2007/01/15 by Johan Segers, Segers, Johan
Economics, Econometrics and Finance · Mathematics · #60H25 #60J05 #62P05 #FOS: Mathematics #Financial Risk and Volatility Modeling #Primary 60G70 #Probability (math.PR) #Statistical Distribution Estimation and Applications #Statistics Theory (math.ST) #Stochastic processes and statistical mechanics #secondary 60G10
paper · pdf · doi:10.48550/arxiv.math/0701411
openalex publication_date 2007/01/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The upper extremes of a Markov chain with regulary varying stationary marginal distribution are known to exhibit under general conditions a multiplicative random walk structure called the tail chain. More generally, if the Markov chain is allowed to switch from positive to negative extremes or vice versa, the distribution of the tail chain increment may depend on the sign of the tail chain on the previous step. But even then, the forward and backward tail chain mutually determine each other through a kind of adjoint relation. As a consequence, the finite-dimensional distributions of the Markov chain are multivariate regularly varying in a way determined by the back-and-forth tail chain. An application of the theory yields the asymptotic distribution of the past and the future of the solution to a stochastic difference equation conditionally on the present value being large in absolute value.