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Forecasting the daily dynamic hedge ratios by GARCH models: evidence from the agricultural futures markets

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  • Yuanyuan Zhang
  • Taufiq Choudhry

Abstract

This paper investigates the forecasting ability of six different generalized autoregressive conditional heteroskedasticity (GARCH) models; bivariate GARCH, BEKK GARCH, GARCH-X, BEKK-X, Q-GARCH and GARCH-GJR based on two different distributions (normal and student- t ). Forecast errors based on four agricultural commodities' futures portfolio return forecasts (based on forecasted hedge ratio) are employed to evaluate the out-of-sample forecasting ability of the six GARCH models. The four commodities under investigation are two storable commodities: wheat and soybean, and two non-storable commodities: live cattle and live hogs. We apply the rolling forecasting method and the Model Confidence Set approach to evaluate and compare the forecasting ability of the six GARCH models. Our results show that the forecasting performances of the six GARCH models are different for storable and non-storable agricultural commodities. We find that the BEKK-type models perform the best in the case of storable products, while the asymmetric GARCH models dominate in the case of non-storable commodities. These results are regardless of the forecast horizon and residual distributions.

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  • Yuanyuan Zhang & Taufiq Choudhry, 2015. "Forecasting the daily dynamic hedge ratios by GARCH models: evidence from the agricultural futures markets," The European Journal of Finance, Taylor & Francis Journals, vol. 21(4), pages 376-399, March.
    • Handle: RePEc:taf:eurjfi:v:21:y:2015:i:4:p:376-399
    DOI: 10.1080/1351847X.2013.794744
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    Cited by:

    1. Martin T. Bohl & Martin Stefan, 2020. "Return dynamics during periods of high speculation in a thinly traded commodity market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(1), pages 145-159, January.
    2. Ran Lu & Hongjun Zeng, 2022. "VIX and major agricultural future markets: dynamic linkage and time-frequency relations around the COVID-19 outbreak," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 40(2), pages 334-353, September.
    3. Sarasty, Oscar & Amin, Modhurima & Badruddoza, Syed, 2022. "Impact of the COVID-19 pandemic on agricultural commodity prices," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322240, Agricultural and Applied Economics Association.
    4. Wang, Ze & Gao, Xiangyun & An, Haizhong & Tang, Renwu & Sun, Qingru, 2020. "Identifying influential energy stocks based on spillover network," International Review of Financial Analysis, Elsevier, vol. 68(C).
    5. Umar, Zaghum & Hussain Shahzad, Syed Jawad & Kenourgios, Dimitris, 2019. "Hedging U.S. metals & mining Industry's credit risk with industrial and precious metals," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    6. Lu, Xinjie & Su, Yuandong & Huang, Dengshi, 2023. "Chinese agricultural futures volatility: New insights from potential domestic and global predictors," International Review of Financial Analysis, Elsevier, vol. 89(C).
    7. Zhou, Wei & Gu, Qinen & Chen, Jin, 2021. "From volatility spillover to risk spread: An empirical study focuses on renewable energy markets," Renewable Energy, Elsevier, vol. 180(C), pages 329-342.

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