Open Access
2023 Long memory of max-stable time series as phase transition: asymptotic behaviour of tail dependence estimators
Marco Oesting, Albert Rapp
Author Affiliations +
Electron. J. Statist. 17(2): 3316-3336 (2023). DOI: 10.1214/23-EJS2181

Abstract

In this paper, we consider a simple estimator for tail dependence coefficients of a max-stable time series and show its asymptotic normality under a mild condition. The novelty of our result is that this condition does not involve mixing properties that are common in the literature. More importantly, our condition is linked to the transition between long and short range dependence (LRD/SRD) for max-stable time series. This is based on a recently proposed notion of LRD in the sense of indicators of excursion sets which is meaningfully defined for infinite-variance time series. In particular, we show that asymptotic normality with standard rate of convergence and a function of the sum of tail coefficients as asymptotic variance holds if and only if the max-stable time series is SRD.

Citation

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Marco Oesting. Albert Rapp. "Long memory of max-stable time series as phase transition: asymptotic behaviour of tail dependence estimators." Electron. J. Statist. 17 (2) 3316 - 3336, 2023. https://doi.org/10.1214/23-EJS2181

Information

Received: 1 May 2023; Published: 2023
First available in Project Euclid: 21 November 2023

Digital Object Identifier: 10.1214/23-EJS2181

Subjects:
Primary: 60F05 , 60G70 , 62M10

Keywords: Extreme value statistics , limit theorems , Long range dependence

Vol.17 • No. 2 • 2023
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