Classification of GARCH time series: an empirical investigation

We examine a discrimination rule for time series data generated by a GARCH(1,1) process that classifies a sample into a group in terms of its unconditional variance. A simulation study indicates that our rule is more efficient than a benchmark rule in most cases, except from a range of alternatives lying on the right side of the null. This range becomes shorter for parameter values approaching the stationarity region bound. The rule is robust in model misspecification.