The truncated Euler-Maruyama method for stochastic differential equations

By Xuerong Mao. Journal of Computational and Applied Mathematics, 2015

322 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Explicit numerical approximations for stochastic differential equations in finite and infinite horizons: truncation methods, convergence in pth moment, and stability (2019, methodology): “In this paper, adopting the truncation idea from the study by Mao (2015) and using a novel approximation technique, we construct several explicit schemes under certain assumptions on the coefficients of the SDEs and derive convergence results in both finite and infinite time intervals.”
Adaptive Euler–Maruyama method for SDEs with nonglobally Lipschitz drift (2016, unknown): “(6) Together, (5) and (6) imply the monotone condition 〈x, f (x)〉+ 12‖g(x)‖ 2 ≤ 3 2 (α‖x‖ 2+β ), which is a key assumption in the analysis of Mao & Szpruch [22] and Mao [21] for SDEs with volatilities which are not globally Lipschitz.”
The partially truncated Euler–Maruyama method and its stability and boundedness (2017, methodology): “Recently, the truncated EM method was developed in [20, 21], where the finite-time strong convergence theory was established and the order of Lconvergence was shown to be arbitrarily close to q/2 for a class of SDEs including the underlying SDE (1.”