GARCH模型并不一定要求自回归的ERROR（下面的V)为正态分布，和什么分布无关，关键是ERROR的VOLATILITY（conditional error variance）是否是 a function of the past realizations of the series.

The generalized autoregressive conditional heteroscedasticity (GARCH) model is one approach to modeling time series with heteroscedastic errors. The GARCH regression model with autoregressive errors is

This model combines the mth-order autoregressive error model with the GARCH(p,q) variance model. It is denoted as the AR(m)-GARCH(p,q) regression model.