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Reevaluating hedging performance

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  • John Cotter
  • Jim Hanly

Abstract

Mixed results have been documented for the performance of hedging strategies with the use of futures. This article reinvestigates this issue with the use of an extensive set of performance‐evaluation metrics across seven international markets. The hedging performances of short and long hedgers are compared with the use of traditional variance‐based approaches together with modern risk‐management techniques, including value at risk, conditional value at risk, and approaches based on downside risk. The findings indicate that use of these metrics to evaluate hedging performance yields differences in terms of best hedging strategy as compared with the traditional variance measure. Also, significant differences in performance between short and long hedgers are found. These results are observed both in sample and out of sample. © 2006 Wiley Periodicals, Inc. Jrl Fut Mark 26:677–702, 2006

Suggested Citation

  • John Cotter & Jim Hanly, 2006. "Reevaluating hedging performance," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 26(7), pages 677-702, July.
  • Handle: RePEc:wly:jfutmk:v:26:y:2006:i:7:p:677-702
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    Cited by:

    1. Wagner Oliveira Monteiro & Rodrigo De Losso da Silveira Bueno, 2011. "Dynamic Hedging inMarkov Regimes Switching," Anais do XXXVII Encontro Nacional de Economia [Proceedings of the 37th Brazilian Economics Meeting] 136, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    2. Power, Gabriel J. & Vedenov, Dmitry V., 2008. "The Shape of the Optimal Hedge Ratio: Modeling Joint Spot-Futures Prices using an Empirical Copula-GARCH Model," 2008 Conference, April 21-22, 2008, St. Louis, Missouri 37609, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    3. Sebastião, Helder & Godinho, Pedro, 2020. "Bitcoin futures: An effective tool for hedging cryptocurrencies," Finance Research Letters, Elsevier, vol. 33(C).
    4. Fei, Chengcheng & Vedenov, Dmitry & Stevens, Reid B. & Anderson, David, 2020. "Single-Commodity vs. Joint Hedging in Cattle Feeding Cycle: Is Joint Hedging Always Essential?," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 0(Preprints), August.
    5. 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.
    6. Vera Mirovic & Dejan Zivkov & Jovan Njegic, 2017. "Construction of Commodity Portfolio and Its Hedge Effectiveness Gauging – Revisiting DCC Models," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 67(5), pages 396-422, October.
    7. 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).
    8. Philip, Dennis & Shi, Yukun, 2016. "Optimal hedging in carbon emission markets using Markov regime switching models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 43(C), pages 1-15.
    9. Ubukata, Masato & Watanabe, Toshiaki, 2015. "Evaluating the performance of futures hedging using multivariate realized volatility," Journal of the Japanese and International Economies, Elsevier, vol. 38(C), pages 148-171.
    10. Cotter, John & Hanly, Jim, 2010. "Time-varying risk aversion: An application to energy hedging," Energy Economics, Elsevier, vol. 32(2), pages 432-441, March.
    11. Cotter, John & Hanly, Jim, 2015. "Performance of utility based hedges," Energy Economics, Elsevier, vol. 49(C), pages 718-726.
    12. Dinica, Mihai Cristian & Armeanu, Daniel, 2014. "The Optimal Hedging Ratio for Non-Ferrous Metals," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 105-122, March.
    13. Park, Jin Suk & Shi, Yukun, 2017. "Hedging and speculative pressures and the transition of the spot-futures relationship in energy and metal markets," International Review of Financial Analysis, Elsevier, vol. 54(C), pages 176-191.
    14. Kunlapath Sukcharoen & Hankyeung Choi & David J. Leatham, 2015. "Optimal gasoline hedging strategies using futures contracts and exchange-traded funds," Applied Economics, Taylor & Francis Journals, vol. 47(32), pages 3482-3498, July.
    15. Amine Lahiani & Khaled Guesmi, 2014. "Commodity Price Correlation and Time varying Hedge Ratios," Working Papers 2014-142, Department of Research, Ipag Business School.
    16. Kuang-Liang Chang, 2011. "The optimal value-at-risk hedging strategy under bivariate regime switching ARCH framework," Applied Economics, Taylor & Francis Journals, vol. 43(21), pages 2627-2640.
    17. 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.
    18. Daniel Doyle & Chris Groendyke, 2018. "Using Neural Networks to Price and Hedge Variable Annuity Guarantees," Risks, MDPI, Open Access Journal, vol. 7(1), pages 1-1, December.
    19. Hung, Jui-Cheng, 2015. "Evaluation of realized multi-power variations in minimum variance hedging," Economic Modelling, Elsevier, vol. 51(C), pages 672-679.
    20. Bernard, Carole & Kwak, Minsuk, 2016. "Semi-static hedging of variable annuities," Insurance: Mathematics and Economics, Elsevier, vol. 67(C), pages 173-186.
    21. Spencer, Simon & Bredin, Don & Conlon, Thomas, 2018. "Energy and agricultural commodities revealed through hedging characteristics: Evidence from developing and mature markets," Journal of Commodity Markets, Elsevier, vol. 9(C), pages 1-20.

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    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G0 - Financial Economics - - General

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