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Interaction between Industrial Policy and Stock Price Volatility: Evidence from China’s Power Market Reform

Author

Listed:
  • Ye Fan

    (School of Business Administration, China University of Petroleum-Beijing, Beijing 102246, China)

  • Zhicheng Zhang

    (Department of Economics, University of Michigan, Ann Arbor, MI 48104, USA)

  • Xiaoli Zhao

    (School of Business Administration, China University of Petroleum-Beijing, Beijing 102246, China)

  • Haitao Yin

    (Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200052, China)

Abstract

This paper examines how China’s power market reform influences the stock price volatility of listed power companies. We use the Iterative Cumulative Sums of Squares (ICSS) algorithm to identify structural breakpoints in stock prices, then analyze the characteristics of stock price volatility based on the GARCH model and report the impact of power regulation on stock price fluctuations based on the Autoregressive Distributed Lag (ARDL) model. Using data on power stock price index followed by industrial policy issued between 2006 and 2012, we find that: (1) three structural breaks in China’s power stock price volatility were related to the promulgation of power market reform policies; (2) industrial policies promote the reduction of power stock price fluctuations and its impact on power stock price volatility is consistent in the long run; and (3) the recent policies related to renewable energy do not have a very significant impact on the power stock market.

Suggested Citation

  • Ye Fan & Zhicheng Zhang & Xiaoli Zhao & Haitao Yin, 2018. "Interaction between Industrial Policy and Stock Price Volatility: Evidence from China’s Power Market Reform," Sustainability, MDPI, vol. 10(6), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:6:p:1719-:d:148836
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    Cited by:

    1. Wang, Yongli & Zhang, Fuli & Zhang, Yuanyuan & Wang, Xiaohai & Fan, Lisha & Song, Fuhao & Ma, Yuze & Wang, Shuo, 2019. "Chinese power-grid financial capacity based on transmission and distribution tariff policy: A system dynamics approach," Utilities Policy, Elsevier, vol. 60(C), pages 1-1.
    2. Wang, Ze & Gao, Xiangyun & An, Haizhong & Tang, Renwu & Sun, Qingru, 2020. "Identifying influential energy stocks based on spillover network," International Review of Financial Analysis, Elsevier, vol. 68(C).
    3. Zhifeng Dai & Huiting Zhou, 2020. "Prediction of Stock Returns: Sum-of-the-Parts Method and Economic Constraint Method," Sustainability, MDPI, vol. 12(2), pages 1-13, January.

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