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Clusters of Extreme Observations and Extremal Index Estimate in GARCH Processes

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  • Laurini Fabrizio

    () (University of Parma)

Abstract

Several methods have been proposed for identifying clusters of extreme values leading to estimators of the extremal index; the latter represents,in the limit, the mean-size of each cluster of thresholds exceedances. The detection of clusters of extremes is relevant for the class of processes commonly used in financial econometrics, such as GARCH processes. The paper illustrates a novel approach to the above identification that exploits additional knowledge of the trajectory of the process around extreme events, and compares it to traditional approaches, using simulation from a GARCH process. We assess the relative performance of estimators in terms of bias, mean square error and distributional properties.

Suggested Citation

  • Laurini Fabrizio, 2004. "Clusters of Extreme Observations and Extremal Index Estimate in GARCH Processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(2), pages 1-23, May.
  • Handle: RePEc:bpj:sndecm:v:8:y:2004:i:2:n:4
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    Cited by:

    1. John G. Galbraith & Serguei Zernov, 2006. "Extreme Dependence In The Nasdaq And S&P Composite Indexes," Departmental Working Papers 2006-14, McGill University, Department of Economics.

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