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Chinese crude oil futures volatility and sustainability: An uncertainty indices perspective

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  • Huang, Yisu
  • Xu, Weiju
  • Huang, Dengshi
  • Zhao, Chenchen

Abstract

Uncertainty is capable of affecting the economy and causing recessions. Thus, it is essential to give close attention to uncertainties and identify important information from uncertainties. This study highlights the predictive ability of three popular uncertainty indices for Chinese crude oil futures market volatility. Currently, the future market of crude oil in Shanghai is firmly established as one of the most influential worldwide, and has become one of the effective means for oil-related companies to cope with risks against the backdrop of highly volatile international oil prices in recent years. Our paper is different from related papers that we explore which uncertainty index performs best in terms of forecasting performance by examining it with framework of the RGARCH-MIDAS model from a comprehensive perspective. Hence, in order to evaluate the benchmark model and the extended model’s performance with uncertainty indices, a number of test methods are employed which are of strong out-of-sample. According to the empirical findings, these uncertainty indices do definitely forecast the Chinese crude oil futures’ volatility. Numerous robustness tests have indicated that the model, after including the uncertainty index GPR, may produce volatility estimates that are more accurate than those produced by other models. Our findings will also provide policymakers and energy market investors a number of new perspectives.

Suggested Citation

  • Huang, Yisu & Xu, Weiju & Huang, Dengshi & Zhao, Chenchen, 2023. "Chinese crude oil futures volatility and sustainability: An uncertainty indices perspective," Resources Policy, Elsevier, vol. 80(C).
  • Handle: RePEc:eee:jrpoli:v:80:y:2023:i:c:s0301420722006705
    DOI: 10.1016/j.resourpol.2022.103227
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    More about this item

    Keywords

    Crude oil futures; Volatility forecasts; Geopolitical risk; Climate policy uncertainty; Global economic policy uncertainty; RGARCH-MIDAS;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q17 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agriculture in International Trade
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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