Discrete approximations to solutions of stochastic differential equations are well-known to converge with strong rate 1/2. Such rates have played a key-role in Giles' multilevel Monte Carlo method [Giles, Oper. Res. 2008] which gives a substantial reduction of the computational effort necessary for the evaluation of diffusion functionals. In the present article similar results are established for large classes of rough differential equations driven by Gaussian processes (including fractional Brownian motion with H>1/4 as special case)
We extend Donsker's approximation of Brownian motion to fractional Brownian motion with Hurst expone...
In this dissertation, we investigate time-discrete numerical approximation schemes for rough differe...
We consider a system of differential equations in a fast long range dependent random environment and...
Discrete approximations to solutions of stochastic differential equations are well-known to converge...
Abstract. Discrete approximations to solutions of stochastic differential equations are well-known t...
In this article, we consider diffusion approximations for a general class of stochastic recursions. ...
AbstractA quantitative version of a well-known limit theorem for stochastic flows is establishing; o...
New classes of stochastic differential equations can now be studied using rough path theory (see, e....
We consider two discrete schemes for studying and approximating stochastic differential equations (...
This is the published version, also available here: http://dx.doi.org/10.1214/10-AOP578.This note is...
AbstractThis article introduces and analyzes multilevel Monte Carlo schemes for the evaluation of th...
We build a hybrid theory of rough stochastic analysis which seamlessly combines the advantages of bo...
This thesis consists of two quite distinct topics. In the first and bigger part we show that the Man...
International audienceIn this article, we study the numerical approximation of stochastic differenti...
AbstractWe consider controlled ordinary differential equations and give new estimates for higher ord...
We extend Donsker's approximation of Brownian motion to fractional Brownian motion with Hurst expone...
In this dissertation, we investigate time-discrete numerical approximation schemes for rough differe...
We consider a system of differential equations in a fast long range dependent random environment and...
Discrete approximations to solutions of stochastic differential equations are well-known to converge...
Abstract. Discrete approximations to solutions of stochastic differential equations are well-known t...
In this article, we consider diffusion approximations for a general class of stochastic recursions. ...
AbstractA quantitative version of a well-known limit theorem for stochastic flows is establishing; o...
New classes of stochastic differential equations can now be studied using rough path theory (see, e....
We consider two discrete schemes for studying and approximating stochastic differential equations (...
This is the published version, also available here: http://dx.doi.org/10.1214/10-AOP578.This note is...
AbstractThis article introduces and analyzes multilevel Monte Carlo schemes for the evaluation of th...
We build a hybrid theory of rough stochastic analysis which seamlessly combines the advantages of bo...
This thesis consists of two quite distinct topics. In the first and bigger part we show that the Man...
International audienceIn this article, we study the numerical approximation of stochastic differenti...
AbstractWe consider controlled ordinary differential equations and give new estimates for higher ord...
We extend Donsker's approximation of Brownian motion to fractional Brownian motion with Hurst expone...
In this dissertation, we investigate time-discrete numerical approximation schemes for rough differe...
We consider a system of differential equations in a fast long range dependent random environment and...