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Forecasting US Stock Market Volatility: Evidence from ESG and CPU indices

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  • Ghani, Usman
  • Zhu, Bo
  • Qin, Quande
  • Ghani, Maria

Abstract

This study investigates the predictability of U.S. stock market volatility using environmental, social, and governance (ESG) and climate policy uncertainty (CPU) indices based on the Markov-regime GARCH-MIDAS model. Our out-of-sample results show that ESG and CPU information is useful to forecast the U.S. stock market volatility. Notably, the ESG index proved to be a more powerful predictor for volatility estimation. Lastly, we ensure the reliability of the study's results through various robustness checks, including the model confidence set (MCS) method. These results suggest that investors and policymakers should consider carefully the impact of ESG and CPU risk on financial decision making and policy realms.

Suggested Citation

  • Ghani, Usman & Zhu, Bo & Qin, Quande & Ghani, Maria, 2024. "Forecasting US Stock Market Volatility: Evidence from ESG and CPU indices," Finance Research Letters, Elsevier, vol. 59(C).
    • Handle: RePEc:eee:finlet:v:59:y:2024:i:c:s1544612323011832
    DOI: 10.1016/j.frl.2023.104811
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    References listed on IDEAS

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