Backward stochastic differential equations : from linear to fully nonlinear theory
Material type:
TextSeries: Probability theory and stochastic modelling ; 86Publication details: New York, NY : Springer, 2017. Description: xv, 386 pagesISBN: 9781493984329Subject(s): Economic theory | Economics, Mathematical | Game theory | Numerical analysis | Partial differential equations | Probabilities | Probability theory and stochastic processes | Economic theory, quantitative economics, mathematical methods | Game theory, economics, social and behav. Sciences | Quantitative finance | Stochastic differential equations | Viscosity solutionsDDC classification: 519.21 Online resources: Table of content | Reviews Summary: 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.
| Item type | Current library | Call number | Status | Date due | Barcode |
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NISER LIBRARY | 519.21 ZHA-B (Browse shelf(Opens below)) | Available | 25711 |
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.
Description based on publisher-supplied MARC data.
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