English

General self-similarity properties for Markov processes and exponential functionals of L{\'e}vy processes

Probability 2019-12-13 v2

Abstract

Positive self-similar Markov processes (pssMp) are positive Markov processes that satisfy the scaling property and it is known that they can be represented as the exponential of a time-changed L\'evy process via Lamperti representation. In this work, we are interested in the following problem: what happens if we consider Markov processes in dimension 11 or 22 that satisfy self-similarity properties of a more general form than a scaling property ? Can they all be represented as a function of a time-changed L\'evy process ? If not, how can Lamperti representation be generalized ? We show that, not surprisingly, a Markovian process in dimension 11 that satisfies self-similarity properties of a general form can indeed be represented as a function of a time-changed L\'evy process, which shows some kind of universality for the classical Lamperti representation in dimension 11. However, and this is our main result, we show that a Markovian process in dimension 22 that satisfies self-similarity properties of a general form is represented as a function of a time-changed exponential functional of a bivariate L\'evy process, and processes that can be represented as a function of a time-changed L\'evy process form a strict subclass. This shows that the classical Lamperti representation is not universal in dimension 22. We briefly discuss the complications that occur in higher dimensions. In dimension 22 we present an example, built from a self-similar fragmentation process, where our representation in term of an exponential functional of a bivariate L\'evy process appears naturally and has a nice interpretation in term of the self-similar fragmentation process.

Keywords

Cite

@article{arxiv.1807.01878,
  title  = {General self-similarity properties for Markov processes and exponential functionals of L{\'e}vy processes},
  author = {Grégoire Véchambre},
  journal= {arXiv preprint arXiv:1807.01878},
  year   = {2019}
}
R2 v1 2026-06-23T02:51:36.535Z