Outlier detection in GARCH models

Marius Ooms · Jurgen A. Doornik

41 citations0/0 files storedDigital Academic REpository of VU University Amsterdam (Vrije Universiteit Amsterdam)

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Financial Risk and Volatility Modeling Advanced Statistical Methods and Models Statistical and numerical algorithms

Abstract

Abstract We present a new procedure for detecting additive outliers in GARCH(1,1) models. The outlier candidate is the observation with the largest standardized residual. First, a likelihood-ratio based tests determines the presence of an outlier. Next, a second LR test determines the type of outlier (volatility or level). This procedure is shown to be approximately independent from the GARCH parameters, with a null distribution that can be easily approximated. 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 to cover GARCH models with Student-t distributed errors.

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References

1987A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return 2006Analysis of Financial Time Series 1995Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1996Statistical aspects of ARCH and stochastic volatility 1999ARCH Models and Financial Applications 1996Analytic derivatives and the computation of GARCH estimates 1998High Breakdown Point Conditional Dispersion Estimation with Application to S & P 500 Daily Returns Volatility

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2008Multimodality in GARCH regression models 2009Shifts in Individual Parameters of a GARCH Model 2009Understanding volatility dynamics in the EU-ETS market: lessons from the future

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