<p>The increasing focus on sustainable finance has highlighted the critical need for accurate risk assessment in clean energy investments. However, existing research often overlooks the sector’s distinctive volatility characteristics, resulting in ineffective risk management approaches that fail to distinguish between the varied risk profiles associated with long and short positions in clean energy equities. This study addresses this gap by improving the forecasting accuracy of tail risk assessments through novel adaptations of existing volatility modeling frameworks. We demonstrate that different modeling paradigms, which assume different statistical properties for price volatility and return distributions, are required for accurate forecasting of long and short positions. Specifically, models incorporating asymmetric volatility responses and heavy-tailed distributions excel for long holdings, while models allowing for highly persistent volatility effects combined with skewed distributions perform best for short positions. This differentiated approach reflects the intrinsic asymmetries in clean energy markets. Our rigorous empirical investigation, spanning more than a decade and including severe market upheavals, reveals that these tailored models significantly outperform standard methods. The findings provide practical insights for investors and regulators by demonstrating how targeted modeling methodologies can effectively capture the complex dynamics of clean energy investments, thus supporting the broader goals of sustainable finance.</p>