Optimization Methods in Finance

ISBN-10: 0521861705
ISBN-13: 9780521861700
Edition: 2006
List price: $103.00 Buy it from $50.88
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Description: Optimization models play an increasingly important role in financial decisions. This is the first textbook devoted to explaining how recent advances in optimization models, methods and software can be applied to solve problems in computational  More...

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Book details

List price: $103.00
Copyright year: 2006
Publisher: Cambridge University Press
Publication date: 12/21/2006
Binding: Hardcover
Pages: 358
Size: 7.00" wide x 9.75" long x 1.00" tall
Weight: 2.046
Language: English

Optimization models play an increasingly important role in financial decisions. This is the first textbook devoted to explaining how recent advances in optimization models, methods and software can be applied to solve problems in computational finance more efficiently and accurately. Chapters discussing the theory and efficient solution methods for all major classes of optimization problems alternate with chapters illustrating their use in modeling problems of mathematical finance. The reader is guided through topics such as volatility estimation, portfolio optimization problems and constructing an index fund, using techniques such as nonlinear optimization models, quadratic programming formulations and integer programming models respectively. The book is based on Master's courses in financial engineering and comes with worked examples, exercises and case studies. It will be welcomed by applied mathematicians, operational researchers and others who work in mathematical and computational finance and who are seeking a text for self-learning or for use with courses.

Reha T�t�nc� is a Vice President in the Quantitative Resources Group at Goldman Sachs Asset Management, New York.

Introduction
Linear programming: theory and algorithms
LP models: asset/liability cash flow matching
LP models: asset pricing and arbitrage
Nonlinear programming: theory and algorithms
NLP volatility estimation
Quadratic programming: theory and algorithms
QP models: portfolio optimization
Conic optimization tools
Conic optimization models in finance
Integer programming: theory and algorithms
IP models: constructing an index fund
Dynamic programming methods
DP models: option pricing
DP models: structuring asset backed securities
Stochastic programming: theory and algorithms
SP models: value-at-risk
SP models: asset/liability management
Robust optimization: theory and tools
Robust optimization models in finance
Convexity
Cones
A probability primer
The revised simplex method
Bibliography
Index

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