Forecasting the Chinese crude oil futures volatility using jump intensity and Markov-regime switching model

Hanlin Wu, Pan Li, Jiawei Cao and Zijian Xu

Energy Economics, 2024, vol. 134, issue C

Abstract: This study examines the predictive ability of nine high-frequency jumps on the Chinese crude oil futures volatility using a series of the Heterogeneous Autoregressive (HAR) models. Out-of-sample empirical results indicate that among the nine high-frequency jump tests, the JO jump component is powerful because the prediction model including this component demonstrates superior predictive performance. Compared to other competing models, the model incorporating JO jump component, jump intensity, and Markov-regime achieves higher predictive accuracy. During the outbreak of the COVID-19 pandemic and periods of high volatility, this new model continues to exhibit strong predictive capability for volatility in the Chinese oil futures market. This study provides novel insights into forecasting volatility in the Chinese oil market under the presence of extreme shocks.

Keywords: Volatility forecasting; Chinese crude oil futures; Jump tests; Jump intensity; Markov-regime switching model (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:134:y:2024:i:c:s0140988324002962

DOI: 10.1016/j.eneco.2024.107588

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