Forecasting Chinese Stock Market Volatility Using the Range and Climate Policy Uncertainty
DOI:
https://doi.org/10.6981/FEM.202607_7(7).0004Keywords:
Volatility Forecasting; Range; Climate Policy Uncertainty; Mixed-Frequency Sampling.Abstract
Within the conditional autoregressive range (CARR) framework, this paper introduces climate policy uncertainty (CPU) as a low-frequency macroeconomic variable and develops a CARR-MIDAS-CPU model for modeling and forecasting Chinese stock market volatility. Using data on the SSE 50 Index, the empirical results show that CPU has a significant effect on long-run stock market volatility. In terms of in-sample fitting performance, range-based CARR models consistently outperform their return-based GARCH counterparts, corroborating the informational value of intraday extreme price data in volatility modeling. We further conduct an out-of-sample forecasting comparison between the CARR-MIDAS-CPU model and a set of competing benchmark specifications, adopting two robust loss functions as the core evaluation criteria. Forecasting results demonstrate that the integration of climate policy uncertainty (CPU) delivers incremental predictive power for Chinese stock market volatility, and the CARR-MIDAS-CPU model achieves the highest predictive accuracy among all specifications under examination. Overall, the proposed model effectively exploits range-based volatility information and climate policy uncertainty to enhance forecasting performance, and offers a practical econometric tool to support risk management for both policymakers and market investors.
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