Del 86 i serien Probability Theory and Stochastic Modelling

Backward Stochastic Differential Equations

From Linear to Fully Nonlinear Theory

Häftad, Engelska, 2018

AvJianfeng Zhang

969 kr

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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.

Produktinformation

Utgivningsdatum2018-08-03
Mått155 x 235 x 25 mm
Vikt612 g
FormatHäftad
SpråkEngelska
SerieProbability Theory and Stochastic Modelling
Antal sidor388
FörlagSpringer-Verlag New York Inc.
ISBN9781493984329

Tillhör följande kategorier

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.

“This book (written by one of the leading experts in the field) constitutes a very handy and self-contained resource on BSDEs, both for people who want to get acquainted with the theory of BSDEs and Ph.D. students who aim to work in this field. … Overall the book is very well written and pleasant to read, and will likely become a classical reference on the topic.” (Anthony Réveillac, Mathematical Reviews, December, 2018)“The book prefers clarity over generality in order to be more accessible and readable for the readers who are expected to be mainly Ph.D. students and junior researches in stochastic analysis.” (Martin Ondreját, zbMATH 1390.60004, 2018)