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Do global COVOL and geopolitical risks affect clean energy prices? Evidence from explainable artificial intelligence models

Author

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  • Sami Ben Jabeur

    (UR CONFLUENCE : Sciences et Humanités (EA 1598) - UCLy - UCLy (Lyon Catholic University), ESDES - ESDES, Lyon Business School - UCLy - UCLy - UCLy (Lyon Catholic University))

  • Yassine Bakkar
  • Oguzhan Cepni

Abstract

We investigate the impact of global common volatility and geopolitical risks on clean energy prices. Our study utilizes daily data from January 1, 2001, to March 18, 2024. Using a new framework based on explainable artificial intelligence (XAI) methods, our findings demonstrate that the COVOL index outperforms the geopolitical risk index in accurately predicting clean energy prices. Furthermore, the Extreme Trees algorithm shows superior performance compared to traditional regression techniques. Our findings indicate that XAI improves transparency, thereby making a substantial contribution to agile decision-making in predicting clean energy prices. Practitioners, including investors and portfolio managers, can enhance investment decisions and manage systemic risks by incorporating COVOL into their risk assessment and asset allocation models.

Suggested Citation

  • Sami Ben Jabeur & Yassine Bakkar & Oguzhan Cepni, 2025. "Do global COVOL and geopolitical risks affect clean energy prices? Evidence from explainable artificial intelligence models," Post-Print hal-05451353, HAL.
    • Handle: RePEc:hal:journl:hal-05451353
    DOI: 10.1016/j.eneco.2024.108112
    as

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