Add to ORKGWe derive explicit tail-estimates for the Jacobian of the solution flow for stochastic differential equations driven by Gaussian rough paths. In particular, we deduce that the Jacobian has finite moments of all order for a wide class of Gaussian process including fractional Brownian motion with Hurst parameter H>1/4. We remark on the relevance of such estimates to a number of significant open problems
Motivated by a problematic coming from mathematical finance, the paper deals with existing and addit...
This dissertation contains three research directions. In the first direction, we use rough paths the...
This thesis consists of two quite distinct topics. In the first and bigger part we show that the Man...
We derive explicit tail-estimates for the Jacobian of the solution flow for stochastic differential ...
We consider stochastic differential equations of the form dYt=V(Yt)dXt+V0(Yt)dt driven by a multi-di...
Gess B, Ouyang C, Tindel S. Density Bounds for Solutions to Differential Equations Driven by Gaussia...
We consider the rough differential equation with drift driven by a Gaussian geometric rough path. Un...
We show that the tail probability of the rough line integral \int_{0}^{1}\phi(X_{t})dY_{t}, where (X...
In this paper we show, by using dyadic approximations, the existence of a geometric rough path assoc...
AbstractWe consider controlled ordinary differential equations and give new estimates for higher ord...
We give meaning to differential equations with a rough path term and a Brownian noise term and study...
We study semilinear rough stochastic partial differential equations as introduced in [Gerasimovi{\v{...
Using covariance identities based on the Clark-Ocone representation formula we derive Gaussian densi...
Abstract. Discrete approximations to solutions of stochastic differential equations are well-known t...
Within the rough path framework, we prove the continuity of the solution to random differential equa...
Motivated by a problematic coming from mathematical finance, the paper deals with existing and addit...
This dissertation contains three research directions. In the first direction, we use rough paths the...
This thesis consists of two quite distinct topics. In the first and bigger part we show that the Man...
We derive explicit tail-estimates for the Jacobian of the solution flow for stochastic differential ...
We consider stochastic differential equations of the form dYt=V(Yt)dXt+V0(Yt)dt driven by a multi-di...
Gess B, Ouyang C, Tindel S. Density Bounds for Solutions to Differential Equations Driven by Gaussia...
We consider the rough differential equation with drift driven by a Gaussian geometric rough path. Un...
We show that the tail probability of the rough line integral \int_{0}^{1}\phi(X_{t})dY_{t}, where (X...
In this paper we show, by using dyadic approximations, the existence of a geometric rough path assoc...
AbstractWe consider controlled ordinary differential equations and give new estimates for higher ord...
We give meaning to differential equations with a rough path term and a Brownian noise term and study...
We study semilinear rough stochastic partial differential equations as introduced in [Gerasimovi{\v{...
Using covariance identities based on the Clark-Ocone representation formula we derive Gaussian densi...
Abstract. Discrete approximations to solutions of stochastic differential equations are well-known t...
Within the rough path framework, we prove the continuity of the solution to random differential equa...
Motivated by a problematic coming from mathematical finance, the paper deals with existing and addit...
This dissertation contains three research directions. In the first direction, we use rough paths the...
This thesis consists of two quite distinct topics. In the first and bigger part we show that the Man...