31 pagesIn his 1972 paper, John Lamperti characterized all positive self-similar Markov processes as time-changes of exponentials of Levy processes. In the past decade the problem of classifying all non-negative self-similar Markov processes that do not necessarily have zero as a trap has been solved gradually via connections to ladder height processes and excursion theory. Motivated by a recent article of Chaumont, Rivero, Panti we classify via jump-type SDEs the symmetric real-valued self-similar Markov processes that only decrease the absolute value by jumps and leave zero continuously. Our construction of these self-similar processes involves a pseudo excursion construction and singular stochastic calculus arguments ensuring that soluti...
Abstract. We study stochastic equations of non-negative processes with jumps. The existence and uniq...
We start by providing an explicit characterization and analytical properties, including the persiste...
An R d-valued Markov process X (x) t = (X 1,x 1 t ,. .. , X d,x d t), t ≥ 0, x ∈ R d is said to be m...
38 pagesWe present a new approach to positive self-similar Markov processes (pssMps) by reformulatin...
We study a class of self-similar jump type SDEs driven by Hölder-continuous drift and noise coeffici...
7 pagesIt has been shown by Bertoin and Yor (2002) that the law of positive self-similar Markov proc...
For a positive self-similar Markov process, X, we construct a local time for the random set, Θ, of t...
In this talk, we present a necessary and sufficient condition for the existence of recurrent extensi...
Abstract: This note surveys some recent results on self-similar Markov processes. Since the research...
AbstractA path decomposition at the infimum for positive self-similar Markov processes (pssMp) is ob...
In this paper we obtain a Lamperti type representation for real-valued self-similar Markov processes...
In this talk, we consider self-similar Markov processes defined on $R^d$ without the origin, which a...
We study scaling limits of non-increasing Markov chains with values in the set of non-negative inte...
A classical result, due to Lamperti, establishes a one-to-one correspondence between a class of stri...
condition, excursion Let X = (Xt)t≥0 be a self-similar Markov process with values in [0,∞[, such tha...
Abstract. We study stochastic equations of non-negative processes with jumps. The existence and uniq...
We start by providing an explicit characterization and analytical properties, including the persiste...
An R d-valued Markov process X (x) t = (X 1,x 1 t ,. .. , X d,x d t), t ≥ 0, x ∈ R d is said to be m...
38 pagesWe present a new approach to positive self-similar Markov processes (pssMps) by reformulatin...
We study a class of self-similar jump type SDEs driven by Hölder-continuous drift and noise coeffici...
7 pagesIt has been shown by Bertoin and Yor (2002) that the law of positive self-similar Markov proc...
For a positive self-similar Markov process, X, we construct a local time for the random set, Θ, of t...
In this talk, we present a necessary and sufficient condition for the existence of recurrent extensi...
Abstract: This note surveys some recent results on self-similar Markov processes. Since the research...
AbstractA path decomposition at the infimum for positive self-similar Markov processes (pssMp) is ob...
In this paper we obtain a Lamperti type representation for real-valued self-similar Markov processes...
In this talk, we consider self-similar Markov processes defined on $R^d$ without the origin, which a...
We study scaling limits of non-increasing Markov chains with values in the set of non-negative inte...
A classical result, due to Lamperti, establishes a one-to-one correspondence between a class of stri...
condition, excursion Let X = (Xt)t≥0 be a self-similar Markov process with values in [0,∞[, such tha...
Abstract. We study stochastic equations of non-negative processes with jumps. The existence and uniq...
We start by providing an explicit characterization and analytical properties, including the persiste...
An R d-valued Markov process X (x) t = (X 1,x 1 t ,. .. , X d,x d t), t ≥ 0, x ∈ R d is said to be m...