Strong convergence rate of Euler-Maruyama method for stochastic differential equations with Hölder continuous drift coefficient driven by symmetric $α$-stable process

Fuente: arXiv
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Auteur principal: Liu, Wei
Format: Preprint
Publié: 2019
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_version_ 1866929641159131136
author Liu, Wei
author_facet Liu, Wei
contents Euler-Maruyama method is studied to approximate stochastic differential equations driven by the symmetric $α$-stable additive noise with the $β$ Hölder continuous drift coefficient. When $α\in (1,2)$ and $β\in (0,α/2)$, for $p \in (0,2]$ the $L^p$ strong convergence rate is proved to be $pβ/α$. The proofs in this paper are extensively based on Hölder's and Bihari's inequalities, which is significantly different from those in Huang and Liao (2018).
format Preprint
id arxiv_https___arxiv_org_abs_1901_08742
institution arXiv
publishDate 2019
record_format arxiv
spellingShingle Strong convergence rate of Euler-Maruyama method for stochastic differential equations with Hölder continuous drift coefficient driven by symmetric $α$-stable process
Liu, Wei
Numerical Analysis
Probability
65C30
Euler-Maruyama method is studied to approximate stochastic differential equations driven by the symmetric $α$-stable additive noise with the $β$ Hölder continuous drift coefficient. When $α\in (1,2)$ and $β\in (0,α/2)$, for $p \in (0,2]$ the $L^p$ strong convergence rate is proved to be $pβ/α$. The proofs in this paper are extensively based on Hölder's and Bihari's inequalities, which is significantly different from those in Huang and Liao (2018).
title Strong convergence rate of Euler-Maruyama method for stochastic differential equations with Hölder continuous drift coefficient driven by symmetric $α$-stable process
topic Numerical Analysis
Probability
65C30
url https://arxiv.org/abs/1901.08742