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Improving futures hedging performance using option information: Evidence from the S&P 500 index

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  • Bai, Yujuan
  • Pan, Zhiyuan
  • Liu, Li

Abstract

Option prices contain important information about risk preferences. This study proposes an option-based model to estimate the optimal dynamic hedging ratio. Using a sample of S&P 500 index, we find that the option-implied hedging ratio has the best performance both in-sample and out-of-sample due to its relative risk aversion. This finding will help risk managers reduce their hedging risk.

Suggested Citation

  • Bai, Yujuan & Pan, Zhiyuan & Liu, Li, 2019. "Improving futures hedging performance using option information: Evidence from the S&P 500 index," Finance Research Letters, Elsevier, vol. 28(C), pages 112-117.
    • Handle: RePEc:eee:finlet:v:28:y:2019:i:c:p:112-117
    DOI: 10.1016/j.frl.2018.04.014
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    References listed on IDEAS

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    1. Rui Fan & Haiqi Li & Sung Y. Park, 2016. "Estimation and Hedging Effectiveness of Time‐Varying Hedge Ratio: Nonparametric Approaches," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(10), pages 968-991, October.
    2. Choudhry, Taufiq, 2003. "Short-run deviations and optimal hedge ratio: evidence from stock futures," Journal of Multinational Financial Management, Elsevier, vol. 13(2), pages 171-192, April.
    3. DeMiguel, Victor & Plyakha, Yuliya & Uppal, Raman & Vilkov, Grigory, 2013. "Improving Portfolio Selection Using Option-Implied Volatility and Skewness," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(6), pages 1813-1845, December.
    4. Lorenzo Cappiello & Robert F. Engle & Kevin Sheppard, 2006. "Asymmetric Dynamics in the Correlations of Global Equity and Bond Returns," Journal of Financial Econometrics, Oxford University Press, vol. 4(4), pages 537-572.
    5. Luo, Xingguo & Ye, Zinan, 2015. "Predicting volatility of the Shanghai silver futures market: What is the role of the U.S. options market?," Finance Research Letters, Elsevier, vol. 15(C), pages 68-77.
    6. Tae H. Park & Lorne N. Switzer, 1995. "Bivariate GARCH estimation of the optimal hedge ratios for stock index futures: A note," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 15(1), pages 61-67, February.
    7. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-350, July.
    8. George J. Jiang & Yisong S. Tian, 2005. "The Model-Free Implied Volatility and Its Information Content," The Review of Financial Studies, Society for Financial Studies, vol. 18(4), pages 1305-1342.
    9. Canina, Linda & Figlewski, Stephen, 1993. "The Informational Content of Implied Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 6(3), pages 659-681.
    10. Luo, Xingguo & Qin, Shihua & Ye, Zinan, 2016. "The information content of implied volatility and jumps in forecasting volatility: Evidence from the Shanghai gold futures market," Finance Research Letters, Elsevier, vol. 19(C), pages 105-111.
    11. Gurdip Bakshi & Nikunj Kapadia & Dilip Madan, 2003. "Stock Return Characteristics, Skew Laws, and the Differential Pricing of Individual Equity Options," The Review of Financial Studies, Society for Financial Studies, vol. 16(1), pages 101-143.
    12. Donald Lien & Keshab Shrestha, 2005. "Estimating the optimal hedge ratio with focus information criterion," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 25(10), pages 1011-1024, October.
    13. 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.
    14. Yudong Wang & Chongfeng Wu & Li Yang, 2015. "Hedging with Futures: Does Anything Beat the Naïve Hedging Strategy?," Management Science, INFORMS, vol. 61(12), pages 2870-2889, December.
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    Cited by:

    1. Michele Azzone & Roberto Baviera, 2020. "Synthetic forwards and cost of funding in the equity derivative market," Papers 2011.03795, arXiv.org, revised Jan 2022.
    2. Azzone, Michele & Baviera, Roberto, 2021. "Synthetic forwards and cost of funding in the equity derivative market," Finance Research Letters, Elsevier, vol. 41(C).
    3. Babu Jose & James Varghese, 2021. "Ideal Investment Protection in Optimistic Perceptions: Evidence From the Indian Equity Options Market," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 12(2), pages 327-340, April.

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    More about this item

    Keywords

    Hedge ratio; Option-implied information; Volatility; Hedging performance;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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