  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.
