Forecasting volatility in Chinese crude oil futures: insights from volatility-of-volatility and Markov regime-switching approaches

Gaoxiu Qiao, Yijun Pan and Chao Liang

Quantitative Finance, 2024, vol. 24, issue 12, 1839-1856

Abstract: This study aims to improve the prediction ability of realized volatility in the Chinese crude oil futures market by characterizing the volatility of volatility (VOV) and its jump components, as well as the Markov regime-switching feature. We extend the HAR-DJI-GARCH model to include the continuous and jump volatility of volatility while incorporating the Markov regime-switching feature through the MS-GARCH framework, thus offering a novel approach for capturing the intricate, nonlinear behaviour of crude oil futures volatility. Model parameters are estimated by improving the maximum likelihood approach, and the performance of the proposed model is compared to that of other models via out-of-sample R2, the CW test, the MCS test and various robustness checks. The empirical findings suggest that the incorporation of VOV, particularly jump information, alongside Markov regime switching significantly enhances the predictive power for the volatility of the Chinese crude oil futures market.

Date: 2024
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DOI: 10.1080/14697688.2024.2434127

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