Name
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 R-d. 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. |
Year | Venue | Keywords |
|---|---|---|
2014 | JOURNAL OF APPLIED PROBABILITY | Autoregressive conditional heteroskedasticity, extreme value distribution, (multivariate) Markov chain, multivariate regular variation, random walk, stochastic difference equation, tail chain, tail-switching potential |
Field | DocType | Volume |
Markov chain mixing time,Additive Markov chain,Markov process,Random walk,Markov chain,Balance equation,Variable-order Markov model,Statistics,Mathematics,Asymptotic distribution | Journal | 51 |
Issue | ISSN | Citations |
4 | 0021-9002 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| anja janssen | 1 | 0 | 0.34 |
| Johan Segers | 2 | 41 | 10.37 |
| Anja Janßen | 3 | 0 | 0.34 |