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Classification of GARCH time series: an empirical investigation

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  • T. Kalantzis
  • D. Papanastassiou

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.

Suggested Citation

  • T. Kalantzis & D. Papanastassiou, 2008. "Classification of GARCH time series: an empirical investigation," Applied Financial Economics, Taylor & Francis Journals, vol. 18(9), pages 759-764.
    • Handle: RePEc:taf:apfiec:v:18:y:2008:i:9:p:759-764
    DOI: 10.1080/09603100701320160
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    References listed on IDEAS

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    1. David McMillan & Alan Speight, 2003. "Asymmetric volatility dynamics in high frequency FTSE-100 stock index futures," Applied Financial Economics, Taylor & Francis Journals, vol. 13(8), pages 599-607.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Soosung Hwang & Steve Satchell, 2005. "GARCH model with cross-sectional volatility: GARCHX models," Applied Financial Economics, Taylor & Francis Journals, vol. 15(3), pages 203-216.
    4. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. F. Lisi & E. Otranto, 2008. "Clustering Mutual Funds by Return and Risk Levels," Working Paper CRENoS 200813, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.

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