Additive outliers, GARCH and forecasting volatility
The Generalized Autoregressive Conditional Heteroskedasticity [GARCH] model is often used for forecasting stock market volatility. It is frequently found, however, that estimated residuals from GARCH models have excess kurtosis, even when one allows for conditional t-distributed errors. In this paper we examine if this feature can be due to neglected additive outliers [AOs], where we focus on the out-of-sample forecasting properties of GARCH models for AO-corrected returns. We find that models for AO-corrected data yield substantial improvement over GARCH and GARCH-t models for the original returns, and that this improvement holds for various samples, two forecast evaluation criteria and four stock markets.
|Keywords||GARCH, additive outlier, forecasting volatility|
|Persistent URL||dx.doi.org/10.1016/S0169-2070(98)00053-3, hdl.handle.net/1765/2174|
|Journal||International Journal of Forecasting|
Franses, Ph.H.B.F, & Ghijsels, H. (1999). Additive outliers, GARCH and forecasting volatility. International Journal of Forecasting, 1–9. doi:10.1016/S0169-2070(98)00053-3