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Volatility Spillovers between Crude Oil Prices and New Energy Stock Price in China

Author

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  • Yufeng Chen

    (College of Business Administration, Capital University of Economics and Business, Beijing, 100070, China; School of Economics and Center for Studies of Modern Business, Zhejiang Gongshang University, Hangzhou, 310018, China.)

  • Wenqi Li

    (School of Economics, Zhejiang Gongshang University, Hangzhou, 310018, China.)

  • Xi Jin

    (Department of Economics, New York University, New York, 10044, US.)

Abstract

Using data from the crude oil market and the stock market in China, this paper employed VAR model and multivariate GARCH models (including BEKK, DCC, and CCC) to analyze the mean and volatility spillover effects between crude oil future prices and new energy stock prices in China. The BEKK model is found to fit the data the best, with the comparison of three types of GARCH models. The result shows that there is unilateral mean and volatility spillover effects from crude oil future prices to new energy stock prices in China. Then, the time-varying conditional correlations are constructed to offer a deeper insight for the relationship of crude oil futures market and Chinese new energy stock market. In addition, a dollar long position of new energy stock could be a hedge with twelve cents short position of crude oil. These empirical findings can be useful to both investors and policy-makers for the current and especially future economic and financial environments.

Suggested Citation

  • Yufeng Chen & Wenqi Li & Xi Jin, 2018. "Volatility Spillovers between Crude Oil Prices and New Energy Stock Price in China," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 43-62, December.
    • Handle: RePEc:rjr:romjef:v::y:2018:i:2:p:43-62
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    References listed on IDEAS

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    Cited by:

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    2. Dejan Zivkov & Slavica Manic & Jasmina Duraskovic & Jelena Kovacevic, 2019. "Bidirectional Nexus between Inflation and Inflation Uncertainty in the Asian Emerging Markets – The GARCH-in-Mean Approach," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 69(6), pages 580-599, December.
    3. Jingjian, Si & Xiangyun, Gao & Jinsheng, Zhou & Anjian, Wang & Xiaotian, Sun & Yiran, Zhao & Hongyu, Wei, 2023. "The impact of oil price shocks on energy stocks from the perspective of investor attention," Energy, Elsevier, vol. 278(PB).
    4. Dejan Živkov & Suzana Balaban & Marko Pećanac, 2021. "Assessing the multiscale “meteor shower” effect from oil to the central and eastern European stock indices," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1855-1870, April.
    5. Hanif, Waqas & Arreola Hernandez, Jose & Kang, Sang Hoon & Boako, Gideon & Yoon, Seong-Min, 2024. "Interdependence and spillovers between big oil companies and regional and global energy equity markets," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 451-469.
    6. Bai, Lan & Wei, Yu & Zhang, Jiahao & Wang, Yizhi & Lucey, Brian M., 2023. "Diversification effects of China's carbon neutral bond on renewable energy stock markets: A minimum connectedness portfolio approach," Energy Economics, Elsevier, vol. 123(C).
    7. Chen, Yufeng & Zheng, Biao & Qu, Fang, 2020. "Modeling the nexus of crude oil, new energy and rare earth in China: An asymmetric VAR-BEKK (DCC)-GARCH approach," Resources Policy, Elsevier, vol. 65(C).
    8. Chen, Yufeng & Li, Wenqi & Qu, Fang, 2019. "Dynamic asymmetric spillovers and volatility interdependence on China’s stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 825-838.
    9. Theplib, Krit & Sethapramote, Yuthana & Jiranyakul, Komain, 2020. "Shock and Volatility Spillovers between Crude Oil Price and Stock Returns: Evidence for Thailand," MPRA Paper 98094, University Library of Munich, Germany.

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    More about this item

    Keywords

    oil price; new energy stock; volatility; multivariate GARCH; VAR;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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