Limit theorems for Markov walks conditioned to stay positive in the $\alpha$-stable regime under a spectral gap assumption

TL;DR

This paper establishes limit theorems for Markov walks conditioned to stay positive in the $ ext{alpha}$-stable regime, extending Gaussian results to stable laws and introducing stable meanders for Markov additive processes.

Contribution

It extends spectral-gap theory to the $ ext{alpha}$-stable regime and introduces stable meanders for Markov additive processes under these conditions.

Findings

Asymptotic behavior of $P_x( au_y>n)$ characterized by a positive harmonic function.

Conditional limit theorem showing convergence to the $ ext{alpha}$-stable meander.

Growth rate of the harmonic function $V_ ext{alpha}(x,y)$ as $y oty$.

Abstract

Let $(X_n)_{n\ge 1}$ be a Markov chain on a measurable state space $X$, and let $S_n = \sum_{k=1}^n f(X_k)$ be the associated Markov walk. For $y>0$, denote by $\tau_y$ the first time at which $y+S_n$ becomes non-positive. Assuming that the centred martingale approximation of $S_n$ lies in the domain of attraction of a strictly $\alpha$-stable law with $\alpha\in(1,2)$, and that the transition operator satisfies a spectral-gap condition, we determine the asymptotic behaviour of $P_x(\tau_y>n)$. In particular, we show the existence of a strictly positive $Q^+$-harmonic function $V_\alpha(x,y)$ such that $$n^{1-\rho} L(n)\, P_x(\tau_y>n) \longrightarrow V_\alpha(x,y),$$ where $L$ is slowly varying and $\rho$ is the positivity parameter of the limiting $\alpha$-stable process. We further establish the asymptotic growth of $V_\alpha(x,y)$ as $y\to\infty$ and prove a conditional limit theorem: conditionally on $\{\tau_y>n\}$, $$\frac{S_n}{n^{1/\alpha} L(n)}$$ converges in distribution to the $\alpha$-stable meander. These results extend the Gaussian spectral-gap theory of Markov walks to the full stable regime and give the first appearance of stable meanders for Markov additive processes under such assumptions.