Practical Financial Optimization: Decision Making for Financial Engineers

ISBN-13: 9781405132008 / Angielski / Twarda / 2007 / 432 str.

Stavros Zenois; Stavros A. Zenios; Giuseppe Bertola

Opis

Practical Financial Optimization is a comprehensive guide to optimization techniques in financial decision making. This book illuminates the relationship between theory and practice, providing the readers with solid foundational knowledge.

• Focuses on classical static mean-variance analysis and portfolio immunization, scenario-based models, multi-period dynamic portfolio optimization, and the relationships between classes of models
• Analyizes real world applications and implications for financial engineers
• Includes a list of models and a section on notations that includes a glossary of symbols and abbreviations

Recenzje

This volume is both a comprehensive guide to optimization techniques useful in financial decision making and a well–illustrated essay on the relationship between theory and practice. While the real problem may always be more complex than any model of it we build, that does not necessarily imply that the largest, most complex model will serve us best. Zenios supplies the reader with a spectrum of optimization models, from simple to complex, and sage advice on how to use them.
From the Foreword by Harry M. Markowitz, Nobel Laureate in Economics

Most books on portfolio optimization focus on continuous time stochastic control models. By contrast, Zenios s decision to focus on mathematical programming models in financial engineering is an auspicious one. The book is well organized and clearly written, and uses a minimum of technical prerequisites (both mathematical and financial). It should therefore be accessible and of interest to a broad audience: industry practitioners interested in the potential application of optimization to the problems they face, students curious about how optimization is applied in finance, and professional researchers who would like a comprehensive overview of the uses of mathematical programming in financial engineering.
David Saunders, University of Waterloo

Spis treści

Foreword: Harry M. Markowitz.

Preface.

Acknowledgments.

Notation.

List of Models.

I. Introduction.

1. An Optimization View of Financial Engineering.

2. Basics of Risk Management.

II. Portfolio Optimization Models.

3. Mean–Variance Analysis.

4. Portfolio Models for Fixed Income.

5. Scenario Optimization.

6. Dynamic Portfolio Optimization with Stochastic Programming.

7. Index Funds.

8. Designing Financial Products.

9. Scenario Generation.

III. Applications.

10. International Asset Allocation.

11. Corporate Bond Portfolios.

12. Insurance Policies with Guarantees.

13. Personal Financial Planning.

IV. Library of Financial Optimization Models.

14. FINLIB: A Library of Financial Optimization Models.

Bibliography.

Index

O autorze

Stavros A. Zenios is Professor of Business and Public Administration at the University of Cyprus, Director of the HERMES European Center of Excellence on Computational Finance and Economics, and Senior Fellow at the Wharton Financial Institutions Center of the University of Pennsylvania. His previous books include Financial Optimization (1996); Parellel Optimization: Theory, Algorithms, and Applications (1997); and Performance of Financial Institutions: Efficiency, Innovation, Regulation (2000).

Blurb

This book gives a comprehensive account of financial optimization models used to support decision–making for financial engineers. It starts with the classical static mean–variance analysis and portfolio immunization, moves on to scenario–based models, and builds towards multi–period dynamic portfolio optimization.

As the story unfolds, the relationships between classes of models are revealed. Once the foundations are laid with several building blocks and the broad landscape of financial optimization is charted, the book moves on to analyze several real–world applications. In this way the reader acquires not only solid knowledge of the foundations of financial optimization, but also a taste for the large–scale models that can be grounded on these foundations. The math prerequisite is optimization with matrix algebra.