- Paperback: 432 pages
- Publisher: Wiley-Blackwell (29 Feb 2008)
- Language English
- ISBN-10: 1405132019
- ISBN-13: 978-1405132015
- Product Dimensions: 24.4 x 19 x 2.2 cm
- Amazon Bestsellers Rank: 866,931 in Books (See Top 100 in Books)
- See Complete Table of Contents
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"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
Product Description
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
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22 Mar 2011
Format:Paperback
To get a hot-button topic out of the way, I was surprised by the proposed approach to dynamic optimization. Instead of using dynamic programming (DP), the author takes advantage of specific problems' linearity and solves "flattened out" linear programs, optimizing decisions in all states - e.g. in all nodes of a state-transition lattice - in one go. It is not clear what is gained in exchange for the resulting loss of generality; "curse of dimensionality" is mooted, but this argument is never developed, and DP dismissed in a single paragraph.
Looking over Parts I-II - and leaving out the "applications" Part III: Chapter 1 gives a broad overview of risk management. Chapter 2 goes on to risk measurement, presents risk measures such as return variance, VaR, CVaR, etc. and discusses CAPM for equities, and duration and convexity for FI; factor models are covered, and a PCA-decomposition-of-yield-curve exercise shown. Chapter 3 describes mean-variance portfolio optimization, and Chapter 4 discusses immunization and dedication. (Multi-period problems both, but with a one-time decision). Chapter 5 moves onto dynamic optimization, introduces "scenario optimization" and shows examples of relevant objective-function/constraint-set combinations. Chapter 6 presents dynamic portfolio optimization - with intertemporal constraints - and "stochastic programming"; in-depth examples are provided in Chapters 6-8. Chapter 9 addresses generation of scenarios.
The book is not too technical - master's, not PhD-student level, I'd say; definitely not another Meucci - which I think is the best for practitioner audience. Readers who wish to go further can make use of extensive, commented chapter bibliographies - and those wishing to get their hands dirty and actually get some numbers out can shell out $80 on the companion GAMS-aided book. (In my opinion, a pain and the albatross of this combo - which could have been something like Brandimarte's text).
An original and well-written book that deserves a wider audience. (Too bad the publishers can't even be bothered to enable "Search inside"). Do take a look.
Looking over Parts I-II - and leaving out the "applications" Part III: Chapter 1 gives a broad overview of risk management. Chapter 2 goes on to risk measurement, presents risk measures such as return variance, VaR, CVaR, etc. and discusses CAPM for equities, and duration and convexity for FI; factor models are covered, and a PCA-decomposition-of-yield-curve exercise shown. Chapter 3 describes mean-variance portfolio optimization, and Chapter 4 discusses immunization and dedication. (Multi-period problems both, but with a one-time decision). Chapter 5 moves onto dynamic optimization, introduces "scenario optimization" and shows examples of relevant objective-function/constraint-set combinations. Chapter 6 presents dynamic portfolio optimization - with intertemporal constraints - and "stochastic programming"; in-depth examples are provided in Chapters 6-8. Chapter 9 addresses generation of scenarios.
The book is not too technical - master's, not PhD-student level, I'd say; definitely not another Meucci - which I think is the best for practitioner audience. Readers who wish to go further can make use of extensive, commented chapter bibliographies - and those wishing to get their hands dirty and actually get some numbers out can shell out $80 on the companion GAMS-aided book. (In my opinion, a pain and the albatross of this combo - which could have been something like Brandimarte's text).
An original and well-written book that deserves a wider audience. (Too bad the publishers can't even be bothered to enable "Search inside"). Do take a look.
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