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December 2014 Markov tail chains
A. Janssen, J. Segers
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J. Appl. Probab. 51(4): 1133-1153 (December 2014).

Abstract

The extremes of a univariate Markov chain with regularly varying stationary marginal distribution and asymptotically linear behavior are known to exhibit a multiplicative random walk structure called the tail chain. In this paper we extend this fact to Markov chains with multivariate regularly varying marginal distributions in Rd. We analyze both the forward and the backward tail process and show that they mutually determine each other through a kind of adjoint relation. In a broader setting, we will show that even for non-Markovian underlying processes a Markovian forward tail chain always implies that the backward tail chain is also Markovian. We analyze the resulting class of limiting processes in detail. Applications of the theory yield the asymptotic distribution of both the past and the future of univariate and multivariate stochastic difference equations conditioned on an extreme event.

Citation

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A. Janssen. J. Segers. "Markov tail chains." J. Appl. Probab. 51 (4) 1133 - 1153, December 2014.

Information

Published: December 2014
First available in Project Euclid: 20 January 2015

zbMATH: 1308.60067
MathSciNet: MR3301293

Subjects:
Primary: 60G70, 60J05
Secondary: 60G10, 60H25, 62P05

Rights: Copyright © 2014 Applied Probability Trust

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Vol.51 • No. 4 • December 2014
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