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Optimal Hedging with Higher Moments

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  • Chris Brooks

    (ICMA Centre, University of Reading)

  • A.Cerny

    (Cass Business School)

  • J. Miffre

    (Cass Business School)

Abstract

This study proposes a utility-based framework for the determination of optimal hedge ratios that can allow for the impact of higher moments on the hedging decision. The approach is applied to a set of 20 commodities that are hedged with futures contracts. We find that in sample, the performance of hedges constructed allowing for non-zero higher moments is only very slightly better than the performance of the much simpler OLS hedge ratio. When implemented out of sample, utility-based hedge ratios are usually less stable over time, and can make investors worse off for some assets compared to hedging using the traditional methods. We conclude, in common with a growing body of very recent literature, by suggesting that higher moments matter in theory but not in practice.

Suggested Citation

  • Chris Brooks & A.Cerny & J. Miffre, 2006. "Optimal Hedging with Higher Moments," ICMA Centre Discussion Papers in Finance icma-dp2006-12, Henley Business School, University of Reading.
    • Handle: RePEc:rdg:icmadp:icma-dp2006-12
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    References listed on IDEAS

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    1. Vikas Agarwal, 2004. "Risks and Portfolio Decisions Involving Hedge Funds," Review of Financial Studies, Society for Financial Studies, vol. 17(1), pages 63-98.
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    4. Chris Brooks & Harry. M Kat, 2001. "The Statistical Properties of Hedge Fund Index Returns," ICMA Centre Discussion Papers in Finance icma-dp2001-09, Henley Business School, University of Reading.
    5. 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 109-124, April-Jun.
    6. Ederington, Louis H, 1979. "The Hedging Performance of the New Futures Markets," Journal of Finance, American Finance Association, vol. 34(1), pages 157-170, March.
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    8. Chunhachinda, Pornchai & Dandapani, Krishnan & Hamid, Shahid & Prakash, Arun J., 1997. "Portfolio selection and skewness: Evidence from international stock markets," Journal of Banking & Finance, Elsevier, vol. 21(2), pages 143-167, February.
    9. Y. Peter Chung & Michael J. Schill, 2006. "Asset Pricing When Returns Are Nonnormal: Fama-French Factors versus Higher-Order Systematic Comoments," The Journal of Business, University of Chicago Press, vol. 79(2), pages 923-940, March.
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    Citations

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    Cited by:

    1. Yang (Greg) Hou & Mark Holmes, 2020. "Do higher order moments of return distribution provide better decisions in minimum-variance hedging? Evidence from US stock index futures," Australian Journal of Management, Australian School of Business, vol. 45(2), pages 240-265, May.
    2. Siroos Khademalomoom & Paresh Kumar Narayan & Susan Sunila Sharma, 2019. "Higher Moments and Exchange Rate Behavior," The Financial Review, Eastern Finance Association, vol. 54(1), pages 201-229, February.
    3. Jim Hanly, 2017. "Managing Energy Price Risk using Futures Contracts: A Comparative Analysis," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    4. Berghöfer, Britta & Lucey, Brian, 2014. "Fuel hedging, operational hedging and risk exposure — Evidence from the global airline industry," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 124-139.
    5. Stutzer, Michael, 2013. "Optimal hedging via large deviation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(15), pages 3177-3182.
    6. Jules Sadefo Kamdem & Zoulkiflou Moumouni, 2020. "Comparison of Some Static Hedging Models of Agricultural Commodities Price Uncertainty," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(3), pages 631-655, September.
    7. Cotter, John & Hanly, Jim, 2015. "Performance of utility based hedges," Energy Economics, Elsevier, vol. 49(C), pages 718-726.
    8. Corbet, Shaen & Hou, Yang (Greg) & Hu, Yang & Oxley, Les, 2022. "The influence of the COVID-19 pandemic on the hedging functionality of Chinese financial markets," Research in International Business and Finance, Elsevier, vol. 59(C).
    9. Vedenov, Dmitry & Power, Gabriel J., 2022. "We don't need no fancy hedges! Or do we?," International Review of Financial Analysis, Elsevier, vol. 81(C).
    10. Lawrence Kryzanowski & Jie Zhang & Rui Zhong, 2021. "Currency hedging and quantitative easing: Evidence from global bond markets," International Review of Finance, International Review of Finance Ltd., vol. 21(2), pages 555-597, June.
    11. Pan, Zhiyuan & Xiao, Dongli & Dong, Qingma & Liu, Li, 2022. "Structural breaks, macroeconomic fundamentals and cross hedge ratio," Finance Research Letters, Elsevier, vol. 47(PA).
    12. Hou, Yang & Holmes, Mark, 2017. "On the effects of static and autoregressive conditional higher order moments on dynamic optimal hedging," MPRA Paper 82000, University Library of Munich, Germany.
    13. Kim, Myeong Jun & Park, Sung Y., 2016. "Optimal conditional hedge ratio: A simple shrinkage estimation approach," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 139-156.
    14. Ana-Maria Fuertes & Joëlle Miffre & Wooi-Hou Tan, 2009. "Momentum profits, nonnormality risks and the business cycle," Applied Financial Economics, Taylor & Francis Journals, vol. 19(12), pages 935-953.
    15. Zoulkiflou Moumouni & Jules Sadefo-Kamdem, 2019. "New models of commodity risk hedging according to the behavior of economic decision-makers or Rollover Strategies," Working Papers hal-02417459, HAL.
    16. 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.

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    More about this item

    Keywords

    Utility-based hedging; OLS; Non-normality; risk; commodity futures; skewness; kurtosis;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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