$L^{α-1}$ distance between two one-dimensional stochastic differential equations with drift terms driven by a symmetric $α$-stable process

Fuente: arXiv
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Autor principal: Nakagawa, Takuya
Formato: Preprint
Publicado: 2025
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author Nakagawa, Takuya
author_facet Nakagawa, Takuya
contents This paper establishes a quantitative stability theory for one-dimensional stochastic differential equations (SDEs) with non-zero drift, driven by a symmetric $α$-stable process for $α\in(1,2)$. Our work generalizes the classical pathwise comparison method, pioneered by Komatsu for uniqueness problems, to address the stability of SDEs featuring both non-zero drift and, crucially, time-dependent coefficients. We provide the first explicit convergence rates for this broad class of SDEs. The main result is a Hölder-type estimate for the $L^{α-1}(Ω)$ distance between two solution paths, which quantifies the stability with respect to the initial values and coefficients. A key innovation of our approach is the measurement of the distance between coefficients. Instead of using a standard supremum norm, which would impose restrictive conditions, we introduce a weighted integral norm constructed from the transition probability density of the baseline solution. This technique, which generalizes the framework of Nakagawa \cite{Nakagawa}, is essential for handling time-dependent perturbations and effectively localizes the error analysis. The proof is based on a refined analysis of a mollified auxiliary function, for which we establish a new, sharper derivative estimate to control the drift terms. Finally, we apply these stability results to derive corresponding convergence rates in probability, providing an upper bound for the tail probability of the uniform distance between solution paths.
format Preprint
id arxiv_https___arxiv_org_abs_2510_25151
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle $L^{α-1}$ distance between two one-dimensional stochastic differential equations with drift terms driven by a symmetric $α$-stable process
Nakagawa, Takuya
Probability
60H10, 60G52, 60H07, 60H50
This paper establishes a quantitative stability theory for one-dimensional stochastic differential equations (SDEs) with non-zero drift, driven by a symmetric $α$-stable process for $α\in(1,2)$. Our work generalizes the classical pathwise comparison method, pioneered by Komatsu for uniqueness problems, to address the stability of SDEs featuring both non-zero drift and, crucially, time-dependent coefficients. We provide the first explicit convergence rates for this broad class of SDEs. The main result is a Hölder-type estimate for the $L^{α-1}(Ω)$ distance between two solution paths, which quantifies the stability with respect to the initial values and coefficients. A key innovation of our approach is the measurement of the distance between coefficients. Instead of using a standard supremum norm, which would impose restrictive conditions, we introduce a weighted integral norm constructed from the transition probability density of the baseline solution. This technique, which generalizes the framework of Nakagawa \cite{Nakagawa}, is essential for handling time-dependent perturbations and effectively localizes the error analysis. The proof is based on a refined analysis of a mollified auxiliary function, for which we establish a new, sharper derivative estimate to control the drift terms. Finally, we apply these stability results to derive corresponding convergence rates in probability, providing an upper bound for the tail probability of the uniform distance between solution paths.
title $L^{α-1}$ distance between two one-dimensional stochastic differential equations with drift terms driven by a symmetric $α$-stable process
topic Probability
60H10, 60G52, 60H07, 60H50
url https://arxiv.org/abs/2510.25151