Speaker

 Dr.Jianyuan Chang, Research Fellow, Department of Mathematics and Statistics, the University of Melbourne, Australia     

　　Title

     High Dimensional Stochastic Regression with Latent Factors, Endogeneity and Nonlinearity                  

　　Time

　　Venue

　　S703 

　　Abstract

   In this talk we review theoretical results on the mean-square convergence of numerical methods for stochastic ordinary differential equations, stochastic delay differential equations, neutral stochastic delay differential equations, jump-diffusion differential equations, neutral stochastic delay differential equations with jump-diffusion, stochastic partial differential equations. These results are called fundamental convergence theorems of numerical methods for stochastic differential equations. In this talk we propose a fundamental convergence theorem of semidiscretisation for stochastic Schroedinger equations in temporal direction. And based on Feynman-Kac type formula on backward stochastic differential equations, we present a fundamental convergence theorem of numerical methods for backward stochastic differential equations, and apply it to the mean-square convergence of numerical schemes for backward stochastic differential equations.

　　Affiliation