Cluster based inference for extremes of time series

Holger Drees; Anja Janßen; Sebastian Neblung

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arXiv · 2103.08512

Cluster based inference for extremes of time series

Holger Drees·Anja Janßen·Sebastian Neblung

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Abstract

We introduce a new type of estimator for the spectral tail process of a regularly varying time series. The approach is based on a characterizing invariance property of the spectral tail process, which is incorporated into the new estimator via a projection technique. We show uniform asymptotic normality of this estimator, both in the case of known and of unknown index of regular variation. In a simulation study the new procedure shows a more stable performance than previously proposed estimators.

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Holger Drees, Anja Janßen, Sebastian Neblung. 2021-03-15. Cluster based inference for extremes of time series. https://arxiv.org/abs/2103.08512

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