Classification of GARCH time series: an empirical investigation

• Autores: T. Kalantzis, D. Papanastassiou
• Localización: Applied financial economics, ISSN 0960-3107, Vol. 18, Nº. 7-9, 2008, págs. 759-764
• Idioma: inglés
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• Resumen

  • 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.