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Robust estimation of the optimal hedge ratio

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  • Richard D. F. Harris
  • Jian Shen

Abstract

When using derivative instruments such as futures to hedge a portfolio of risky assets, the primary objective is to estimate the optimal hedge ratio (OHR). When agents have mean‐variance utility and the futures price follows a martingale, the OHR is equivalent to the minimum variance hedge ratio,which can be estimated by regressing the spot market return on the futures market return using ordinary least squares. To accommodate time‐varying volatility in asset returns, estimators based on rolling windows, GARCH, or EWMA models are commonly employed. However, all of these approaches are based on the sample variance and covariance estimators of returns, which, while consistent irrespective of the underlying distribution of the data, are not in general efficient. In particular, when the distribution of the data is leptokurtic, as is commonly found for short horizon asset returns, these estimators will attach too much weight to extreme observations. This article proposes an alternative to the standard approach to the estimation of the OHR that is robust to the leptokurtosis of returns. We use the robust OHR to construct a dynamic hedging strategy for daily returns on the FTSE100 index using index futures. We estimate the robust OHR using both the rolling window approach and the EWMA approach, and compare our results to those based on the standard rolling window and EWMA estimators. It is shown that the robust OHR yields a hedged portfolio variance that is marginally lower than that based on the standard estimator. Moreover, the variance of the robust OHR is as much as 70% lower than the variance of the standard OHR, substantially reducing the transaction costs that are associated with dynamic hedging strategies. © 2003 Wiley Periodicals, Inc. Jrl Fut Mark 23:799–816, 2003

Suggested Citation

  • Richard D. F. Harris & Jian Shen, 2003. "Robust estimation of the optimal hedge ratio," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 23(8), pages 799-816, August.
  • Handle: RePEc:wly:jfutmk:v:23:y:2003:i:8:p:799-816
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    Cited by:

    1. Bernard Bollen, 2015. "What should the value of lambda be in the exponentially weighted moving average volatility model?," Applied Economics, Taylor & Francis Journals, vol. 47(8), pages 853-860, February.
    2. Ming-Chih Lee & Chien-Liang Chiu & Wan-Hsiu Cheng, 2007. "Enhancing Forecast Accuracy By Using Long Estimation Periods," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 1(2), pages 1-9.
    3. Elisa Scarpa & Matteo Manera, 2008. "Pricing and hedging illiquid energy derivatives: An application to the JCC index," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 28(5), pages 464-487, May.
    4. Yu-Jane Liu & Zheng Zhang & Longkai Zhao, 2015. "Speculation Spillovers," Management Science, INFORMS, vol. 61(3), pages 649-664, March.
    5. Janchung Wang, 2009. "Stock market volatility and the forecasting performance of stock index futures," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(4), pages 277-292.
    6. Juan Carlos Gutierrez Betancur, 2017. "Robust Estimation of beta and the hedging ratio in Stock Index Futures In the Integrated Latin American Market," Revista Ecos de Economía, Universidad EAFIT, vol. 21(44), pages 37-71, June.
    7. Yu-Sheng Lai, 2018. "Dynamic hedging with futures: a copula-based GARCH model with high-frequency data," Review of Derivatives Research, Springer, vol. 21(3), pages 307-329, October.
    8. Chiou-Wei, Song-Zan & Chen, Sheng-Hung & Zhu, Zhen, 2020. "Natural gas price, market fundamentals and hedging effectiveness," The Quarterly Review of Economics and Finance, Elsevier, vol. 78(C), pages 321-337.
    9. Gurmeet Singh, 2017. "Estimating Optimal Hedge Ratio and Hedging Effectiveness in the NSE Index Futures," Jindal Journal of Business Research, , vol. 6(2), pages 108-131, December.

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