scholarly journals Simulation Method For Solving Stochastic Differential Equations With Constant Diffusion Coefficients

2014 ◽  
Vol 08 (01) ◽  
pp. 28-32
Author(s):  
Behrouz Fathi Vajargah ◽  
Rahim Asghari
2018 ◽  
Vol 30 (5) ◽  
pp. 928-944 ◽  
Author(s):  
A. T. MEIMARIS ◽  
I. A. KOUGIOUMTZOGLOU ◽  
A. A. PANTELOUS

An approximate analytical solution is derived for a certain class of stochastic differential equations with constant diffusion, but nonlinear drift coefficients. Specifically, a closed form expression is derived for the response process transition probability density function (PDF) based on the concept of the Wiener path integral and on a Cauchy–Schwarz inequality treatment. This is done in conjunction with formulating and solving an error minimisation problem by relying on the associated Fokker–Planck equation operator. The developed technique, which requires minimal computational cost for the determination of the response process PDF, exhibits satisfactory accuracy and is capable of capturing the salient features of the PDF as demonstrated by comparisons with pertinent Monte Carlo simulation data. In addition to the mathematical merit of the approximate analytical solution, the derived PDF can be used also as a benchmark for assessing the accuracy of alternative, more computationally demanding, numerical solution techniques. Several examples are provided for assessing the reliability of the proposed approximation.


2016 ◽  
Vol 5 (2) ◽  
pp. 97
Author(s):  
Mohammed Alsukaini ◽  
Walaa Alkreemawi ◽  
Xiang-Jun Wang

<p>In this paper we investigate consistency and asymptotic normality of the posterior distribution of the parameters in the stochastic differential equations (SDE’s) with diffusion coefficients depending nonlinearly on a random variables  and  (the random effects).The distributions of the random effects  and  depends on unknown parameters which are to be estimated from the continuous observations of the independent processes . We propose the Gaussian distribution for the random effect  and the exponential distribution for the random effect    , we obtained an explicit formula for the likelihood function and find the estimators of the unknown parameters in the random effects.</p>


2020 ◽  
Vol 26 (1) ◽  
pp. 33-47
Author(s):  
Kamal Hiderah

AbstractThe aim of this paper is to show the approximation of Euler–Maruyama {X_{t}^{n}} for one-dimensional stochastic differential equations involving the maximum process. In addition to that it proves the strong convergence of the Euler–Maruyama whose both drift and diffusion coefficients are Lipschitz. After that, it generalizes to the non-Lipschitz case.


2018 ◽  
Vol 64 ◽  
pp. 65-77
Author(s):  
Paul-Éric Chaudru de Raynal ◽  
Gilles Pagès ◽  
Clément Rey

The goal of this paper is to present a series of recent contributions arising in numerical probability. First we present a contribution to a recently introduced problem: stochastic differential equations with constraints in law, investigated through various theoretical and numerical viewpoints. Such a problem may appear as an extension of the famous Skorokhod problem. Then a generic method to approximate in a weak way the invariant distribution of an ergodic Feller process by a Langevin Monte Carlo simulation. It is an extension of a method originally developed for diffusions and based on the weighted empirical measure of an Euler scheme with decreasing step. Finally, we mention without details a recent development of a multilevel Langevin Monte Carlo simulation method for this type of problem.


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