Statistical analysis for stationary time series at extreme levels: New estimators for the limiting cluster size distribution

Axel Bücher and Tobias Jennessen

Stochastic Processes and their Applications, 2022, vol. 149, issue C, 75-106

Abstract: The serial dependence of a stationary time series at extreme levels may be captured by the limiting cluster size distribution. New estimators based on a blocks declustering scheme are proposed and analyzed both theoretically and by means of a large-scale simulation study. A sliding blocks version of the estimators is shown to outperform a disjoint blocks version. In contrast to some competitors from the literature, the estimators only depend on one tuning parameter to be chosen by the statistician.

Keywords: Asymptotic theory; Block maxima; Clusters of extremes; Mixing coefficients (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.spa.2022.03.004

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