The Volatility Research in CSI 300 Index Futures by Using High Frequency Data based on GARCH Model

By using the high frequency data in CSI 300 index futures, we research the pattern of CSI 300 index futures, and find that the returns of high frequency CSI 300 index futures have obvious volatility clustering effect, and there is peak and fat-tailed phenomenon. After that, the ARCH effect test is carried out on the residual error of the high frequency data, and the results show that the residuals have obvious ARCH effect. After eliminating the autocorrelation, the optimal GARCH model is established, and the adequacy of the model fit was verified. The fitting results show that GARCH can well describe the characteristics of high frequency volatility of CSI 300 index futures, the impact on the conditional variance, has a strong persistence, long memory effect is found in the volatility.