Table of Contents
Fetching ...

On optimal error rates for strong approximation of SDEs with a Hölder continuous drift coefficient

Simon Ellinger, Thomas Müller-Gronbach, Larisa Yaroslavtseva

TL;DR

This paper addresses the problem of strong approximation for scalar SDEs with bounded, Hölder-continuous drift and constant diffusion, focusing on the final-time error when using finite evaluations of the driving Brownian motion. By transforming the SDE to a zero-drift form via a bi-Lipschitz mapping and employing a Milstein-type scheme, the authors derive lower bounds that match the known Euler scheme rate of $(1+\alpha)/2$ for $\alpha\in(0,1)$, up to logarithmic factors. They extend sharp lower bounds to drift coefficients with fractional Sobolev regularity, showing that the $(1+\alpha)/2$ rate cannot be improved by any method based on finitely many Brownian evaluations, up to a polylog term. The results rely on carefully constructed Weierstrass-type drift functions and a noise-coupling technique, providing a deeper understanding of the complexity of strong SDE approximation and the near-optimality of the Euler scheme in this setting.

Abstract

In the present article we study strong approximation of solutions of scalar stochastic differential equations (SDEs) with bounded and $α$-Hölder continuous drift coefficient and constant diffusion coefficient at time point $1$. Recently, it was shown in [arXiv:1909.07961v4 (2021)] that for such SDEs the equidistant Euler scheme achieves an $L^p$-error rate of at least $(1+α)/2$, up to an arbitrary small $\varepsilon$, for all $p\geq 1$ and all $α\in(0, 1]$ in terms of the number of evaluations of the driving Brownian motion $W$. In this article we prove a matching lower error bound for $α\in(0, 1)$. More precisely, we show that for every $α\in(0, 1)$, the $L^p$-error rate $(1+α)/2$ of the Euler scheme in [arXiv:1909.07961v4 (2021)] can not be improved in general by no numerical method based on finitely many evaluations of $W$ at fixed time points. Up to now, this result was known in the literature only for $α=1$. Additionally, we extend a result from [arXiv:2402.13732v2 (2024)] on sharp lower errror bounds for strong approximation of SDEs with a bounded drift coefficient of fractional Sobolev regularity $α\in (0,1)$ and constant diffusion coefficient at time point $1$. We prove that for every $α\in (0,1)$, the $L^p$-error rate $ (1 + α)/2$ that was shown in [arXiv:2101.12185v2 (2022)] for the equidistant Euler scheme can, up to a logarithmic term, not be improved in general by no numerical method based on finitely many evaluations of W at fixed time points. This result was known from [arXiv:2402.13732v2 (2024)] only for $α\in (1/2,1)$ and $p=2$. For the proof of these lower bounds we use variants of the Weierstrass function as a drift coefficient and we employ the coupling of noise technique recently introduced in [arXiv:2010.00915v1 (2020)].

On optimal error rates for strong approximation of SDEs with a Hölder continuous drift coefficient

TL;DR

This paper addresses the problem of strong approximation for scalar SDEs with bounded, Hölder-continuous drift and constant diffusion, focusing on the final-time error when using finite evaluations of the driving Brownian motion. By transforming the SDE to a zero-drift form via a bi-Lipschitz mapping and employing a Milstein-type scheme, the authors derive lower bounds that match the known Euler scheme rate of for , up to logarithmic factors. They extend sharp lower bounds to drift coefficients with fractional Sobolev regularity, showing that the rate cannot be improved by any method based on finitely many Brownian evaluations, up to a polylog term. The results rely on carefully constructed Weierstrass-type drift functions and a noise-coupling technique, providing a deeper understanding of the complexity of strong SDE approximation and the near-optimality of the Euler scheme in this setting.

Abstract

In the present article we study strong approximation of solutions of scalar stochastic differential equations (SDEs) with bounded and -Hölder continuous drift coefficient and constant diffusion coefficient at time point . Recently, it was shown in [arXiv:1909.07961v4 (2021)] that for such SDEs the equidistant Euler scheme achieves an -error rate of at least , up to an arbitrary small , for all and all in terms of the number of evaluations of the driving Brownian motion . In this article we prove a matching lower error bound for . More precisely, we show that for every , the -error rate of the Euler scheme in [arXiv:1909.07961v4 (2021)] can not be improved in general by no numerical method based on finitely many evaluations of at fixed time points. Up to now, this result was known in the literature only for . Additionally, we extend a result from [arXiv:2402.13732v2 (2024)] on sharp lower errror bounds for strong approximation of SDEs with a bounded drift coefficient of fractional Sobolev regularity and constant diffusion coefficient at time point . We prove that for every , the -error rate that was shown in [arXiv:2101.12185v2 (2022)] for the equidistant Euler scheme can, up to a logarithmic term, not be improved in general by no numerical method based on finitely many evaluations of W at fixed time points. This result was known from [arXiv:2402.13732v2 (2024)] only for and . For the proof of these lower bounds we use variants of the Weierstrass function as a drift coefficient and we employ the coupling of noise technique recently introduced in [arXiv:2010.00915v1 (2020)].
Paper Structure (6 sections, 15 theorems, 131 equations)

This paper contains 6 sections, 15 theorems, 131 equations.

Key Result

Theorem 1

For every $\alpha \in (0,1)$ there exist $c\in (0,\infty)$ and a bounded $\mu\in C^\alpha({\mathbb R})$ such that for all $n\in{\mathbb N}$,

Theorems & Definitions (30)

  • Theorem 1
  • Theorem 2
  • Lemma 1
  • proof
  • Lemma 2
  • proof
  • Proposition 1
  • proof
  • Lemma 3
  • proof
  • ...and 20 more