Incremental Predictive Information of Macroeconomic Variables for CSI 300 Volatility: Evidence from GARCH-MIDAS Models

Abstract

Based on daily returns of the CSI 300 index and monthly macroeconomic variables from January 2005 to December 2025, this paper examines whether low-frequency macroeconomic information provides incremental value for stock market volatility forecasting. A GARCH-MIDAS model is employed to decompose daily return volatility into short-run and long-run components. Economic policy uncertainty, industrial value added, M2, CPI, PPI, and a composite macroeconomic state factor are incorporated into the long-run volatility component. Out-of-sample forecasting results for January 2019 to December 2025 show that industrial value added and M2 are not only significantly positive in the long-run component equation, but also perform best under the QLIKE, MSE, and MAE loss functions. The composite macroeconomic state factor has some explanatory power, although it is weaker than industrial value added and M2. Economic policy uncertainty improves forecast accuracy, but no stable and significant positive effect of EPU on the long-run volatility component is found. Overall, real economic activity and monetary liquidity are important low-frequency macroeconomic information sources for understanding and forecasting the long-run volatility of the CSI 300 index.