Empirical Analysis of SSE 50 Index Volatility Based on GARCH Model

Abstract

Volatility is an important index for measuring the risk in the financial market. The research on the volatility of the financial market is the basis of risk prevention and asset pricing. The volatility of the market is predicted by using financial time series analysis and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model. This paper uses the 5 min closing price of the Shanghai 50 index from January 2021 to December 2021 as the small sample data, and the daily closing price of the Shanghai 50 index from January 2012 to December 2020 as the large sample data. The large sample data of SSE 50 index are divided into five sample groups with different interval lengths, and GARCH models are established to empirically analyze the volatility of its return. The “realized” volatility method is used to process the small sample data of the Shanghai Stock Exchange 50 index, and the estimated value of volatility is obtained as the substitute for the real value. The predicted value of five groups of data is compared with the real value, and the influence of sample size on the predicted value is analyzed. The results show that GARCH’s estimation coefficient is very significant and has the expected characteristics, which verifies the existence of persistent volatility aggregation, and the daily return fluctuation of the closing price of the Shanghai 50 index is largely affected by the news about volatility and lagging volatility in the previous period. This result shows that the daily return of the Shanghai Stock Exchange 50 index has sustained high volatility, the investment risk of the Shanghai stock market increases, and the regulatory authorities should take appropriate measures to prevent systemic risks.