Classification of GARCH time series: an empirical investigation

T. Kalantzis and D. Papanastassiou

Applied Financial Economics, 2008, vol. 18, issue 9, 759-764

Abstract: 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.

Date: 2008
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DOI: 10.1080/09603100701320160

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