Cluster indices describe extremal behaviour of stationary time series. We consider their sliding blocks estimators. Using a modern theory of multivariate, regularly varying time series, we obtain central limit theorems under conditions that can be easily verified for a large class of models. In particular, we show that in the Peaks-Over-Threshold framework, sliding and disjoint blocks estimators have the same limiting variance.
Youssouph Cissokho. Rafal Kulik. "Estimation of cluster functionals for regularly varying time series: sliding blocks estimators." Electron. J. Statist. 15 (1) 2777 - 2831, 2021. https://doi.org/10.1214/21-EJS1843