TY  - BOOK
AU  - Zhang,Jianfeng
ED  - SpringerLink (Online service)
TI  - Backward Stochastic Differential Equations: From Linear to Fully Nonlinear Theory 
T2  - Probability Theory and Stochastic Modelling,
SN  - 9781493972562
AV  - QA273.A1-274.9 
U1  - 519.2 23
PY  - 2017///
CY  - New York, NY
PB  - Springer New York, Imprint: Springer
KW  - Distribution (Probability theory
KW  - Finance
KW  - Mathematics
KW  - Differential equations, partial
KW  - Numerical analysis
KW  - Economic theory
KW  - Probability Theory and Stochastic Processes
KW  - Quantitative Finance
KW  - Game Theory, Economics, Social and Behav. Sciences
KW  - Partial Differential Equations
KW  - Numerical Analysis
KW  - Economic Theory/Quantitative Economics/Mathematical Methods
N1  - Preliminaries -- Part I The Basic Theory of SDEs and BSDEs -- Basics of Stochastic Calculus -- Stochastic Differential Equations -- Backward Stochastic Differential Equations -- Markov BSDEs and PDEs -- Part II Further Theory of BSDEs -- Reflected BSDEs -- BSDEs with Quadratic Growth in Z -- Forward Backward SDEs -- Part III The Fully Nonlinear Theory of BSDEs -- Stochastic Calculus Under Weak Formulation -- Nonlinear Expectation -- Path Dependent PDEs -- Second Order BSDEs.. Bibliography -- Index
N2  - This book provides a systematic and accessible approach to stochastic differential equations, backward stochastic differential equations, and their connection with partial differential equations, as well as the recent development of the fully nonlinear theory, including nonlinear expectation, second order backward stochastic differential equations, and path dependent partial differential equations. Their main applications and numerical algorithms, as well as many exercises, are included. The book focuses on ideas and clarity, with most results having been solved from scratch and most theories being motivated from applications. It can be considered a starting point for junior researchers in the field, and can serve as a textbook for a two-semester graduate course in probability theory and stochastic analysis. It is also accessible for graduate students majoring in financial engineering
UR  - https://doi.org/10.1007/978-1-4939-7256-2
ER  -