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Risk and Asset Allocation (Anglais) Broché – 20 mai 2009

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Book by Meucci Attilio

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Couverture | Copyright | Table des matières | Extrait | Index
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20 internautes sur 22 ont trouvé ce commentaire utile 
outstanding professional resource 24 août 2010
Par NY reviewer - Publié sur Amazon.com
Format: Broché
This outstanding book on portfolio theory is a must-have for the professional risk-manager and trader. Note that this bound book is really one of three that Dr. Meucci has written; there is a full-length technical appendix and a full-length problems book that are on-line and free of charge. Also, all of his code is available from the Matlab Central site.

I acknowledge another reviewer's pov that the notation is non-standard, however I have a different reaction. Meucci has designed a notation that uniformly covers what are otherwise highly diverse fields. With this unified notation connections and comparisons are made quickly and effectively across areas that have to date been hard to reconcile. For instance, Chapter 5 on indices of satisfaction: I defy anyone to have a clearer comparison on the certainty equivalent, variance at risk, and coherence measures -- three areas that to my readings of the literature are otherwise unavailable all in one place. As another example: portfolio theory *is* all about multidimensional distributions, and Meucci covers uni- and multi-variate statistics in his first three chapters with deep additions in his technical appendices. Using this as a base it is clear how to construct and forecast the returns on a portfolio.

This book additionally brings robust statistical analysis to the fore. Rather than leaving the reader with a multivariate gaussian models and Markowitz mean-variance optimization Meucci starts in his later chapters a full repeal of these simple approaches and looks both at robust distribution analysis along with robust, or constrained, such as second-order cone programming, analysis of returns and optimization. This is the forefront of risk theory.

Given that Dr. Meucci lectures around the world on these materials and has made so much of his work available and largely free, I find it the height of laziness of the other reviewer to given 1 star and complain about notation. Rather, Meucci's book and material are the starting point for a well-conceived approach to the field and literature.
26 internautes sur 37 ont trouvé ce commentaire utile 
Not for the faint-hearted 31 janvier 2007
Par Frank Ashe - Publié sur Amazon.com
Format: Relié
A great book if you have a strong mathematical background. But the question of asset allocation is bedevilled by mathematics which is too strong to support the weak data supplied by the markets in which we invest.

Unless this weak data is properly integrated into the asset allocation process, an area which Meucci spends too little time on, then the users of quantitative procedures will continue to be disappointed.
1 internautes sur 1 ont trouvé ce commentaire utile 
Excellent and refreshing look at risk management and assset allocation 13 août 2012
Par Aleksander B. Hansen - Publié sur Amazon.com
Format: Broché
This is an excellent book which takes a novel and refreshing look at risk management and asset allocation. I both read the book and attended the Advanced Risk and Portfolio Management Bootcamp. The Bootcamp is week long course run by the author each year, and much of the material covered builds heavily on the material and ideas presented in this book.
[It is also worth mentioning that Dr. Meucci runs this during the month of August and that the entirety of the profit is donated to charity.]

Personally, my background is BSc in Econ, MSc in Finance, former PhD fellow in finance, and I currently work in risk management so that is where I'm coming from. Despite my training there was a lot of material presented in this book which was new to me, including seminal ideas I have later benefited from greatly in my own work.

Although this is just speculation, I think the author's background in the disciplines of economics, physics and mathematics allowed him to combine ideas and methodology from all [three] disciplines, thus allowing us to take a second look at the topics of risk and asset allocation through a different lens. Additionally, the author seems to have a very tight connection with both academia and business, thus avoiding some of the pitfalls made by authors from either fields, by making it rigorous and theoretically sound, yet directly applicable in business [as well as academia].

I would perhaps disagree slightly with the prerequisites recommended in the book. True to the Springer series of book, you can expect a fair amount of rigorous mathematics and statistics. Now, that is not necessarily a drawback, and I would recommend that you DO NOT shy away from purchasing the book because you fear your background might not be solid enough.
Rather, I would put it this way: you can read through this book and come away with wealth of new knowledge despite not having a heavy math/stats background, simply because you do not need to understand all the math and statistics in the book as long as you understand the underlying ideas and concepts. I think the author does a good job of separating the two by, e.g. putting derivations in boxes, so that when you see that, you can safely skip it on first reading.
The book also comes complete with exercises, complete with detailed solutions, making it easier to grasp concepts, and get familiar with the material.

What is unique to this book is that it comes with examples and exercises that are already written and solved for you in Matlab. This gives you the opportunity to play around with the concepts, illustrate important examples as well as give you an implementation of the ideas and concepts covered in the book.

I highly recommend this book to anyone working in finance, especially those who work in risk management, asset pricing and portfolio/asset/investment management. Furthermore, I would highly recommend that you visit the authors web-site [...], where you can look at the aforementioned Matlab code, access papers
1 internautes sur 1 ont trouvé ce commentaire utile 
Mathematical, but extremely informative 15 mars 2014
Par E. Lau - Publié sur Amazon.com
Format: Relié Achat vérifié
With a reasonable math background (calculus and an understanding of linear algebra), this book is extremely informative and useful for anyone trying to get into the quantitative finance area. I would recommend reading "A Primer for the Mathematical Financial Engineering"(http://www.amazon.com/Primer-Mathematics-Financial-Engineering-Edition/dp/0979757622) to get a brief overview of the math required in this book prior if you have not had any experience in the mathematical finance area though.
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