Template-Type: ReDIF-Paper 1.0
Author-Name: Franses, Ph.H.B.F.
Author-Name-Last: Franses
Author-Name-First: Philip Hans
Author-Person: pfr226
Author-Name: van Dijk, D.J.C.
Author-Name-Last: van Dijk
Author-Name-First: Dick
Author-Person: pva27
Title: Outlier detection in the GARCH (1,1) model
Abstract: 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.
Creation-Date: 1999-07-05
File-URL: https://repub.eur.nl/pub/1597/feweco19990708094440.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI 9926-/A
Keywords: autoregressive conditional heteroskedasticity, forecasting volatility, outliers
Handle: RePEc:ems:eureir:1597