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Effects of COVID-19 vaccination programs on EU carbon price forecasts: Evidence from explainable machine learning

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  • Yang, Cai
  • Zhang, Hongwei
  • Weng, Futian

Abstract

The COVID-19 pandemic continues to destroy the carbon market. To alleviate the situation, governments launched vaccination program campaigns. This study aims to predict two carbon pricing features––return and volatility––considering the impacts of the COVID-19 vaccination program. The present study applies the SHAPley Additive exPlanations method of model analysis and interpretability to determine the forces that predict carbon pricing. Our results show that compared with the volatility of the carbon market, the number of daily vaccinations has better predictive performance in terms of carbon pricing. However, compared with other related control factors, the predictive contribution of the COVID-19 vaccination program to volatility is greater than the return of the carbon market. In addition, a smaller number of daily vaccinations correspond to higher carbon market volatility and lower returns. Our results have crucial implications for investors and policymakers in stabilizing and promoting the carbon market during the COVID-19 pandemic; moreover, our results provide a reference for formulating new COVID-19 vaccination-related policies.

Suggested Citation

  • Yang, Cai & Zhang, Hongwei & Weng, Futian, 2024. "Effects of COVID-19 vaccination programs on EU carbon price forecasts: Evidence from explainable machine learning," International Review of Financial Analysis, Elsevier, vol. 91(C).
    • Handle: RePEc:eee:finana:v:91:y:2024:i:c:s1057521923004696
    DOI: 10.1016/j.irfa.2023.102953
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    2. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.

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