# Stability of overshoots of zero mean random walks

## TL;DR

This paper proves the convergence and stability of the overshoot process of zero-mean random walks, providing explicit stationary distributions and ergodicity results under various conditions.

## Contribution

It establishes the convergence in total variation of overshoot Markov chains and derives their explicit stationary distributions, including ergodicity properties for specific increment distributions.

## Key findings

- Overshoot Markov chain converges to a stationary distribution.
- Explicit form of the stationary distribution is derived.
- Ergodicity results depend on the tail behavior of increments.

## Abstract

We prove that for a random walk on the real line whose increments have zero mean and are either integer-valued or spread out (i.e. the distributions of the steps of the walk are eventually non-singular), the Markov chain of overshoots above a fixed level converges in total variation to its stationary distribution. We find the explicit form of this distribution heuristically and then prove its invariance using a time-reversal argument. If, in addition, the increments of the walk are in the domain of attraction of a non-one-sided $\alpha$-stable law with index $\alpha\in(1,2)$ (resp. have finite variance), we establish geometric (resp. uniform) ergodicity for the Markov chain of overshoots. All the convergence results above are also valid for the Markov chain obtained by sampling the walk at the entrance times into an interval.