Persistence problems for additive functionals of one-dimensional Markov processes

TL;DR

This paper investigates the long-term persistence probabilities of additive functionals of one-dimensional Markov processes, extending existing results to processes and functionals that are only asymptotically self-similar or homogeneous.

Contribution

It introduces a new approach using excursion and Wiener–Hopf decompositions to analyze persistence probabilities for asymptotically self-similar Markov processes and functionals.

Findings

Persistence probabilities decay as a power law with explicit asymptotic form

Derived the exponent  as a product of local time scaling and positivity parameters

Extended classical results to non-homogeneous, asymptotically self-similar processes

Abstract

In this article, we consider additive functionals $\zeta_t = \int_0^t f(X_s)\mathrm{d} s$ of a c\`adl\`ag Markov process $(X_t)_{t\geq 0}$ on $\mathbb{R}$. Under some general conditions on the process $(X_t)_{t\geq 0}$ and on the function $f$, we show that the persistence probabilities verify $\mathbb{P}(\zeta_s < z \text{ for all } s\leq t ) \sim \mathcal{V}(z) \varsigma(t) t^{-\theta}$ as $t\to\infty$, for some (explicit) $\mathcal{V}(\cdot)$, some slowly varying function $\varsigma(\cdot)$ and some $\theta\in (0,1)$. This extends results in the literature, which mostly focused on the case of a self-similar process $(X_t)_{t\geq 0}$ (such as Brownian motion or skew-Bessel process) with a homogeneous functional $f$ (namely a pure power, possibly asymmetric). In a nutshell, we are able to deal with processes which are only asymptotically self-similar and functionals which are only asymptotically homogeneous. Our results rely on an excursion decomposition of $(X_t)_{t\geq 0}$, together with a Wiener--Hopf decomposition of an auxiliary (bivariate) L\'evy process, with a probabilistic point of view. This provides an interpretation for the asymptotic behavior of the persistence probabilities, and in particular for the exponent $\theta$, which we write as $\theta = \rho \beta$, with $\beta$ the scaling exponent of the local time of $(X_{t})_{t\geq 0}$ at level $0$ and $\rho$ the (asymptotic) positivity parameter of the auxiliary L\'evy process.