We propose the EGARCH-MIDAS-CPU model, which incorporates the leverage effect and climate policy uncertainty (CPU) to model and forecast European Union allowance futures’ (EUAF) volatility. An empirical analysis based on the daily data of the EUAF price index and the monthly data of the CPU index using the EGARCH-MIDAS-CPU model shows that the EUAF’s volatility exhibits a leverage effect, and the CPU has a significantly negative impact on the EUAF’s volatility. Furthermore, out-of-sample analysis based on three loss functions and the Model Confidence Set (MCS) test suggests that EGARCH-MIDAS-CPU model yields more accurate out-of-sample volatility forecasting results than various competing models. There is room for further application of the model, such as this model could be applied to price carbon futures, so as to improve the liquidity of the carbon market and achieve carbon peak and carbon neutrality as soon as possible.

Intraday range (the difference between intraday high and low prices) is often used to measure volatility, which has proven to be a more efficient volatility estimator than the return‐based one. Meanwhile, a growing body of studies has found that economic policy uncertainty (EPU) has important impact on stock market volatility. In this paper, building on the range‐based volatility model, namely, the conditional autoregressive range (CARR) model, we introduce the CARR‐mixed‐data sampling (CARR‐MIDAS) model framework by considering intraday information to investigate the impact of EPU on the volatility of Chinese stock market and to explore the predictive ability of EPU for Chinese stock market. The empirical results show that both the China EPU (CEPU) and global EPU (GEPU) have a significantly negative effect on the long‐run volatility of Chinese stock market. Furthermore, we find that taking into account the CEPU and GEPU leads to substantial improvement in the ability to forecast the volatility of Chinese stock market. We also find that the CEPU provides superior volatility forecasts compared to the GEPU. Our findings are robust to different forecasting windows.