Clusters of extreme observations and extremal index estimate in GARCH processes

Several methods have been proposed for identifying clusters of ex- treme 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 sim- ulation from a GARCH process. We assess the relative performance of estimators in terms of bias, mean square error and distributional properties.