Clustering of Markov chain exceedances

The tail chain of a Markov chain can be used to model the dependence between extreme observations. For a positive recurrent Markov chain, the tail chain aids in describing the limit of a sequence of point processes {N-n, n >= 1}, consisting of normalized observations plotted against scaled time points. Under fairly general conditions on extremal behaviour, {N-n} converges to a cluster Poisson process. Our technique decomposes the sample path of the chain into i.i.d. regenerative cycles rather than using blocking argument typically employed in the context of stationarity with mixing.