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Forecasting Crude Oil Future Volatilities with a Threshold Zero-Drift GARCH Model

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  • Tong Liu

    (School of General Education, Guizhou University of Commerce, Guiyang 550014, China)

  • Yanlin Shi

    (Department of Actuarial Studies and Business Analytics, Macquarie University, Sydney, NSW 2109, Australia)

Abstract

The recent price crash of the New York Mercantile Exchange (NYMEX) crude oil futures contract, which occurred on 20 April 2020, has caused history-writing movements of relative prices. For instance, the West Texas Intermediate (WTI) experienced a negative price. Explosive heteroskedasticity is also evidenced in associated products, such as the Intercontinental Exchange Brent (BRE) and Shanghai International Energy Exchange (INE) crude oil futures. Those movements indicate potential non-stationarity in the conditional volatility with an asymmetric influence of negative shocks. To incorporate those features, which cannot be accommodated by the existing generalized autoregressive conditional heteroskedasticity (GARCH) models, we propose a threshold zero-drift GARCH (TZD-GARCH) model. Our empirical studies of the daily INE returns from March 2018 to April 2020 demonstrate the usefulness of the TZD-GARCH model in understanding the empirical features and in precisely forecasting the volatility of INE. Robust checks based on BRE and WTI over various periods further lead to highly consistent results. Applications of news impact curves and Value-at-Risk (VaR) analyses indicate the usefulness of the proposed TZD-GARCH model in practice. Implications include more effectively hedging risks of crude oil futures for policymakers and market participants, as well as the potential market inefficiency of INE relative to WTI and BRE.

Suggested Citation

  • Tong Liu & Yanlin Shi, 2022. "Forecasting Crude Oil Future Volatilities with a Threshold Zero-Drift GARCH Model," Mathematics, MDPI, vol. 10(15), pages 1-20, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2757-:d:879457
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    References listed on IDEAS

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