Research On The Volatility In Hu-Shen Stock Return By GARCH Model In Different Stages

In modern finance market, researchers found that most of the time-series, such as stock price, ratio, exchange rates and so on, the error series were non-autocorrelation, but the squared error series were autocorrelation, which indicate the variance or volatility were time-varying. But the OLS supposes the error series are non-autocorrelation and variances are consistent. So OLS is now not fit for making models and estimation for such economic variables. Auto Regressive Conditional Heteroskedasticity model catches the characters of this kind of economic variables. ARCH model is a kind of dynamic non-linear time series model. It reflects a special feature of economic variables-time-varying variances. Now ARCH model is being widely used in analysis of financial time series fields.In this paper, I regard Shanghai stock composite price index and Shenzhen Sub-component Index as the main study object, and use the GARCH models to describe the statistic character in two stages and the relations between the volume and price returns with the statistic software EViews 5.1. My results show that there are significantly volatility, excess kurtosis and heteroskedasticity, persistence and asymmetric effect in Chinese Stock Market. There is a positive relationship between price returns and risks. Moreover, the positive relationship and bi-direction linear Granger causality are maintained between price returns and volume. Volume contains parts of stock markets information.