Outlier Detection in the GARCH(1,1) Model.

In this paper the issue of detecting and handling outliers in the GARCH(1,1) model is addressed. Simulation evidence shows that neglecting even a single outlier has a dramatic on parameter estimates. To detect and correct for outliers, we propose an adaptation of the iterative in Chen and Liu (1993, JASA). We generate the critical values for the relevant test statistic, and we evaluate our method in an extensive simulation study. An application to several weekly stock return series shows that correcting for a few outliers yields substantial improvements in out-of-sample forecasts.