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Practical Financial Optimization. Decision Making for Financial Engineers
John Wiley and Sons Ltd, January 2008, Pages: 432
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: Harry M. Markowitz.
List of Models.
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.
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.
“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