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Estimation and Hedging Effectiveness of Time‐Varying Hedge Ratio: Nonparametric Approaches

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  • Rui Fan
  • Haiqi Li
  • Sung Y. Park

Abstract

Many studies have estimated the optimal time‐varying hedge ratio using futures, with most employing a bivariate generalized autoregressive conditional heteroscedasticity (BGARCH) model or a random coefficient model to estimate the time‐varying hedge ratio. However, it has been argued that when the variability of the estimated time‐varying hedge ratio is large, this ratio's hedging performance is not as good as that of the unconditional (constant) hedge ratio. This study proposes a nonparametric estimation approach to estimate and evaluate the optimal conditional hedge ratio. This method produces a time‐varying hedge ratio with less volatility than those obtained from the BGARCH and random coefficient models. We evaluate the hedging performance of the various models using soybean oil, corn, S&P 500, and Hang Seng futures indices. The empirical results support the proposed nonparametric approach in terms of both in‐sample and out‐of‐sample performance. © 2015 Wiley Periodicals, Inc. Jrl Fut Mark 36:968–991, 2016

Suggested Citation

  • 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.
    • Handle: RePEc:wly:jfutmk:v:36:y:2016:i:10:p:968-991
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    Cited by:

    1. 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.
    2. Caporin, Massimiliano & Malik, Farooq, 2020. "Do structural breaks in volatility cause spurious volatility transmission?," Journal of Empirical Finance, Elsevier, vol. 55(C), pages 60-82.
    3. Farooq Malik, 2022. "Volatility spillover among sector equity returns under structural breaks," Review of Quantitative Finance and Accounting, Springer, vol. 58(3), pages 1063-1080, April.
    4. Jebabli, Ikram & Roubaud, David, 2018. "Time-varying efficiency in food and energy markets: Evidence and implications," Economic Modelling, Elsevier, vol. 70(C), pages 97-114.
    5. You‐How Go & Jia‐Jun Teo & Kam Fong Chan, 2023. "The effectiveness of crude oil futures hedging during infectious disease outbreaks in the 21st century," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(11), pages 1559-1575, November.
    6. 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).
    7. 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.
    8. Pan, Zhiyuan & Xiao, Dongli & Dong, Qingma & Liu, Li, 2022. "Structural breaks, macroeconomic fundamentals and cross hedge ratio," Finance Research Letters, Elsevier, vol. 47(PA).
    9. Li Liu & Zhiyuan Pan & Yudong Wang, 2021. "What can we learn from the return predictability over the business cycle?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 108-131, January.

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