Noise Reinforced L\'evy Processes: L\'evy-It\^o Decomposition and Applications
Alejandro Rosales-Ortiz

TL;DR
This paper introduces noise reinforced Lévy processes, proves their path properties and Lévy-Itô decomposition, and explores their convergence from discrete reinforced random walks, highlighting their growth behavior and infinite divisibility.
Contribution
It establishes the Lévy-Itô decomposition for noise reinforced Lévy processes and demonstrates their convergence from discrete reinforced random walks, extending Lévy process theory.
Findings
Noise reinforced Lévy processes have right-continuous with left limits paths.
They satisfy a Lévy-Itô decomposition involving reinforced Poisson jumps.
The reinforced processes converge from discrete step reinforced random walks as mesh size decreases.
Abstract
A step reinforced random walk is a discrete time process with memory such that at each time step, with fixed probability , it repeats a previously performed step chosen uniformly at random while with complementary probability , it performs an independent step with fixed law. In the continuum, the main result of Bertoin in [7] states that the random walk constructed from the discrete-time skeleton of a L\'evy process for a time partition of mesh-size converges, as in the sense of finite dimensional distributions, to a process referred to as a noise reinforced L\'evy process. Our first main result states that a noise reinforced L\'evy processes has rcll paths and satisfies a L\'evy It\^o decomposition in terms of the Poisson point process of its jumps. We introduce the joint…
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Taxonomy
TopicsStochastic processes and statistical mechanics · Probability and Risk Models · Point processes and geometric inequalities
