Abstract
Based on high frequency data (5 minutes) and low frequency data (day), this paper models and analyses the Shanghai Stock Exchange Index (SH1A0001). Through the stationarity test and the ARCH effect test, the ARMA (1,1) – GARCH (1,1) model is established. Under the assumption of the error term obeys the Laplace distribution, the estimation is obtained. By the result of forward prediction, it is found that the result of model prediction in high frequency data is better than those in low frequency data.
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