A new blocks estimator for the extremal index

The occurrence of successive extreme observations can have an impact on society. In extreme value theory there are parameters to evaluate the effect of clustering of high values, such as the extremal index. The estimation of the extremal index is a recurrent theme in the literature and there are several methodologies for this purpose. The majority of existing methods depend on two parameters whose choice affects the performance of the estimators. Here we consider a new estimator depending only on one of the parameters, thus contributing to a decrease in the degree of uncertainty. A simulation study presents motivating results. An application to financial data will also be presented.

Automated summary

The occurrence of successive extreme observations can impact society. Extremal index, a parameter in extreme value theory, is used to evaluate clustering effects of high values. Existing methods for estimating the extremal index depend on two parameters, but we propose a new estimator depending only on one parameter, reducing uncertainty. Simulation results and an application to financial data demonstrate the effectiveness of our approach.