Measuring the response of clean energy stock price volatility to extreme shocks

Li Zhang, Lu Wang, Lijuan Peng and Keyu Luo

Renewable Energy, 2023, vol. 206, issue C, 1289-1300

Abstract: The development of clean energy has made the green finance market attractive to investors, but existing studies lack an analysis of the impact of uncertain environmental shocks on the volatility of green finance stocks. This paper focuses on the impact of major events on clean energy stock returns by using a mixed-frequency model. Specifically, to identify asymmetric effects and extreme information spillovers, we construct extreme positive returns/positive returns and extreme negative returns/negative returns, respectively. The in-sample findings suggest that asymmetric effects and extreme shocks contain valid information about future clean energy stock price volatility. The out-of-sample analysis shows that the extended model can successfully predict the volatility of clean energy stock markets. More importantly, the extended model can generate predictive gains from both statistical and economic aspects. This study can provide a reference for implementing and adjusting energy policies, which is important for optimizing the energy structure and developing the green energy industry.

Keywords: Extreme events; Asymmetric effects; Volatility forecasting; Mixed-frequency model (search for similar items in EconPapers)
JEL-codes: C32 C53 Q47 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:206:y:2023:i:c:p:1289-1300

DOI: 10.1016/j.renene.2023.02.066

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