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Image of - Backward Stochastic Differential Equations From Linear To Fully Nonlinear Theory | Paperback
Backward Stochastic Differential Equations From Linear To Fully Nonlinear Theory | Paperback

Backward Stochastic Differential Equations From Linear To Fully Nonlinear Theory | Paperback

by Zhang J.

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.

Highlights

  • binding-icon

    9781493984329

    ISBN:

  • binding-icon

    Zhang J.

    Author:

  • binding-icon

    388

    Pages:

  • binding-icon

    239 gm

    Weight:

  • langauage-icon

    English

    Language:

  • date-icon

    2017

    Year:

  • edition-icon

    1st Edition

    Edition:

  • binding-icon

    Paperback

    Binding:

₹5254

₹6567

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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