Open AccessAn Empirical Analysis on the Volatility of Return of CSI 300 Index
Author(s) -
Jinling Liang,
Guofu Deng
Publication year - 2021
Publication title -
international journal of accounting and finance studies
Language(s) - English
Resource type - Journals
eISSN - 2576-201X
pISSN - 2576-2001
DOI - 10.22158/ijafs.v4n2p1
Subject(s) - heteroscedasticity , autoregressive conditional heteroskedasticity , econometrics , conditional variance , volatility (finance) , index (typography) , arch , stock market index , economics , statistics , mathematics , volatility clustering , stock market , computer science , geography , context (archaeology) , archaeology , world wide web
In order to better observe the trend of the stock market, this paper selects the daily closing price data of CSI 300 index from April 12, 2016 to September 30, 2021, and makes an empirical analysis on the logarithmic return of CSI 300 index. It is found that: (1) the return series of the CSI 300 index shows the statistical characteristics of peak, thick tail, bias, asymmetry and persistence. The ARMA (2,3) model can effectively fit the yield series and predict the future trend to a certain extent. (2) The residuals of ARMA model show obvious cluster effect and ARCH effect (conditional heteroscedasticity). GARCH (1,1) model can better fit the conditional heteroscedasticity, so as to eliminate the ARCH effect. (3) By constructing GARCH (1,1) model, it is found that the sum of ARCH term coefficient and GARCH term coefficient is very close to 1, indicating that GARCH process is wide and stable, the impact on conditional variance is lasting, and the market risk is large, that is, the impact plays an important role in all future forecasts.