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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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  • Ben Jabeur, Sami
  • Bakkar, Yassine
  • Cepni, Oguzhan

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

  • Ben Jabeur, Sami & Bakkar, Yassine & Cepni, Oguzhan, 2025. "Do global COVOL and geopolitical risks affect clean energy prices? Evidence from explainable artificial intelligence models," Energy Economics, Elsevier, vol. 141(C).
    • Handle: RePEc:eee:eneeco:v:141:y:2025:i:c:s0140988324008211
    DOI: 10.1016/j.eneco.2024.108112
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    More about this item

    Keywords

    COVOL; Geopolitical risks; Global financial risk; Clean energy; Explainable Artificial Intelligence Models;
    All these keywords.

    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • F3 - International Economics - - International Finance
    • F4 - International Economics - - Macroeconomic Aspects of International Trade and Finance

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