Optimization Methods in Finance

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.

ISBN 0521861705   Volumes 1 ISBN13 9780521861700 (What's this?)   Weight (grammes) 772 Publisher Cambridge University Press   Published in Cambridge Imprint Cambridge University Press   Series ISSN 5 Format Hardback   Series title Mathematics, Finance and Risk Publication date 21 Dec 2006   Height (mm) 247 Library of Congress HG   Width (mm) 174 DEWEY 332.015196   Spine width (mm) 23 DEWEY edition DC22   Academic level Professional / Scholarly Pages 358

1		Introduction		1
2		Linear programming : theory and algorithms		15
3		LP models : asset/liability cash-flow matching		41
4		LP models : asset pricing and arbitrage		62
5		Nonlinear programming : theory and algorithms		80
6		NLP models : volatility estimation		112
7		Quadratic programming : theory and algorithms		121
8		QP models : portfolio optimization		138
9		Conic optimization tools		168
10		Conic optimization models in finance		178
11		Integer programming : theory and algorithms		192
12		Integer programming models : constructing an index fund		212
13		Dynamic programming methods		225
14		DP models : option pricing		240
15		DP models : structuring asset-backed securities		248
16		Stochastic programming : theory and algorithms		255
17		Stochastic programming models : value-at-risk and conditional value-at-risk		271
18		Stochastic programming models : asset/liability management		279
19		Robust optimization : theory and tools		292
20		Robust optimization models in finance		306
App. A		Convexity		320
App. B		Cones		322
App. C		A probability primer		323
App. D		The revised simplex method		327

'This book would certainly appeal to someone with a mathematical background, perhaps in operations research, wishing to update and apply their knowledge to the financial world.' Mathematics TODAY

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