具有定期变化扰动的GARCH（1,1）模型参数评估

In practice, Financial Time Series have serious volatility cluster, that is large volatility tend to be concentrated in a certain period of time, and small volatility tend to be concentrated in another period of time. While GARCH models can well describe the dynamic changes of the volatility of financial time series, and capture the cluster and heteroscedasticity phenomena. At the beginning of this paper, the modification of maximum likelihood function has been constructed as the theoretical basis of this study. Secondly, estimate parameters of GARCH(1,1) model with residuals having regularly varying distributions with index > 0. Finally, the consistency and asymptotic normality of the estimates constructed are further proved.