Optimization Methods in Finance (Mathematics, Finance and Risk, Series Number 5)

By Javier Peña, Reha Tutuncu, Reha Tütüncü

Book Overview

Optimization methods play a central role in financial modeling. This textbook is devoted to explaining how state-of-the-art optimization theory, algorithms, and software can be used to efficiently... This description may be from another edition of this product.

• Format: Hardcover
• Language: English
• ISBN: 0521861705
• ISBN13: 9780521861700
• Release Date: January 2007
• Publisher: Cambridge University Press
• Length: 358 Pages
• Weight: 1.60 lbs.
• Dimensions: 9.8" x 1.0" x 7.0"

Customer Reviews

Wonderful book with lots of financial applications

This book is very well-written with some theories and tons of financial applications. I hadn't thought that a GARCH model for estimating stochastic volatility can be seen as a nonlinear programming application. It touches some relatively modern/advanced topics such as robust optimization and cone optimization. It's just a wonderful optimization book for financial engineers and people in the financial industry.

Modern view, balanced coverage of optimization methods for quantitative finance

This is a very up-to-date book featuring complete, balanced coverage of optimization methods used in quantitative finance. It should be a great resource for practitioners in financial engineering or portfolio management who need to know what methods to apply to different problems, and how to evaluate competing vendor claims, without going too deeply into the algorithmic details of each optimization method. The book has 20 chapters that alternate between an overview of a class of optimization methods, then a set of examples applying those methods to problems in quantitative finance: * Linear programming, with applications to asset/liability cash flow matching and arbitrage detection * Nonlinear programming, with applications to volatility estimation * Quadratic programming, with good coverage of mean-variance portfolio optimization * Conic optimization, with several applications: index tracking, approximating covariance matrices, recovering risk-neutral probabilities from option prices * Integer programming, with applications to index fund construction and combinatorial auctions * Dynamic programming, with applications to pricing American options * Stochastic programming, with applications that minimize Conditional Value at Risk, and manage assets and liabilities over multiple periods * Robust optimization, with models to deal with estimation risk in portfolio optimization It's difficult to find another book with this breadth of coverage of optimization methods, especially with a focus on quantitative finance. It's also difficult to find another book that treats modern methods of conic optimization and robust optimization, which have growing importance in finance. Granted, the treatment of the different applications is not meant to be comprehensive -- it's really just enough to give the reader an idea of how each problem can be approached, with appropriate references to the academic literature to learn more. There are some references to available software for the different methods, but this is a brief and partial snapshot; commercial software for the newer methods is getting better all the time. Appendices provide brief, helpful introductions to four key technical topics in optimization: Convexity, cones, probability, and the revised Simplex method. An understanding of convexity and cones is essential to an appreciation of modern methods of conic and robust optimization, and certainly anyone working in this field needs an understanding of probability and the Simplex method for linear programming. I believe this book fills a need that has existed for some time: For the quantitative finance practitioner with way too much technical literature to deal with, it provides a comprehensive, modern introduction to optimization methods that makes efficient use of the reader's time. It's well worth the price for someone working in this field.