Economic Policy Uncertainty and Volatility Forecasting in China’s Stock Market
DOI:
https://doi.org/10.6981/FEM.202607_7(7).0007Keywords:
Volatility Forecasting; Realized Volatility; Economic Policy Uncertainty; Mixed-Frequency Data Sampling.Abstract
This paper develops the GARCH-MIDAS-RV-EPU model, which integrates high-frequency realized volatility, low-frequency macro indicators and the exogenous Chinese Economic Policy Uncertainty (EPU) index to predict China’s stock market volatility. Using 5-minute high-frequency data of the Shanghai Composite Index (SSEC) and the Shenzhen Component Index (SZSEC), we find that the proposed model outperforms alternative specifications in return fitting and volatility description. With robust loss functions as evaluation criteria, we compare the out-of-sample volatility forecasting performance of all models. The empirical results show that incorporating high-frequency, low-frequency and EPU information significantly boosts volatility predictability, and our model exhibits the strongest forecasting power among all competing specifications.
Downloads
References
[1] Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007.
[2] Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
[3] Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.
[4] Pi, T. L. (2003). Empirical research on volatility clustering GARCH effect of Shanghai stock market in China. Journal of Management Science, 16(6), 31–35. (In Chinese)
[5] Li, Y. J., Zhu, H. Q., & Peng, Y. W. (2003). Volatility forecast of China's stock market based on GARCH family models. Mathematics in Practice and Theory, 33(11), 65–71. (In Chinese)
[6] Ghysels, E., Sinko, A., & Valkanov, R. (2007). Midas regressions: Further results and new directions. Econometric Reviews, 26(1–3), 53–90.
[7] Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. The Review of Economics and Statistics, 95(3), 776–797.
[8] Zheng, T. G., & Shang, Y. H. (2014). Measurement and prediction of stock market volatility based on macroeconomic fundamentals. The World Economy, 37(12), 118–139. (In Chinese)
[9] Lei, L. K., Yu, J., & Wei, Y. (2018). Research on economic policy uncertainty and China’s stock market volatility forecasting. Journal of Management Science in China, 21(6), 88–98. (In Chinese)
[10] Xia, T., & Wen, Y. C. (2018). Is economic uncertainty a factor of stock market volatility? - Analysis based on GARCH-MIDAS model. Chinese Journal of Management Science, 26(12), 1–11. (In Chinese)
[11] Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593–1636.
[12] Li, L. H. (2020). Research on stock market volatility forecasting under economic policy uncertainty: From the perspective of STR-GARCH-MIDAS model [Master’s thesis]. Southwest Jiaotong University. (In Chinese)
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Frontiers in Economics and Management
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
