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A Bivariate High‐Frequency‐Based Volatility Model for Optimal Futures Hedging

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  • Yu‐Sheng Lai
  • Donald Lien

Abstract

This study examines the usefulness of high‐frequency data for estimating hedge ratios for different hedging horizons. By jointly modeling the returns and conditional expectation of the covariation, the multivariate high‐frequency‐based volatility (HEAVY) model generates spot‐futures distributions over longer horizons. Using the data on international equity index futures, performance comparisons between HEAVY and generalized autoregressive conditional heteroskedasticity (GARCH) hedge ratios indicate that HEAVY hedge ratios perform more effectively than GARCH hedge ratios at shorter hedging horizons. This implies that the distinct properties of short‐time response and short‐run momentum effects revealed in the HEAVY model are vital for hedge ratio estimation. © 2017 Wiley Periodicals, Inc. Jrl Fut Mark 37:913–929, 2017

Suggested Citation

  • Yu‐Sheng Lai & Donald Lien, 2017. "A Bivariate High‐Frequency‐Based Volatility Model for Optimal Futures Hedging," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 37(9), pages 913-929, September.
    • Handle: RePEc:wly:jfutmk:v:37:y:2017:i:9:p:913-929
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    Cited by:

    1. Stavros Degiannakis & Christos Floros & Enrique Salvador & Dimitrios Vougas, 2022. "On the stationarity of futures hedge ratios," Operational Research, Springer, vol. 22(3), pages 2281-2303, July.
    2. Billio, Monica & Casarin, Roberto & Osuntuyi, Anthony, 2018. "Markov switching GARCH models for Bayesian hedging on energy futures markets," Energy Economics, Elsevier, vol. 70(C), pages 545-562.
    3. Wen-Chung Hsu & Hsiang-Tai Lee, 2018. "Cross Hedging Stock Sector Risk with Index Futures by Considering the Global Equity Systematic Risk," IJFS, MDPI, vol. 6(2), pages 1-17, April.

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