TY - JOUR
T1 - The linear Steklov method for SDEs with non-globally Lipschitz coefficients: Strong convergence and simulation
AU - Díaz Infante Velasco, Saúl
AU - Jerez, Silvia
PY - 2017/1
Y1 - 2017/1
N2 - We present an explicit numerical method for solving stochastic differential equations with non-globally Lipschitz coefficients. A linear version of the Steklov average under a split-step formulation supports our new solver. The linear Steklov method converges strongly with a standard one-half order. Also, we present numerical evidence that the explicit linear Steklov reproduces almost surely stability solutions with high-accuracy for diverse application models even for stochastic differential systems with super-linear diffusion coefficients.
AB - We present an explicit numerical method for solving stochastic differential equations with non-globally Lipschitz coefficients. A linear version of the Steklov average under a split-step formulation supports our new solver. The linear Steklov method converges strongly with a standard one-half order. Also, we present numerical evidence that the explicit linear Steklov reproduces almost surely stability solutions with high-accuracy for diverse application models even for stochastic differential systems with super-linear diffusion coefficients.
KW - Explicit methods; Steklov average; Stochastic differential equations; Strong convergence
UR - http://linkinghub.elsevier.com/retrieve/pii/S0377042716301844
U2 - 10.1016/j.cam.2016.04.011
DO - 10.1016/j.cam.2016.04.011
M3 - Artículo
VL - 309
SP - 408
JO - Journal of Computational and Applied Mathematics
JF - Journal of Computational and Applied Mathematics
SN - 0377-0427
ER -