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Predicting volatility in China's clean energy sector: Advantages of the carbon transition risk

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  • Chen, Wang
  • Chen, Zhu
  • Luo, Qin

Abstract

This paper develops a market-level carbon transition risk concern index and examines its ability to predict the volatility of China's clean energy market along with eight economic policy uncertainty indices. The results suggest that the carbon transition risk concern and global economic policy uncertainty index significantly predict the volatility of China's clean energy market. Carbon transition risk concern is the primary driver of market volatility; its extended model demonstrates increased predictive accuracy. Robustness tests confirm the reliability of the findings.

Suggested Citation

  • Chen, Wang & Chen, Zhu & Luo, Qin, 2025. "Predicting volatility in China's clean energy sector: Advantages of the carbon transition risk," Finance Research Letters, Elsevier, vol. 72(C).
    • Handle: RePEc:eee:finlet:v:72:y:2025:i:c:s1544612324015630
    DOI: 10.1016/j.frl.2024.106534
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    as
    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Tim Bollerslev & Benjamin Hood & John Huss & Lasse Heje Pedersen, 2018. "Risk Everywhere: Modeling and Managing Volatility," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2729-2773.
    3. Zhang, Jiaming & Xiang, Yitian & Zou, Yang & Guo, Songlin, 2024. "Volatility forecasting of Chinese energy market: Which uncertainty have better performance?," International Review of Financial Analysis, Elsevier, vol. 91(C).
    4. Andersen, Torben G & Bollerslev, Tim, 1997. "Heterogeneous Information Arrivals and Return Volatility Dynamics: Uncovering the Long-Run in High Frequency Returns," Journal of Finance, American Finance Association, vol. 52(3), pages 975-1005, July.
    5. Karel Janda & Ladislav Kristoufek & Binyi Zhang, 2021. "Return and volatility spillovers between Chinese and U.S. Clean Energy Related Stocks: Evidence from VAR-MGARCH estimations," FFA Working Papers 4.001, Prague University of Economics and Business, revised 17 Jan 2022.
    6. Qu, Fang & Chen, Yufeng & Zheng, Biao, 2021. "Is new energy driven by crude oil, high-tech sector or low-carbon notion? New evidence from high-frequency data," Energy, Elsevier, vol. 230(C).
    7. Wen, Fenghua & Gong, Xu & Cai, Shenghua, 2016. "Forecasting the volatility of crude oil futures using HAR-type models with structural breaks," Energy Economics, Elsevier, vol. 59(C), pages 400-413.
    8. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    9. Kim, Y. Han (Andy), 2013. "Self attribution bias of the CEO: Evidence from CEO interviews on CNBC," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2472-2489.
    10. Zheng, Shuhong & Yang, Juan & Yu, Shiwei, 2021. "How renewable energy technological innovation promotes renewable power generation: Evidence from China's provincial panel data," Renewable Energy, Elsevier, vol. 177(C), pages 1394-1407.
    11. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    12. Kim Christensen & Mathias Siggaard & Bezirgen Veliyev, 2023. "A Machine Learning Approach to Volatility Forecasting," Journal of Financial Econometrics, Oxford University Press, vol. 21(5), pages 1680-1727.
    13. Henriques, Irene & Sadorsky, Perry, 2008. "Oil prices and the stock prices of alternative energy companies," Energy Economics, Elsevier, vol. 30(3), pages 998-1010, May.
    14. Barbara Rossi & Atsushi Inoue, 2012. "Out-of-Sample Forecast Tests Robust to the Choice of Window Size," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 432-453, April.
    15. Wang, Jiqian & Ma, Feng & Bouri, Elie & Zhong, Juandan, 2022. "Volatility of clean energy and natural gas, uncertainty indices, and global economic conditions," Energy Economics, Elsevier, vol. 108(C).
    16. David Ardia & Keven Bluteau & Kris Boudt & Koen Inghelbrecht, 2023. "Climate Change Concerns and the Performance of Green vs. Brown Stocks," Management Science, INFORMS, vol. 69(12), pages 7607-7632, December.
    17. Janda, Karel & Kristoufek, Ladislav & Zhang, Binyi, 2022. "Return and volatility spillovers between Chinese and U.S. clean energy related stocks," Energy Economics, Elsevier, vol. 108(C).
    18. Kumar, Surender & Managi, Shunsuke & Matsuda, Akimi, 2012. "Stock prices of clean energy firms, oil and carbon markets: A vector autoregressive analysis," Energy Economics, Elsevier, vol. 34(1), pages 215-226.
    19. Liang, Chao & Umar, Muhammad & Ma, Feng & Huynh, Toan L.D., 2022. "Climate policy uncertainty and world renewable energy index volatility forecasting," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    20. Jiang, Fuwei & Lee, Joshua & Martin, Xiumin & Zhou, Guofu, 2019. "Manager sentiment and stock returns," Journal of Financial Economics, Elsevier, vol. 132(1), pages 126-149.
    21. Wang, Lu & Zhao, Chenchen & Liang, Chao & Jiu, Song, 2022. "Predicting the volatility of China's new energy stock market: Deep insight from the realized EGARCH-MIDAS model," Finance Research Letters, Elsevier, vol. 48(C).
    22. Dutta, Anupam & Bouri, Elie & Noor, Md Hasib, 2018. "Return and volatility linkages between CO2 emission and clean energy stock prices," Energy, Elsevier, vol. 164(C), pages 803-810.
    23. Paye, Bradley S., 2012. "‘Déjà vol’: Predictive regressions for aggregate stock market volatility using macroeconomic variables," Journal of Financial Economics, Elsevier, vol. 106(3), pages 527-546.
    24. Luo, Qin & Ma, Feng & Wang, Jiqian & Wu, You, 2024. "Changing determinant driver and oil volatility forecasting: A comprehensive analysis," Energy Economics, Elsevier, vol. 129(C).
    25. Wilms, Ines & Rombouts, Jeroen & Croux, Christophe, 2021. "Multivariate volatility forecasts for stock market indices," International Journal of Forecasting, Elsevier, vol. 37(2), pages 484-499.
    26. Ma, Feng & Liao, Yin & Zhang, Yaojie & Cao, Yang, 2019. "Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks," Journal of Empirical Finance, Elsevier, vol. 52(C), pages 40-55.
    27. Sévi, Benoît, 2014. "Forecasting the volatility of crude oil futures using intraday data," European Journal of Operational Research, Elsevier, vol. 235(3), pages 643-659.
    28. Liang, Chao & Wang, Lu & Duong, Duy, 2024. "More attention and better volatility forecast accuracy: How does war attention affect stock volatility predictability?," Journal of Economic Behavior & Organization, Elsevier, vol. 218(C), pages 1-19.
    29. Sun, Chuanwang & Ding, Dan & Fang, Xingming & Zhang, Huiming & Li, Jianglong, 2019. "How do fossil energy prices affect the stock prices of new energy companies? Evidence from Divisia energy price index in China's market," Energy, Elsevier, vol. 169(C), pages 637-645.
    30. Lyócsa, Štefan & Todorova, Neda, 2024. "Forecasting of clean energy market volatility: The role of oil and the technology sector," Energy Economics, Elsevier, vol. 132(C).
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