caa a22 4500 378929798 CHVBK 20180305123626.0 cr unu---uuuuu 161128e20040518xx s 000 0 eng 10.2202/1558-3708.1224 doi (NATIONALLICENCE)gruyter-10.2202/1558-3708.1224 Grossi Luigi 1University of Parma, luigi.grossi@unipr.it Analyzing Financial Time Series through Robust Estimators [Elektronische Daten] [Luigi Grossi] In this paper we suggest an extension of the forward search methodology to GARCH models which are often used for forecasting stock market volatility. It is frequently found that estimated residuals from GARCH models have excess kurtosis, even when one allows for conditional t-distributed errors. Some papers have appeared on outlier detection in GARCH models but the proposed methods are iterative and may suffer from masking effects. The forward search is a method for determining the effect of outliers on fitted parameters and for detecting also masked outliers. In the case of GARCH models outliers are strictly related to extreme observations which are responsible for the well-known volatility clustering of financial returns. It is possible, through the forward search, to visualize the effect on estimated parameters of patches of extremal observations. ©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston Studies in Nonlinear Dynamics & Econometrics De Gruyter 8/2(2004-05-18) 8:2 2004 8 snde https://doi.org/10.2202/1558-3708.1224 text/html Onlinezugriff via DOI 1 research article jats NATIONALLICENCE 856 40 https://doi.org/10.2202/1558-3708.1224 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Grossi Luigi 1University of Parma, luigi.grossi@unipr.it NATIONALLICENCE 773 0- Studies in Nonlinear Dynamics & Econometrics De Gruyter 8/2(2004-05-18) 8:2 2004 8 snde CC0 http://creativecommons.org/publicdomain/zero/1.0 nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-gruyter