IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v51y2023ics1544612322006821.html
   My bibliography  Save this article

Climate policy uncertainty and stock market volatility: Evidence from different sectors

Author

Listed:
  • Lv, Wendai
  • Li, Bin

Abstract

This paper mainly investigates whether the climate policy uncertainty index (CPU) can predict the volatility of Chinese stock market volatility considering different sectors. Out-of-sample results show that climate policy uncertainty index can have a greater effect on the utility sector. We also investigate the effects of CPU based on longer horizons, different volatility levels and the COVID-19 pandemic. This paper tries to provide new evidence based on sector stock indices.

Suggested Citation

  • Lv, Wendai & Li, Bin, 2023. "Climate policy uncertainty and stock market volatility: Evidence from different sectors," Finance Research Letters, Elsevier, vol. 51(C).
    • Handle: RePEc:eee:finlet:v:51:y:2023:i:c:s1544612322006821
    DOI: 10.1016/j.frl.2022.103506
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612322006821
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2022.103506?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    2. Ströbel, Johannes & Wurgler, Jeffrey, 2021. "What do you think about climate finance?," CEPR Discussion Papers 16622, C.E.P.R. Discussion Papers.
    3. Lu, Xinjie & Ma, Feng & Wang, Jianqiong & Dong, Dayong, 2022. "Singlehanded or joint race? Stock market volatility prediction," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 734-754.
    4. 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.
    5. Zhao, Jinsong & Zhou, Boxu & Li, Xinrui, 2022. "Do good intentions bring bad results? Climate finance and economic risks," Finance Research Letters, Elsevier, vol. 48(C).
    6. 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.
    7. Lee, Chi-Chuan & Li, Xinrui & Yu, Chin-Hsien & Zhao, Jinsong, 2022. "The contribution of climate finance toward environmental sustainability: New global evidence," Energy Economics, Elsevier, vol. 111(C).
    8. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    9. Baumöhl, Eduard & Lyócsa, Štefan, 2017. "Directional predictability from stock market sector indices to gold: A cross-quantilogram analysis," Finance Research Letters, Elsevier, vol. 23(C), pages 152-164.
    10. Liang, Chao & Tang, Linchun & Li, Yan & Wei, Yu, 2020. "Which sentiment index is more informative to forecast stock market volatility? Evidence from China," International Review of Financial Analysis, Elsevier, vol. 71(C).
    11. Ma, Feng & Lu, Xinjie & Liu, Jia & Huang, Dengshi, 2022. "Macroeconomic attention and stock market return predictability," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    12. Mazur, Mieszko & Dang, Man & Vega, Miguel, 2021. "COVID-19 and the march 2020 stock market crash. Evidence from S&P1500," Finance Research Letters, Elsevier, vol. 38(C).
    13. Feng Ma & Chao Liang & Yuanhui Ma & M.I.M. Wahab, 2020. "Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1277-1290, December.
    14. 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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Jiqian & Li, Liang, 2023. "Climate risk and Chinese stock volatility forecasting: Evidence from ESG index," Finance Research Letters, Elsevier, vol. 55(PA).
    2. Treepongkaruna, Sirimon & Chan, Kam Fong & Malik, Ihtisham, 2023. "Climate policy uncertainty and the cross-section of stock returns," Finance Research Letters, Elsevier, vol. 55(PA).
    3. Huthaifa Sameeh Alqaralleh, 2023. "The extreme spillover from climate policy uncertainty to the Chinese sector stock market: wavelet time-varying approach," Letters in Spatial and Resource Sciences, Springer, vol. 16(1), pages 1-17, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xu, Yongan & Duong, Duy & Xu, Hualong, 2023. "Attention! Predicting crude oil prices from the perspective of extreme weather," Finance Research Letters, Elsevier, vol. 57(C).
    2. Wang, Jiqian & Li, Liang, 2023. "Climate risk and Chinese stock volatility forecasting: Evidence from ESG index," Finance Research Letters, Elsevier, vol. 55(PA).
    3. Zhang, Dan & Li, Biangxiang, 2022. "What can we learn from financial stress indicator?," Finance Research Letters, Elsevier, vol. 50(C).
    4. Gong, Xue & Zhang, Weiguo & Wang, Junbo & Wang, Chao, 2022. "Investor sentiment and stock volatility: New evidence," International Review of Financial Analysis, Elsevier, vol. 80(C).
    5. Su, Yuandong & Lu, Xinjie & Zeng, Qing & Huang, Dengshi, 2022. "Good air quality and stock market returns," Research in International Business and Finance, Elsevier, vol. 62(C).
    6. Liang, Chao & Xu, Yongan & Wang, Jianqiong & Yang, Mo, 2022. "Whether dimensionality reduction techniques can improve the ability of sentiment proxies to predict stock market returns," International Review of Financial Analysis, Elsevier, vol. 82(C).
    7. Nonejad, Nima, 2022. "Predicting equity premium out-of-sample by conditioning on newspaper-based uncertainty measures: A comparative study," International Review of Financial Analysis, Elsevier, vol. 83(C).
    8. Liu, Guangqiang & Guo, Xiaozhu, 2022. "Forecasting stock market volatility using commodity futures volatility information," Resources Policy, Elsevier, vol. 75(C).
    9. Nima Nonejad, 2021. "Bayesian model averaging and the conditional volatility process: an application to predicting aggregate equity returns by conditioning on economic variables," Quantitative Finance, Taylor & Francis Journals, vol. 21(8), pages 1387-1411, August.
    10. Dai, Zhifeng & Kang, Jie & Hu, Yangli, 2021. "Efficient predictability of oil price: The role of number of IPOs and U.S. dollar index," Resources Policy, Elsevier, vol. 74(C).
    11. Xu, Yongan & Wang, Jianqiong & Chen, Zhonglu & Liang, Chao, 2021. "Economic policy uncertainty and stock market returns: New evidence," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    12. Ma, Feng & Guo, Yangli & Chevallier, Julien & Huang, Dengshi, 2022. "Macroeconomic attention, economic policy uncertainty, and stock volatility predictability," International Review of Financial Analysis, Elsevier, vol. 84(C).
    13. Bai, Fan & Zhang, Yaqi & Chen, Zhonglu & Li, Yan, 2023. "The volatility of daily tug-of-war intensity and stock market returns," Finance Research Letters, Elsevier, vol. 55(PA).
    14. Gebka, Bartosz & Wohar, Mark E., 2019. "Stock return distribution and predictability: Evidence from over a century of daily data on the DJIA index," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 1-25.
    15. Chen, Jian & Jiang, Fuwei & Xue, Shuyu & Yao, Jiaquan, 2019. "The world predictive power of U.S. equity market skewness risk," Journal of International Money and Finance, Elsevier, vol. 96(C), pages 210-227.
    16. He, Mengxi & Wang, Yudong & Zeng, Qing & Zhang, Yaojie, 2023. "Forecasting aggregate stock market volatility with industry volatilities: The role of spillover index," Research in International Business and Finance, Elsevier, vol. 65(C).
    17. Chen, Jian & Jiang, Fuwei & Li, Hongyi & Xu, Weidong, 2016. "Chinese stock market volatility and the role of U.S. economic variables," Pacific-Basin Finance Journal, Elsevier, vol. 39(C), pages 70-83.
    18. Song, Ziyu & Gong, Xiaomin & Zhang, Cheng & Yu, Changrui, 2023. "Investor sentiment based on scaled PCA method: A powerful predictor of realized volatility in the Chinese stock market," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 528-545.
    19. Nima Nonejad, 2021. "Should crude oil price volatility receive more attention than the price of crude oil? An empirical investigation via a large‐scale out‐of‐sample forecast evaluation of US macroeconomic data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 769-791, August.
    20. Nonejad, Nima, 2022. "Understanding the conditional out-of-sample predictive impact of the price of crude oil on aggregate equity return volatility," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:51:y:2023:i:c:s1544612322006821. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.