the effects of regime switching and conditional jumps. This might be the fact that there is no existing hedging model encompassing both of these features. The JSGO solves this problem by introducing a jump switching filtering algorithm to infer ex post both the distributions of jumps and state variables and a recombining procedure to solve the path-dependency problem. To justify the usefulness of the JSGO on dynamic futures hedging, hedging exercises are performed using FTSE 100 futures data traded in the London International Financial Futures and Options Exchange (LIFFE). JSGO exhibits good out-of-sample performance compared to its jump-free and state-independent counterparts in terms of both criteria of variance reductions and utility improvements.

Suggested Citation

  • Lee, Hsiang-Tai, 2009. "Optimal futures hedging under jump switching dynamics," Journal of Empirical Finance, Elsevier, vol. 16(3), pages 446-456, June.
  • Handle: RePEc:eee:empfin:v:16:y:2009:i:3:p:446-456

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    References listed on IDEAS

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    Cited by:

    1. Fu, Junhui, 2014. "Multi-objective hedging model with the third central moment and the capital budget," Economic Modelling, Elsevier, vol. 36(C), pages 213-219.
    2. François, Pascal & Gauthier, Geneviève & Godin, Frédéric, 2014. "Optimal hedging when the underlying asset follows a regime-switching Markov process," European Journal of Operational Research, Elsevier, vol. 237(1), pages 312-322.
    3. Dark, Jonathan, 2015. "Futures hedging with Markov switching vector error correction FIEGARCH and FIAPARCH," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 269-285.
    4. Fu, Junhui & Zhang, Wei-Guo & Yao, Zheng & Zhang, Xili, 2012. "Hedging the portfolio of raw materials and the commodity under the mark-to-market risk," Economic Modelling, Elsevier, vol. 29(4), pages 1070-1075.
    5. Hung, Jui-Cheng & Yi-Hsien Wang, & Chang, Matthew C. & Shih, Kuang-Hsun & Hsiu-Hsueh Kao,, 2011. "Minimum variance hedging with bivariate regime-switching model for WTI crude oil," Energy, Elsevier, vol. 36(5), pages 3050-3057.
    6. Alexander, Carol & Prokopczuk, Marcel & Sumawong, Anannit, 2013. "The (de)merits of minimum-variance hedging: Application to the crack spread," Energy Economics, Elsevier, vol. 36(C), pages 698-707.
    7. Pascal François & Geneviève Gauthier & Frédéric Godin, 2012. "Optimal Hedging when the Underlying Asset Follows a Regime-switching Markov Process," Cahiers de recherche 1234, CIRPEE.
    8. repec:eee:finana:v:54:y:2017:i:c:p:176-191 is not listed on IDEAS
    9. Markopoulou, Chrysi E. & Skintzi, Vasiliki D. & Refenes, Apostolos-Paul N., 2016. "Realized hedge ratio: Predictability and hedging performance," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 121-133.
    10. Ying Jiang & Shamim Ahmed & Xiaoquan Liu, 2017. "Volatility forecasting in the Chinese commodity futures market with intraday data," Review of Quantitative Finance and Accounting, Springer, vol. 48(4), pages 1123-1173, May.
    11. Thomas Conlon & John Cotter & Ramazan Gencay, 2012. "Commodity futures hedging, risk aversion and the hedging horizon," Working Papers 201218, Geary Institute, University College Dublin.
    12. Lee, Hsiang-Tai, 2010. "Regime switching correlation hedging," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2728-2741, November.


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