Markov tail chains

Preprint, Research, Other literature type English OPEN
Janßen, Anja ; Segers, Johan (2013)
  • Publisher: Applied Probability Trust
  • Journal: (issn: 0021-9002)
  • Subject: tail chain | tail-switching potential | 60H25 | 60J05 | 62P05 | 60G70, 60J05 (primary) 60G10, 60H25, 62P05 (secondary) | multivariate regular variation | extreme value distribution | random walk | Autoregressive conditional heteroskedasticity | 60G70 | stochastic difference equation | Mathematics - Probability | (multivariate) Markov chain | 60G10

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 R<sup>d</sup>. 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.
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