In this paper, we detect and correct abnormal returns in 17 French stocks returns and the French index CAC40 from additive-outlier detection method in GARCH models developed by Franses and Ghijsels (1999) and extended to innovative outliers by Charles and Darne (2005). We study the effects of outlying observations on several popular econometric tests. Moreover, we show that the parameters of the equation governing the volatility dynamics are biased when we do not take into account additive and innovative outliers. Finally, we show that the volatility forecast is better when the data are cleaned of outliers for several step-ahead forecasts (short, medium- and long-term) even if we consider a GARCH-t process

Forecasting volatility with outliers in GARCH models

https://iopscience.iop.org/article/10.1088/1757-899X/697/1/012012/pdf

Modeling and simulation of manufacture sector data in Malaysia with detection of outliers: An ARMA-GARCH approach

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Modeling and simulation of manufacture sector data in Malaysia with detection of outliers: An ARMA-GARCH approach