Outlier detection in the GARCH (1,1) model

Philip Hans Franses and Dick van Dijk

No EI 9926-/A, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute

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.

Keywords: autoregressive conditional heteroskedasticity; forecasting volatility; outliers (search for similar items in EconPapers)
Date: 1999-07-05
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Persistent link: https://EconPapers.repec.org/RePEc:ems:eureir:1597

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