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Backward stochastic differential equ...

Zhang, Jianfeng.

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Backward stochastic differential equations = from linear to fully nonlinear theory /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Backward stochastic differential equations/ by Jianfeng Zhang.
Reminder of title:	from linear to fully nonlinear theory /
Author:	Zhang, Jianfeng.
Published:	New York, NY :Springer New York : : 2017.,
Description:	xvi, 388 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Stochastic differential equations. -
Online resource:	http://dx.doi.org/10.1007/978-1-4939-7256-2
ISBN:	9781493972562

Backward stochastic differential equations = from linear to fully nonlinear theory /
Zhang, Jianfeng.

Backward stochastic differential equationsfrom linear to fully nonlinear theory /[electronic resource] :by Jianfeng Zhang. - New York, NY :Springer New York :2017. - xvi, 388 p. :ill., digital ;24 cm. - Probability theory and stochastic modelling,v.862199-3130 ;. - Probability theory and stochastic modelling ;v.86..

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 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.

ISBN: 9781493972562

Standard No.: 10.1007/978-1-4939-7256-2doiSubjects--Topical Terms:

621860
Stochastic differential equations.

LC Class. No.: QA274.23

Dewey Class. No.: 519.22

Backward stochastic differential equations = from linear to fully nonlinear theory /
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505 0 $a 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.
520 $a 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.
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