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Asymmetric impacts of energy market-related uncertainty on clean energy stock volatility: The role of extreme shocks

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Listed:
  • Liu, Yanchen
  • Yi, Siyu
  • Li, Sitong
  • Chen, Gengxuan

Abstract

Amid escalating uncertainties in the global energy transition and markets, clean energy stock volatility has become a critical concern for investors and policy-makers. This study applies two extended GARCH-MIDAS frameworks to analyse the effects of energy market-related uncertainty indicators—the global energy-related uncertainty index (GEUI), the oil price uncertainty index (OPU), and the crude oil volatility index (OVX)—on clean energy stock volatility. The results show that the GEUI exerts the most substantial influence, with the OPU and OVX having comparatively smaller effects. Further analysis reveals that positive extreme uncertainty shocks exert a stronger influence on stock market volatility than negative shocks, highlighting the heightened sensitivity of markets during periods of increasing uncertainty. Additionally, a leverage effect is observed, where past negative volatility has a greater influence on current volatility than positive events do. These findings offer empirical guidance for risk management and valuable insights for policy-makers addressing frequent uncertainty shocks in the clean energy sector.

Suggested Citation

  • Liu, Yanchen & Yi, Siyu & Li, Sitong & Chen, Gengxuan, 2025. "Asymmetric impacts of energy market-related uncertainty on clean energy stock volatility: The role of extreme shocks," International Review of Financial Analysis, Elsevier, vol. 103(C).
    • Handle: RePEc:eee:finana:v:103:y:2025:i:c:s1057521925002935
    DOI: 10.1016/j.irfa.2025.104206
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    More about this item

    Keywords

    Clean energy stocks; Volatility forecasting; Energy-related uncertainty; Extreme shocks;
    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
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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