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Praise for Robust Portfolio Optimization and Management
"In the half century since Harry Markowitz introduced his elegant theory for selecting portfolios, investors and scholars have extended and refined its application to a wide range of real–world problems, culminating in the contents of this masterful book. Fabozzi, Kolm, Pachamanova, and Focardi deserve high praise for producing a technically rigorous yet remarkably accessible guide to the latest advances in portfolio construction."
––Mark Kritzman, President and CEO, Windham Capital Management, LLC
"The topic of robust optimization (RO) has become ′hot′ over the past several years, especially in real–world financial applications. This interest has been sparked, in part, by practitioners who implemented classical portfolio models for asset allocation without considering estimation and model robustness a part of their overall allocation methodology, and experienced poor performance. Anyone interested in these developments ought to own a copy of this book. The authors cover the recent developments of the RO area in an intuitive, easy–to–read manner, provide numerous examples, and discuss practical considerations. I highly recommend this book to finance professionals and students alike."
––John M. Mulvey, Professor of Operations Research and Financial Engineering, Princeton University
"In the half century since Harry Markowitz introduced his elegant theory for selecting portfolios, investors and scholars have extended and refined its application to a wide range of real–world problems, culminating in the contents of this masterful book. Fabozzi, Kolm, Pachamanova, and Focardi deserve high praise for producing a technically rigorous yet remarkably accessible guide to the latest advances in portfolio construction."
––Mark Kritzman, President and CEO, Windham Capital Management, LLC
"The topic of robust optimization (RO) has become ′hot′ over the past several years, especially in real–world financial applications. This interest has been sparked, in part, by practitioners who implemented classical portfolio models for asset allocation without considering estimation and model robustness a part of their overall allocation methodology, and experienced poor performance. Anyone interested in these developments ought to own a copy of this book. The authors cover the recent developments of the RO area in an intuitive, easy–to–read manner, provide numerous examples, and discuss practical considerations. I highly recommend this book to finance professionals and students alike."
––John M. Mulvey, Professor of Operations Research and Financial Engineering, Princeton University
From the Back Cover
Praise for Robust Portfolio Optimization and Management
"In the half century since Harry Markowitz introduced his elegant theory for selecting portfolios, investors and scholars have extended and refined its application to a wide range of real–world problems, culminating in the contents of this masterful book. Fabozzi, Kolm, Pachamanova, and Focardi deserve high praise for producing a technically rigorous yet remarkably accessible guide to the latest advances in portfolio construction."
—Mark Kritzman, President and CEO, Windham Capital Management, LLC
"The topic of robust optimization (RO) has become ′hot′ over the past several years, especially in real–world financial applications. This interest has been sparked, in part, by practitioners who implemented classical portfolio models for asset allocation without considering estimation and model robustness a part of their overall allocation methodology, and experienced poor performance. Anyone interested in these developments ought to own a copy of this book. The authors cover the recent developments of the RO area in an intuitive, easy–to–read manner, provide numerous examples, and discuss practical considerations. I highly recommend this book to finance professionals and students alike."
—John M. Mulvey, Professor of Operations Research and Financial Engineering, Princeton University
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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4 reviews
12 of 14 people found the following review helpful
A must read for Quant
29 Jun 2007
Format:Hardcover
Quick fact:
1) Highly recommand this book to serious Quants.
2) Graduate lever math is required for serious reader.
3) Good reference book and good for self-study
4) Well written, easy read.
5) worth the money.
The field of quantitative techniques have developed so much in the last 10 years, but almost no book cover enough serious topics about these new directions. I had already learn a bit of the robust techniques while working, including robust estimates, robust portfolio construction, error control, bayesian estimates and others. But those are all picked up in pieces, at different times, and with much research efforts. So you can imagine my delight to see a book that covers a lot of the pieces concisely.
This book itself is very well written, occasionally misspelled math labels are easily corrected by more math inclined reader, and will not interfare with casual reading. Like many of Fabozzi books, overall organization is slightly loose, so that you can start any chapter in the book and still get pretty much decent view about that subject. But better written for quants than some of Fabozzi's early books (which are mainly used as reference books)
what is missing in this book?
just one: sometimes, reference papers or books are given even though a little more details would save serious reader a lot more time. Yes, I know, those are advance topics, still would like to see them as a serious reader. Maybe as appendix for relevent chapers.
Over all, worth every penny of it.
1) Highly recommand this book to serious Quants.
2) Graduate lever math is required for serious reader.
3) Good reference book and good for self-study
4) Well written, easy read.
5) worth the money.
The field of quantitative techniques have developed so much in the last 10 years, but almost no book cover enough serious topics about these new directions. I had already learn a bit of the robust techniques while working, including robust estimates, robust portfolio construction, error control, bayesian estimates and others. But those are all picked up in pieces, at different times, and with much research efforts. So you can imagine my delight to see a book that covers a lot of the pieces concisely.
This book itself is very well written, occasionally misspelled math labels are easily corrected by more math inclined reader, and will not interfare with casual reading. Like many of Fabozzi books, overall organization is slightly loose, so that you can start any chapter in the book and still get pretty much decent view about that subject. But better written for quants than some of Fabozzi's early books (which are mainly used as reference books)
what is missing in this book?
just one: sometimes, reference papers or books are given even though a little more details would save serious reader a lot more time. Yes, I know, those are advance topics, still would like to see them as a serious reader. Maybe as appendix for relevent chapers.
Over all, worth every penny of it.
7 of 8 people found the following review helpful
Excellent, Cutting-edge!
1 Oct 2007
Format:Hardcover|Amazon Verified Purchase
This book is fabulous. It covers the latest optimization and statistical methods in Finance as well as the modern portfolio theory and some risk management and can be used for audience with Finance background, or optimization or statistics background. It is definitely one of the best books serving as an interface between Finance and Operations Research (O.R.). The other excellent book is "Optimization Methods in Finance," by Cornuejols and Tutuncu (2007), which discusses O.R. techniques with financial applications. This one is almost the opposite - it focuses on modern portfolio theory and discusses how O.R. and statistical methods can be applied. Both books discuss some latest optimization techniques - however, this one has much more details on a modern method for handling uncertainty called Robust Optimization, in which Dr. Pachamanova (third author) is an expert, as well as some relatively advanced stat methods such as robust statistics and Bayesian estimation. Despite the advanced methods, I found this book Fabozzi (2007) clearly written and quite easy to follow, just like Cornuejols and Tutuncu. The only comment I have is that I wish there was a list of references at the back, just like most other books.
Another text I would recommend is Ruppert's "Statistics and Finance: An Introduction" which serves as an interface between statistics and finance, as the title indicated.
Another text I would recommend is Ruppert's "Statistics and Finance: An Introduction" which serves as an interface between statistics and finance, as the title indicated.
3 of 3 people found the following review helpful
Robust is a Must
18 Aug 2010
Format:Hardcover
This is the best quant finance book I've yet read. The symbols on the cover may look daunting, but the text actually keeps notation simple. Many topics are covered quickly and accessibly; this is a math book you can actually skim, or skip around in. I think that's due to good writing.
Also: I stand firmly in the Robust camp. After my class with Karen Kafadar, I'm confident that Robust models are easier to explain and more reliable. Her typical example was to mis-type just one of the data by repeating a digit or moving the decimal place -- and how likely is that! -- and see how much the output changed. Ideally your real-world recommendation shouldn't change too much based on just one data point. (If that's unavoidable, you should withdraw any recommendation.)
So many mathematical questions or ideas yield up a flowering of possible tweaks and adjustments that can be made to a model, with no recommendation of which parameter value to use. A good answer is: whatever is most stable across different potential scenarios.
There is a wide variance among the Frank J. Fabozzi series (Advanced Stochastic Optimization, for example, is way worse than this). If you only have time to read one, read this one.
Also: I stand firmly in the Robust camp. After my class with Karen Kafadar, I'm confident that Robust models are easier to explain and more reliable. Her typical example was to mis-type just one of the data by repeating a digit or moving the decimal place -- and how likely is that! -- and see how much the output changed. Ideally your real-world recommendation shouldn't change too much based on just one data point. (If that's unavoidable, you should withdraw any recommendation.)
So many mathematical questions or ideas yield up a flowering of possible tweaks and adjustments that can be made to a model, with no recommendation of which parameter value to use. A good answer is: whatever is most stable across different potential scenarios.
There is a wide variance among the Frank J. Fabozzi series (Advanced Stochastic Optimization, for example, is way worse than this). If you only have time to read one, read this one.
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