ISSN: 2222-6990
Open access
This study conducts an empirical analysis of the volatility characteristics of SSE, SZSE and CNI using GARCH model, aiming to investigate the dynamic volatility patterns of the Chinese stock market and its response to external shocks. The findings reveal that the volatility of the Chinese stock market exhibits significant persistence, asymmetry, and sensitivity to external disturbances. Among the three indices, SSE, which primarily consists of large capitalization enterprises, demonstrates the lowest volatility, whereas CNI, which includes a greater proportion of small and medium sized oriented firms, exhibits the highest level of volatility. Furthermore, major financial and economic disruptions, such as 2015 stock market crash, 2018 trade conflict between China and the US, and 2020 outbreak of the COVID-19 pandemic, significantly increased market volatility, with CNI being the most affected and SSE remaining relatively stable. Based on these findings, this study suggests enhancing market risk monitoring systems by leveraging financial technology to improve early warning mechanisms and implementing appropriate regulatory measures in extreme market conditions. Additionally, it advocates for optimizing financial policies and promoting long term investments by encouraging greater participation from institutional investors. Furthermore, a differentiated regulatory approach should be adopted, requiring stricter disclosure and governance standards for highly volatile market segments. Lastly, strengthening international cooperation is essential to reduce uncertainties arising from external economic disturbances and ensure overall market stability. This study provides valuable insights for investors and regulators, contributing to improved risk management and the resilience of financial markets.
Ardia, D., Bluteau, K., & Rüede, M. (2019). Regime changes in Bitcoin GARCH volatility dynamics. Finance Research Letters, 29, 266-271.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3), 307-327.
Bollerslev, T., Hood, B., Huss, J., & Pedersen, L. H. (2018). Risk everywhere: Modeling and managing volatility. The Review of Financial Studies, 31(7), 2729-2773.
Caporale, G. M., & Zekokh, T. (2019). Modelling volatility of cryptocurrencies using Markov-Switching GARCH models. Research in International Business and Finance, 48, 143-155.
Carpenter, J. N., Lu, F., & Whitelaw, R. F. (2021). The real value of China’s stock market. Journal of Financial Economics, 139(3), 679-696.
Fakhfekh, M., & Jeribi, A. (2020). Volatility dynamics of crypto-currencies’ returns: Evidence from asymmetric and long memory GARCH models. Research in International Business and Finance, 51, 101075.
Harvey, A., & Lange, R. J. (2018). Modeling the Interactions between Volatility and Returns using EGARCH?M. Journal of Time Series Analysis, 39(6), 909-919.
Lang, Q., Wang, J., Ma, F., Huang, D., & Ismail, M. W. M. (2021). Is Baidu index really powerful to predict the Chinese stock market volatility? New evidence from the internet information. China Finance Review International, 13(2), 263-284.
Leippold, M., Wang, Q., & Zhou, W. (2022). Machine learning in the Chinese stock market. Journal of Financial Economics, 145(2), 64-82.
Li, X., & Wei, Y. (2018). The dependence and risk spillover between crude oil market and China stock market: New evidence from a variational mode decomposition-based copula method. Energy Economics, 74, 565-581.
Lin, Z. (2018). Modelling and forecasting the stock market volatility of SSE Composite Index using GARCH models. Future Generation Computer Systems, 79, 960-972.
Pan, L., & Mishra, V. (2018). Stock market development and economic growth: Empirical evidence from China. Economic Modelling, 68, 661-673.
Peng, Z., Xiong, K., & Yang, Y. (2024). Segmentation of the Chinese stock market: A review. Journal of Economic Surveys, 38(4), 1156-1198.
Xu, Y., & Lien, D. (2022). Forecasting volatilities of oil and gas assets: A comparison of GAS, GARCH, and EGARCH models. Journal of Forecasting, 41(2), 259-278.
Zeng, X., Hu, Y., Pan, C., & Hou, Y. (2024). Exploring Systemic Risk Dynamics in the Chinese Stock Market: A Network Analysis with Risk Transmission Index. Risks, 12(3), 56.
Zhu, Y., Taasim, S. I., & Cai, J. (2025). Volatility of the Chinese Stock Market: Evidence from the GARCH Model. International Journal of Academic Research in Business and Social Sciences, 15(2), 1574–1583.
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