Climate policy uncertainty and realized volatility across tranquil and economic stress regimes

Abstract

This study investigates the out-of-sample impact of climate policy uncertainty (CPU) on realized volatility across tranquil and economic stress regimes. Using data from August 2010 to June 2025, we estimate multiple autoregressive (AR) models with and without CPU as an exogenous predictor. Furthermore, rolling window estimation is used to ensure the robustness across changing market conditions. The results show that basic AR models provide limited forecasting power for most sectors, particularly at longer horizons and during instability. However, incorporating CPU improves risk forecasts, especially for sectors highly exposed to environmental regulations such as Energy, Electricity, and Utilities. Thus, the influence of CPU on volatility is both sector-specific and sensitive to market regimes. The study concludes with policy implications and recommendations for key financial stakeholders.