Tail risk, large fluctuations and downfalls in renewable energy markets.

The renewable energy sector has expanded rapidly as countries pursue decarbonization goals, increasing investor exposure to market risk and extreme price movements. While prior research has examined downside risk, volatility, and dependence structures in clean energy markets, less is known about the statistical properties governing the decay of extreme returns and the existence of finite moments. This study addresses this gap by analyzing the heavy-tailed behavior of renewable and conventional energy equity indices using daily data from April 2005 to February 2025. We estimate tail indices to assess the degree of heavy-tailedness and to infer the existence of finite moments, relying on confidence-interval-based inference rather than point estimates. In addition to full-sample analysis, a recursive expanding-window approach is employed to examine the time variation of tail risk and its response to major market stress events. The results indicate that both renewable and conventional energy indices exhibit heavy-tailed return distributions consistent with power-law behavior. Confidence-interval-based inference supports the existence of finite first and second moments across all indices, while higher-order moments may be infinite, implying limitations for models that rely on skewness or kurtosis. The recursive analysis reveals pronounced increases in tail risk during periods of systemic stress, particularly during the global financial crisis, highlighting the state-dependent nature of extreme risk in energy markets. Overall, the findings emphasize the importance of statistically justified tail analysis for risk measurement, portfolio construction, and stress testing in renewable energy markets, complementing existing studies focused on volatility and dependence.