Practical Financial Optimization

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

Foreword.

Preface.

Acknowledgements.

List of Models.

Notation.

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. Application I: International Asset Allocation.

11. Application II: Corporate Bond Portfolios.

12. Application III: Insurance Policies with Guarantees.

13. Application IV: Personal Financial Planning.

IV. Library of Financial Optimization Models.

14. FINLIB: A Library of Financial Optimization Models

A. Basics of Optimization.

B. Basics of Probability Theory.

C. Stochastic Processes.

Bibliography.

Index.

Stavros 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). 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 prerequisites are optimization with ODEs/matrix algebra.