scholarly journals An adaptive discretization algorithm for the weak approximation of stochastic differential equations

PAMM ◽  
2004 ◽  
Vol 4 (1) ◽  
pp. 19-22 ◽  
Author(s):  
Andreas Rößler
Keyword(s):  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Weak Approximation ◽  
Adaptive Discretization ◽  
Discretization Algorithm

A third-order weak approximation of multidimensional Itô stochastic differential equations

2019 ◽  
Vol 25 (2) ◽  
pp. 97-120 ◽  
Author(s):  
Riu Naito ◽  
Toshihiro Yamada
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Differential Equations ◽  
Stochastic Differential Equations ◽  
Wiener Space ◽  
Weak Approximation ◽  
Third Order ◽  
Brownian Motions ◽  
Numerical Examples ◽  
Discretization Algorithm

Abstract This paper proposes a new third-order discretization algorithm for multidimensional Itô stochastic differential equations driven by Brownian motions. The scheme is constructed by the Euler–Maruyama scheme with a stochastic weight given by polynomials of Brownian motions, which is simply implemented by a Monte Carlo method. The method of Watanabe distributions on Wiener space is effectively applied in the computation of the polynomial weight of Brownian motions. Numerical examples are shown to confirm the accuracy of the scheme.

An introduction to numerical methods for stochastic differential equations

Acta Numerica ◽  
1999 ◽  
Vol 8 ◽  
pp. 197-246 ◽  
Author(s):  
Eckhard Platen
Keyword(s):  
Numerical Analysis ◽  
Numerical Methods ◽  
Differential Equations ◽  
Stochastic Differential Equations ◽  
The Other ◽  
Weak Approximation ◽  
Approximation Methods ◽  
Unified Framework ◽  
Stochastic Nature ◽  
The One

This paper aims to give an overview and summary of numerical methods for the solution of stochastic differential equations. It covers discrete time strong and weak approximation methods that are suitable for different applications. A range of approaches and results is discussed within a unified framework. On the one hand, these methods can be interpreted as generalizing the well-developed theory on numerical analysis for deterministic ordinary differential equations. On the other hand they highlight the specific stochastic nature of the equations. In some cases these methods lead to completely new and challenging problems.

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