paper

Stability of perpetuities in Markovian environment

arXiv:1610.09965 · doi:10.1080/10236198.2016.1271878

Abstract

The stability of iterations of affine linear maps , , is studied in the presence of a Markovian environment, more precisely, for the situation when is modulated by an ergodic Markov chain with countable state space and stationary distribution . We provide necessary and sufficient conditions for the a.s. and the distributional convergence of the backward iterations and also describe all possible limit laws as solutions to a certain Markovian stochastic fixed-point equation. As a consequence of the random environment, these limit laws are stochastic kernels from to rather than distributions on , thus reflecting their dependence on where the driving chain is started. We give also necessary and sufficient conditions for the distributional convergence of the forward iterations . The main differences caused by the Markovian environment as opposed to the extensively studied case of independent and identically distributed (iid) are that: (1) backward iterations may still converge in distribution, if a.s. convergence fails, (2) the degenerate case when a.s. for suitable constants , , is by far more complex than the degenerate case for iid when a.s. for some , and (3) forward and backward iterations generally have different laws given for so that the former ones need a separate analysis. Our proofs draw on related results for the iid-case, notably by Vervaat, Grincevičius, and Goldie and Maller, in combination with recent results by the authors on fluctuation theory for Markov random walks.

36 pages

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