Almost sure diffusion approximation in averaging via rough paths theory

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: Friz, Peter, Kifer, Yuri
Format: Preprint
Published: 2021
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866929339693531136
author Friz, Peter
Kifer, Yuri
author_facet Friz, Peter
Kifer, Yuri
contents The paper deals with the fast-slow motions setups in the continuous time $\frac {dX^\ve(t)}{dt}=\frac 1\ve\sig(X^\ve(t))ξ(t/\ve^2)+b(X^\ve(t)),\, t\in [0,T]$ and the discrete time $X_N((n+1)/N)=X_N(n/N)+N^{-1/2}\sig(X_N(n/N))ξ(n))+N^{-1}b(X_N(n/N))ξ(n)$, $n=0,1,...,[TN]$ where $\sig$ and $b$ are smooth matrix and vector functions, respectively, $ξ$ is a centered stationary vector stochastic process and $\ve, 1/N$ are small parameters. We derive, first, estimates in the strong invariance principles for sums $S_{N}(t)=N^{-1/2}\sum_{0\leq k< [Nt]}ξ(k)$ and iterated sums $\bbS^{ij}_{N}(t)=N^{-1}\sum_{0\leq k<l<[Nt]}ξ_i(k)ξ_j(l)$ together with the corresponding results for integrals in the continuous time case which, in fact, yields almost sure invariance principles for iterated sums and integrals of any order and, moreover, implies laws of iterated logarithm for these objects. Then, relying on the rough paths theory, we obtain strong almost sure approximations of processes $X^\ve$ and $X_N$ by corresponding diffusion processes $Ξ^\ve$ and $Ξ_N$, respectively. Previous results for the above setup dealt either with weak or moment diffusion approximations and not with almost sure approximation which is the new and natural generalization of well known works on strong invariance principles for sums of weakly dependent random variables.
format Preprint
id arxiv_https___arxiv_org_abs_2111_05390
institution arXiv
publishDate 2021
record_format arxiv
spellingShingle Almost sure diffusion approximation in averaging via rough paths theory
Friz, Peter
Kifer, Yuri
Probability
Dynamical Systems
34C29, 60F15, 60L20, 60G40, 91A05
The paper deals with the fast-slow motions setups in the continuous time $\frac {dX^\ve(t)}{dt}=\frac 1\ve\sig(X^\ve(t))ξ(t/\ve^2)+b(X^\ve(t)),\, t\in [0,T]$ and the discrete time $X_N((n+1)/N)=X_N(n/N)+N^{-1/2}\sig(X_N(n/N))ξ(n))+N^{-1}b(X_N(n/N))ξ(n)$, $n=0,1,...,[TN]$ where $\sig$ and $b$ are smooth matrix and vector functions, respectively, $ξ$ is a centered stationary vector stochastic process and $\ve, 1/N$ are small parameters. We derive, first, estimates in the strong invariance principles for sums $S_{N}(t)=N^{-1/2}\sum_{0\leq k< [Nt]}ξ(k)$ and iterated sums $\bbS^{ij}_{N}(t)=N^{-1}\sum_{0\leq k<l<[Nt]}ξ_i(k)ξ_j(l)$ together with the corresponding results for integrals in the continuous time case which, in fact, yields almost sure invariance principles for iterated sums and integrals of any order and, moreover, implies laws of iterated logarithm for these objects. Then, relying on the rough paths theory, we obtain strong almost sure approximations of processes $X^\ve$ and $X_N$ by corresponding diffusion processes $Ξ^\ve$ and $Ξ_N$, respectively. Previous results for the above setup dealt either with weak or moment diffusion approximations and not with almost sure approximation which is the new and natural generalization of well known works on strong invariance principles for sums of weakly dependent random variables.
title Almost sure diffusion approximation in averaging via rough paths theory
topic Probability
Dynamical Systems
34C29, 60F15, 60L20, 60G40, 91A05
url https://arxiv.org/abs/2111.05390