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Forecasting China's crude oil futures volatility: How to dig out the information of other energy futures volatilities?

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  • Jin, Daxiang
  • He, Mengxi
  • Xing, Lu
  • Zhang, Yaojie

Abstract

This paper explores the predictability of China's crude oil futures volatility by considering other energy futures volatilities. Empirical results show that other energy futures volatilities can provide useful information for forecasting crude oil futures volatility both in- and out-of-sample. To further dig out predictive information from other energy futures volatilities, we employ forecast combinations and shrinkage methods. Corresponding results suggest that both forecast combinations and shrinkage methods make full use of information from other energy futures volatilities and generate more accurate forecasts of crude oil futures volatility. Furthermore, shrinkage methods have better forecasting performance than forecast combinations. Finally, the superior performance of shrinkage methods stems from their ability to accurately select other energy futures volatilities with strong predictive power.

Suggested Citation

  • Jin, Daxiang & He, Mengxi & Xing, Lu & Zhang, Yaojie, 2022. "Forecasting China's crude oil futures volatility: How to dig out the information of other energy futures volatilities?," Resources Policy, Elsevier, vol. 78(C).
    • Handle: RePEc:eee:jrpoli:v:78:y:2022:i:c:s0301420722002987
    DOI: 10.1016/j.resourpol.2022.102852
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    Cited by:

    1. Wang, Jia & Wang, Xinyi & Wang, Xu, 2024. "International oil shocks and the volatility forecasting of Chinese stock market based on machine learning combination models," The North American Journal of Economics and Finance, Elsevier, vol. 70(C).
    2. Fameliti Stavroula & Skintzi Vasiliki, 2024. "Macroeconomic attention and commodity market volatility," Empirical Economics, Springer, vol. 67(5), pages 1967-2007, November.
    3. Guo, Lili & Huang, Xinya & Li, Yanjiao & Li, Houjian, 2023. "Forecasting crude oil futures price using machine learning methods: Evidence from China," Energy Economics, Elsevier, vol. 127(PA).
    4. Liu, Yanqiong & Lu, Jinjin & Shi, Fengyuan, 2023. "Spillover relationship between different oil shocks and high- and low-carbon assets: An analysis based on time-frequency spillover effects," Finance Research Letters, Elsevier, vol. 58(PC).

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    More about this item

    Keywords

    Crude oil; Energy futures; Volatility forecasting; Forecast combinations; Shrinkage methods;
    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
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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