Backward Stochastic Differential Equations

Provides a systematic study from linear equations to fully nonlinear equations

Includes up-to-date developments in the field

A powerful and convenient tool for financial engineering and stochastic optimization

Accessible to graduate students and junior researchers

Provides a systematic study from linear equations to fully nonlinear equations Includes up-to-date developments in the field A powerful and convenient tool for financial engineering and stochastic optimization Accessible to graduate students and junior researchers Includes supplementary material: sn.pub/extras

Autorentext
Jianfeng Zhang is a professor of Mathematics at the University of Southern California, Los Angeles. His research interests include stochastic analysis, backward stochastic differential equations, stochastic numerics, and mathematical finance.

Inhalt
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.