Outlier Detection in GARCH Models

Jurgen Doornik and Marius Ooms

No 05-092/4, Tinbergen Institute Discussion Papers from Tinbergen Institute

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.

Keywords: Dummy variable; Generalized Autoregressive Conditional Heteroskedasticity; GARCH-t; Outlier detection; Extreme value distribution (search for similar items in EconPapers)
JEL-codes: C22 C52 G10 (search for similar items in EconPapers)
Date: 2005-10-13
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (23)

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Related works:
Working Paper: Outlier Detection in GARCH Models (2005)
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Persistent link: https://EconPapers.repec.org/RePEc:tin:wpaper:20050092

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