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A convergence theorem for the continuous weak approximation of the solution of stochastic differential equations by general one step methods is proved, which is an extension of a theorem due to Milstein. As an application, uniform second…

Numerical Analysis · Mathematics 2013-03-19 Kristian Debrabant , Andreas Rößler

We introduce an explicit adaptive Milstein method for stochastic differential equations (SDEs) with no commutativity condition. The drift and diffusion are separately locally Lipschitz and together satisfy a monotone condition. This method…

Numerical Analysis · Mathematics 2022-11-22 Cónall Kelly , Gabriel Lord , Fandi Sun

The truncated Euler-Maruyama (EM) method is proposed to approximate a class of non-autonomous stochastic differential equations (SDEs) with the H\"older continuity in the temporal variable and the super-linear growth in the state variable.…

Numerical Analysis · Mathematics 2019-07-19 Wei Liu , Xuerong Mao , Jingwen Tang , Yue Wu

In this paper, the truncated Euler-Maruyama (EM) method is employed together with the Multi-level Monte Carlo (MLMC) method to approximate the expectations of functions of solutions to stochastic differential equations (SDEs). The…

Numerical Analysis · Mathematics 2017-02-22 Qian Guo , Wei Liu , Xuerong Mao , Weijun Zhan

This paper focuses on explicit approximations for nonlinear stochastic delay differential equations (SDDEs). Under the weakly local Lipschitz and some suitable conditions, a generic truncated Euler-Maruyama (TEM) scheme for SDDEs is…

Numerical Analysis · Mathematics 2020-08-20 Guoting Song , Junhao Hu , Shuaibin Gao , Xiaoyue Li

In this article we compare the mean-square stability properties of the Theta-Maruyama and Theta-Milstein method that are used to solve stochastic differential equations. For the linear stability analysis, we propose an extension of the…

Numerical Analysis · Mathematics 2010-04-22 Evelyn Buckwar , Thorsten Sickenberger

We combine the rough path theory and stochastic backward error analysis to develop a new framework for error analysis on numerical schemes. Based on our approach, we prove that the almost sure convergence rate of the modified Milstein…

Numerical Analysis · Mathematics 2021-03-23 Chuying Huang

The numerical solutions of stochastic differential delay equations (SDDEs) under the generalized Khasminskii-type condition were discussed by Mao [15], and the theory there showed that the Euler-Maruyama (EM) numerical solutions converge to…

Numerical Analysis · Mathematics 2019-07-16 Qian Guo , Xuerong Mao , Rongxian Yue

The semi-implicit Euler-Maruyama (EM) method is investigated to approximate a class of time-changed stochastic differential equations, whose drift coefficient can grow super-linearly and diffusion coefficient obeys the global Lipschitz…

Numerical Analysis · Mathematics 2019-07-29 Chang-Song Deng , Wei Liu

In this paper, we use the truncated EM method to study the finite time strong convergence for the SDEs with Poisson jumps under the Khasminskii-type condition. We establish the finite time $ \mathcal L ^r (r \ge 2) $ convergence rate when…

Numerical Analysis · Mathematics 2018-05-30 Shounian Deng , Weiyin Fei , Wei Liu , Xuerong Mao

An explicit first-order drift-randomized Milstein scheme for a regime switching stochastic differential equation is proposed and its bi-stability and rate of strong convergence are investigated for a non-differentiable drift coefficient.…

Probability · Mathematics 2025-03-11 Divyanshu Vashistha , Chaman Kumar

To construct positivity-preserving numerical methods, a vast majority of existing works employ transformation techniques such as the Lamperti transformation or logarithmic transformation. However, using these techniques often leads to the…

Numerical Analysis · Mathematics 2025-08-26 Xingwei Hu , Xinjie Dai , Aiguo Xiao

We propose a tamed-adaptive Milstein scheme for stochastic differential equations in which the first-order derivatives of the coefficients are locally H\"older continuous of order $\alpha$. We show that the scheme converges in the…

Probability · Mathematics 2025-07-10 Thi-Huong Vu , Hoang-Long Ngo , Duc-Trong Luong , Tran Ngoc Khue

In order to approximate solutions of stochastic partial differential equations (SPDEs) that do not possess commutative noise, one has to simulate the involved iterated stochastic integrals. Recently, two approximation methods for iterated…

Probability · Mathematics 2019-10-09 Claudine von Hallern , Andreas Rößler

This manuscript is dedicated to the numerical approximation of super-linear slow-fast stochastic differential equations (SFSDEs). Borrowing the heterogeneous multiscale idea, we propose an explicit multiscale Euler-Maruyama scheme suitable…

Numerical Analysis · Mathematics 2025-03-18 Yuanping Cui , Xiaoyue Li , Xuerong Mao

This paper focuses on the numerical scheme for multiple-delay stochastic differential equations with partially H\"older continuous drifts and locally H\"older continuous diffusion coefficients. To handle with the superlinear terms in…

Numerical Analysis · Mathematics 2024-03-19 Zhuoqi Liu , Zhaohang Wang , Siying Sun , Shuaibin Gao

We propose and analyse a new Milstein type scheme for simulating stochastic differential equations (SDEs) with highly nonlinear coefficients. Our work is motivated by the need to justify multi-level Monte Carlo simulations for…

Numerical Analysis · Mathematics 2012-04-10 Desmond J. Higham , Xuerong Mao , Lukasz Szpruch

The Milstein approximation with step size $\Delta t>0$ of the solution $(X, Y)$ to a two-by-two system of linear stochastic differential equations is considered. It is proved that when the solution of the underlying model is exponentially…

Probability · Mathematics 2025-04-18 Vu Thi Hue

This paper is concerned with the numerical approximation of stochastic mechanical systems with nonlinear holonomic constraints. Such systems are described by second order stochastic differential-algebraic equations involving an implicitly…

Probability · Mathematics 2017-09-26 Felix Lindner , Holger Stroot

Let $(X_t)_{t \ge 0}$ be the solution of the stochastic differential equation $$dX_t = b(X_t) dt+A dZ_t, \quad X_{0}=x,$$ where $b: \mathbb{R}^d \rightarrow \mathbb R^d$ is a Lipschitz function, $A \in \mathbb R^{d \times d}$ is a positive…

Probability · Mathematics 2023-10-10 Peng Chen , Xinghu Jin , Yimin Xiao , Lihu Xu