Paperback. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo: Backward Stochastic Differential Equations (Paperback)
Editor: Springer-Verlag New York Inc., New York
Fecha de publicaci�n: 2018
Idioma: Ingl�s
ISBN 10: 1493984322
ISBN 13: 9781493984329
Encuadernaci�n: Paperback
Estado: new

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Sinopsis

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

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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.

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