We present Runge-Kutta methods of high accuracy for stochastic differential equations with constant diffusion coefficients. Abstract This paper gives a review of recent progress in the design of numerical methods for computing the trajectories (sample paths) of solutions to stochastic differential equations. In this paper we are interested in the numerical solution of stochastic differential equations with non negative solutions. Numerical Solution of Stochastic Differential Equations with Jumps in Finance Eckhard Platen School of Finance and Economics and School of Mathematical Sciences University of Technology, Sydney Kloeden, P.E. An Itô formula in the generality needed for Taylor … XXXVI, 632 pp., 85 figs., DM 118,OO. &Pl, E.: Numerical Solution of Stochastic Differential Equations Springer, Applications of Mathematics 23 (1992,1995,1999). We analyze L2 convergence of these methods and present convergence proofs. We first introduce Berlin etc., Springer‐Verlag 1992. Pl, E. &Heath, D.: A Benchmark Approach to Quantitative Finance, … There are two types of convergence for a numerical solution of a stochastic differential equation, the strong convergence and the weak convergence. For scalar equations a second-order method is derived, and for systems … Numerical Solution of Stochastic Differential Equations with Constant Diffusion Coefficients By Chien-Cheng Chang Abstract. The numerical solution of stochastic partial differential equations (SPDEs) is at a stage of development roughly similar to that of stochastic ordinary differential equations (SODEs) in the 1970s, when stochastic Taylor schemes based on an iterated application of the Itô formula were introduced and used to derive higher order numerical schemes. We give a brief survey of the area focusing on a number of Kloeden, P. E.; Platen, E., Numerical Solution of Stochastic Differential Equations. In this dissertation, we consider the problem of simulation of stochastic differential equations driven by Brownian motions or the general Lévy processes.