International Journal of Frontiers in Sociology, 2025, 7(6); doi: 10.25236/IJFS.2025.070603.
Zeyu Fan, Aihua Wang
College of Business and Economics, Shanghai Business School, Shanghai, China
This study aims to dynamically predict the market risk of the CSI 500 Index using the GARCH model. After analyzing the data, it is found that the daily return series of the CSI 500 Index does not follow a normal distribution, prompting the selection of a GARCH model with a student’s t-distribution. The GARCH (1,1)-t model is identified as the optimal model through fitting and diagnostic testing. The model is then used for dynamic market risk forecasting, with Value at Risk (VaR) applied for risk assessment from March 2024 to March 2025. The model's reliability is verified through Kupiec’s likelihood ratio backtesting, evaluating VaR exception frequencies at multiple confidence levels. Based on the findings, the study offers recommendations: investors should strengthen risk awareness, use derivatives for hedging, and adjust investment strategies based on the GARCH model's volatility predictions; financial institutions should develop intelligent risk management tools and optimize asset allocation; and regulators should enhance dynamic risk monitoring and promote the digital transformation of financial institutions.
Financing GARCH Model, CSI 500 Index, Market Risk, Dynamic Forecasting
Zeyu Fan, Aihua Wang. The Dynamic Prediction of Market Risk Based on the GARCH Model for the CSI 500 Index. International Journal of Frontiers in Sociology (2025), Vol. 7, Issue 6: 13-20. https://doi.org/10.25236/IJFS.2025.070603.
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