scholarly journals On the averaging principle for SDEs driven by G-Brownian motion with non-Lipschitz coefficients

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wei Mao ◽  
Bo Chen ◽  
Surong You
Keyword(s):  
Brownian Motion ◽  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Averaging Principle ◽  
Mean Square ◽  
The Mean

AbstractIn this paper, we aim to develop the averaging principle for stochastic differential equations driven by G-Brownian motion (G-SDEs for short) with non-Lipschitz coefficients. By the properties of G-Brownian motion and stochastic inequality, we prove that the solution of the averaged G-SDEs converges to that of the standard one in the mean-square sense and also in capacity. Finally, two examples are presented to illustrate our theory.

Strong Convergence Analysis of Split-Step θ-Scheme for Nonlinear Stochastic Differential Equations with Jumps

2016 ◽  
Vol 8 (6) ◽  
pp. 1004-1022 ◽  
Author(s):  
Xu Yang ◽  
Weidong Zhao
Keyword(s):  
Differential Equations ◽  
Theoretical Result ◽  
Strong Convergence ◽  
Stochastic Differential Equations ◽  
Rate Of Convergence ◽  
Numerical Experiments ◽  
Mean Square ◽  
Nonlinear Stochastic Differential Equations ◽  
Mean Square Convergence ◽  
The Mean

AbstractIn this paper, we investigate the mean-square convergence of the split-step θ-scheme for nonlinear stochastic differential equations with jumps. Under some standard assumptions, we rigorously prove that the strong rate of convergence of the split-step θ-scheme in strong sense is one half. Some numerical experiments are carried out to assert our theoretical result.

scholarly journals An Averaging Principle for Mckean–Vlasov-Type Caputo Fractional Stochastic Differential Equations

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Weifeng Wang ◽  
Lei Yan ◽  
Junhao Hu ◽  
Zhongkai Guo
Keyword(s):  
Differential Equation ◽  
Brownian Motion ◽  
Differential Equations ◽  
Stochastic Differential Equation ◽  
Stochastic Differential Equations ◽  
Averaging Principle ◽  
Convergence Results ◽  
Fractional Stochastic Differential Equations

In this paper, we want to establish an averaging principle for Mckean–Vlasov-type Caputo fractional stochastic differential equations with Brownian motion. Compared with the classic averaging condition for stochastic differential equation, we propose a new averaging condition and obtain the averaging convergence results for Mckean–Vlasov-type Caputo fractional stochastic differential equations.

scholarly journals A Compensated Numerical Method for Solving Stochastic Differential Equations with Variable Delays and Random Jump Magnitudes

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Ying Du ◽  
Changlin Mei
Keyword(s):  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Numerical Method ◽  
Analytical Solutions ◽  
Numerical Solutions ◽  
Mean Square ◽  
Variable Delays ◽  
Mean Square Stability ◽  
The Mean ◽  
Random Jump

Stochastic differential equations with jumps are of a wide application area especially in mathematical finance. In general, it is hard to obtain their analytical solutions and the construction of some numerical solutions with good performance is therefore an important task in practice. In this study, a compensated split-stepθmethod is proposed to numerically solve the stochastic differential equations with variable delays and random jump magnitudes. It is proved that the numerical solutions converge to the analytical solutions in mean-square with the approximate rate of 1/2. Furthermore, the mean-square stability of the exact solutions and the numerical solutions are investigated via a linear test equation and the results show that the proposed numerical method shares both the mean-square stability and the so-called A-stability.

scholarly journals The truncated Milstein method for super-linear stochastic differential equations with Markovian switching

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Weijun Zhan ◽  
Qian Guo ◽  
Yuhao Cong
Keyword(s):  
Markov Chain ◽  
Differential Equations ◽  
Convergence Rate ◽  
Stochastic Differential Equations ◽  
Convergence Result ◽  
Convergence Order ◽  
Mean Square ◽  
Numerical Example ◽  
Milstein Method ◽  
The Mean

<p style='text-indent:20px;'>In this paper, to approximate the super-linear stochastic differential equations modulated by a Markov chain, we investigate a truncated Milstein method with convergence order 1 in the mean-square sense. Under Khasminskii-type conditions, we establish the convergence result by employing a relationship between local and global errors. Finally, we confirm the convergence rate by a numerical example.</p>

An averaging principle for stochastic differential equations of fractional order 0 < α < 1

2020 ◽  
Vol 23 (3) ◽  
pp. 908-919 ◽  
Author(s):  
Wenjing Xu ◽  
Wei Xu ◽  
Kai Lu
Keyword(s):  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Fractional Order ◽  
Mild Solution ◽  
Averaging Principle ◽  
Mean Square ◽  
Fractional Systems ◽  
Before And After ◽  
Fractional Stochastic Differential Equations ◽  
Fractional Equation

AbstractThis paper presents an averaging principle for fractional stochastic differential equations in ℝn with fractional order 0 < α < 1. We obtain a time-averaged equation under suitable conditions, such that the solutions to original fractional equation can be approximated by solutions to simpler averaged equation. By mathematical manipulations, we show that the mild solution of two equations before and after averaging are equivalent in the sense of mean square, which means the classical Khasminskii approach for the integer order systems can be extended to fractional systems.

scholarly journals Numerical Spline Method for Simulation of Stochastic Differential Equations systems: طريقة شرائحية عددية لمحاكاة حل نظم من المعادلات التفاضلية العشوائية

2021 ◽  
Vol 5 (4) ◽  
pp. 130-111
Author(s):  
Suliman M. Mahmoud, Ahmad Al-Wassouf, Ali S. Ehsaan Suliman M. Mahmoud, Ahmad Al-Wassouf, Ali S. Ehsaan
Keyword(s):  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Continuous Process ◽  
Mean Square ◽  
Spline Method ◽  
Third Order ◽  
Mean Square Stability ◽  
The Mean ◽  
Linear Problems ◽  
Two Parameters

In this paper, numerical spline method is presented with collocation two parameters for solving systems of multi-dimensional stochastic differential equations (SDEs). Multi-Wiener's time-continuous process is simulated as a discrete process, and then the mean-square stability of proposed method when applied to a system of two-dimensional linear SDEs is studied. The study shows that the method is mean-square stability and third-order convergent when applied to a system of linear and nonlinear SDEs. Moreover, the effectiveness of our method was tested by solving two test linear and non-linear problems. The numerical results show that the accuracy and applicability of the proposed method are worthy of attention.

Stochastic averaging principle for differential equations with non-Lipschitz coefficients driven by fractional Brownian motion

2017 ◽  
Vol 17 (02) ◽  
pp. 1750013 ◽  
Author(s):  
Yong Xu ◽  
Bin Pei ◽  
Jiang-Lun Wu
Keyword(s):  
Brownian Motion ◽  
Differential Equations ◽  
Fractional Brownian Motion ◽  
Stochastic Averaging ◽  
Hurst Parameter ◽  
Stochastic Integrals ◽  
Averaging Principle ◽  
Mean Square ◽  
Asymptotic Results ◽  
Different Types

In this paper, we are concerned with the stochastic averaging principle for stochastic differential equations (SDEs) with non-Lipschitz coefficients driven by fractional Brownian motion (fBm) of the Hurst parameter [Formula: see text]. We define the stochastic integrals with respect to the fBm in the integral formulation of the SDEs as pathwise integrals and we adopt the non-Lipschitz condition proposed by Taniguchi (1992) which is a much weaker condition with wider range of applications. The averaged SDEs are established. We then use their corresponding solutions to approximate the solutions of the original SDEs both in the sense of mean square and of probability. One can find that the similar asymptotic results are suitable for those non-Lipschitz SDEs with fBm under different types of stochastic integrals.

Sign in / Sign up

Export Citation Format

Share Document