Robust Estimates for Arch Processes

In this paper, we present two robust estimates for ARCH(p) models: A - and filtered A-estimates. These are defined by the minimization of conveniently robustified likelihood functions. The robustification is achieved by replacing the mean square error of the standardized observations with the square of a robust A-scale estimate in the reduced form of the Gaussian likelihood function. The robust filtering procedure avoids the propagation of the effect of one outlier on subsequent conditional variances. A Monte-Carlo study shows that the maximum likelihood estimate practically collapses when there is only a small percentage of outlier contamination, while both robust estimates perform much better