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Extreme weather events as the main driver of electricity price volatility in Italy: A GARCH-MIDAS approach with machine learning-based variable selection

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  • Guerzoni, Marco
  • Riso, Luigi
  • Zoia, M. Grazia

Abstract

This paper investigates the impact of extreme weather events on electricity price volatility in Italy, employing a novel combination of advanced econometric techniques and a robust variable selection process. First, we provide empirical evidence that extreme weather events significantly predict electricity price volatility. We compile a comprehensive set of economic and financial variables known in the literature to influence electricity price volatility and apply the Best Path Algorithm (BPA) for variable selection, identifying the most relevant predictors. A Granger causality analysis of the selected variables confirms that extreme weather events not only emerge as the primary factor driving volatility but also exhibit a clear unidirectional causal relationship.

Suggested Citation

  • Guerzoni, Marco & Riso, Luigi & Zoia, M. Grazia, 2026. "Extreme weather events as the main driver of electricity price volatility in Italy: A GARCH-MIDAS approach with machine learning-based variable selection," The North American Journal of Economics and Finance, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:ecofin:v:81:y:2026:i:c:s1062940825001524
    DOI: 10.1016/j.najef.2025.102512
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    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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