Long Memory of Max-Stable Time Series as Phase Transition: Asymptotic Behaviour of Tail Dependence Estimators

TL;DR

This paper investigates the asymptotic behavior of tail dependence estimators in max-stable time series, revealing a phase transition linked to long and short-range dependence based on a novel dependence notion.

Contribution

It introduces a new condition for asymptotic normality that does not rely on mixing properties but relates to the LRD/SRD transition in max-stable processes.

Findings

Asymptotic normality holds if and only if the process is SRD.

The estimator's variance is a function of tail coefficients.

The condition connects dependence structure with tail behavior.

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.