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The dynamic information spill-over effect of WTI crude oil prices on China’s traditional energy sectors

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  • Yue-Jun Zhang
  • Yao-Bin Wu

Abstract

Purpose - The purpose of this paper is to explore the dynamic influence of WTI crude oil returns on the stock returns of China’s traditional energy sectors, including oil and gas exploitation, coal mining and processing, petroleum processing and coking, electricity, heat production and supply and mining services. Design/methodology/approach - Hong’s information spill-over test and the DP Granger causality test are applied to investigate the relationship between the two markets. Moreover, a rolling window is introduced into the above two tests to capture time-varying characteristics of the influence of WTI crude oil returns. Findings - The empirical results indicate that, first, there exists significant bidirectional linear causality between WTI crude oil returns and China’s traditional energy sectoral stock returns, but the nonlinear causality appears weaker. Second, the influence of WTI crude oil returns on traditional energy sectoral stock returns has time-varying characteristics and industry heterogeneity both in the linear and nonlinear cases. Finally, the decline of WTI crude oil prices may strengthen its linear influence on the stock returns of traditional energy sectors, while the excessive rise of market values in traditional energy sectors may weaken the linear and nonlinear influence of WTI on them. Originality/value - The general nexus between international crude oil market and China’s traditional energy stock market is explored both in the linear and nonlinear perspectives. In particular, the dynamic linear and nonlinear influence of WTI crude oil returns on China’s traditional energy sectoral stock returns and its industry heterogeneity are analysed in detail.

Suggested Citation

  • Yue-Jun Zhang & Yao-Bin Wu, 2018. "The dynamic information spill-over effect of WTI crude oil prices on China’s traditional energy sectors," China Agricultural Economic Review, Emerald Group Publishing Limited, vol. 10(3), pages 516-534, July.
  • Handle: RePEc:eme:caerpp:caer-05-2017-0094
    DOI: 10.1108/CAER-05-2017-0094
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    Citations

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    Cited by:

    1. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    2. Lu-Tao Zhao & Li-Na Liu & Zi-Jie Wang & Ling-Yun He, 2019. "Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach," Sustainability, MDPI, vol. 11(14), pages 1-20, July.
    3. Zhang, Dayong & Wang, Tiantian & Shi, Xunpeng & Liu, Jia, 2018. "Is hub-based pricing a better choice than oil indexation for natural gas? Evidence from a multiple bubble test," Energy Economics, Elsevier, vol. 76(C), pages 495-503.
    4. Sui, Bo & Chang, Chun-Ping & Jang, Chyi-Lu & Gong, Qiang, 2021. "Analyzing causality between epidemics and oil prices: Role of the stock market," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 148-158.

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