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Measuring the impact of climate risk on renewable energy stock volatility: A case study of G20 economies

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  • Zhang, Li
  • Liang, Chao
  • Huynh, Luu Duc Toan
  • Wang, Lu
  • Damette, Olivier

Abstract

The contemporary world faces significant challenges in energy crises and climate change. To analyze the relationship between energy and climate, we explore the influence of the climate-related attention of G20 countries on renewable energy stock volatility forecasting under the framework of the extended GARCH-MIDAS model. In the context of COP26, we further adopt natural language processing technology and shrinkage approach to obtain Google search volume for 107 climate-related keywords and then construct new climate risk attention indicators. The in-sample parameter estimation results show that the climate attention of G20 countries has a remarkable positive effect on the renewable energy stock market volatility. The out-of-sample results demonstrate that the climate attention of different countries exerts varying influences on the volatility of the renewable energy stock market. Climate risk and energy issues are among the serious challenges facing the 21st century, and reducing greenhouse gas emissions and finding cleaner energy is an urgent task. As the response to climate change necessitates diverse strategies in various countries, our research can offer valuable guidance and serve as a reference for national energy transitions and the selection of alternative energy solutions.

Suggested Citation

  • Zhang, Li & Liang, Chao & Huynh, Luu Duc Toan & Wang, Lu & Damette, Olivier, 2024. "Measuring the impact of climate risk on renewable energy stock volatility: A case study of G20 economies," Journal of Economic Behavior & Organization, Elsevier, vol. 223(C), pages 168-184.
    • Handle: RePEc:eee:jeborg:v:223:y:2024:i:c:p:168-184
    DOI: 10.1016/j.jebo.2024.05.005
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    Keywords

    Climate crisis; G20 economies; Climate risk attention; Renewable energy stock; Volatility forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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