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Optimal hedging with higher moments

Author

Listed:
  • Chris Brooks
  • Alešs Černý
  • Joëlle Miffre

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.
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Suggested Citation

  • 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.
    • Handle: RePEc:wly:jfutmk:v:32:y:2012:i:10:p:909-944
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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. Cotter, John & Hanly, Jim, 2015. "Performance of utility based hedges," Energy Economics, Elsevier, vol. 49(C), pages 718-726.
    7. 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).
    8. He, Yulu & Ma, Wenliang & Fan, Keke & Li, Hongchang & Wang, Kun, 2025. "The impacts of exchange rate fluctuations on the international air transport," Transportation Research Part A: Policy and Practice, Elsevier, vol. 198(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. Hao, Wei & Pham, Linh, 2024. "Dynamic connectedness in the higher moments between clean energy and oil prices," Energy Economics, Elsevier, vol. 140(C).
    12. Pan, Zhiyuan & Xiao, Dongli & Dong, Qingma & Liu, Li, 2022. "Structural breaks, macroeconomic fundamentals and cross hedge ratio," Finance Research Letters, Elsevier, vol. 47(PA).
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. Xuejun Jiang & Lingju Cheng & Xinjie Dai, 2025. "The effects of skewness and kurtosis on production and hedging decisions: a Gram-Charlier expansion approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-17, December.
    20. Yan Hu & Jian Ni, 2024. "A deep learning‐based financial hedging approach for the effective management of commodity risks," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(6), pages 879-900, June.

    More about this item

    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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