Balanced-Euler approximation schemes for stiff systems of stochastic differential equations


Hassan Ranjbar, Leila Torkzadeh, Kazem Nouri




This paper aims to design new families of balanced-Euler approximation schemes for the solutions of stiff stochastic differential systems. To prove the mean-square convergence, we use some fundamental inequalities such as the global Lipschitz condition and linear growth bound. The mean-square stability properties of our new schemes are analyzed. Also, numerical examples illustrate the accuracy and efficiency of the proposed schemes.