Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/86376
Authors: 
Doornik, Jurgen A.
Ooms, Marius
Year of Publication: 
2005
Series/Report no.: 
Tinbergen Institute Discussion Paper 05-092/4
Abstract: 
We present a new procedure for detecting multiple additive outliers in GARCH(1,1) models at unknown dates. The outlier candidates are the observations with the largest standardized residual. First, a likelihood-ratio based test determines the presence and timing of an outlier. Next, a second test determines the type of additive outlier (volatility or level). The tests are shown to be similar with respect to the GARCH parameters. Their null distribution can be easily approximated from an extreme value distribution, so that computation of p-values does not require simulation. The procedure outperforms alternative methods, especially when it comes to determining the date of the outlier. We apply the method to returns of the Dow Jones index, using monthly, weekly, and daily data. The procedure is extended and applied to GARCH models with Student-t distributed errors.
Subjects: 
Dummy variable
Generalized Autoregressive Conditional Heteroskedasticity
GARCH-t
Outlier detection
Extreme value distribution
JEL: 
C22
C52
G10
Document Type: 
Working Paper

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