The tail empirical process of regularly varying functions of geometrically ergodic Markov chains
2015
We consider a stationary regularly varying time series which can be expressed as a function of a geometrically ergodic Markov chain. We obtain practical conditions for the weak convergence of weighted versions of the multivariate tail empirical process. These conditions include the so-called geometric drift or Foster-Lyapunov condition and can be easily checked for most usual time series models with a Markovian structure. We illustrate these conditions on several models and statistical applications.
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