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Risk and Asset Allocation (Anglais) Broché – 2 juin 2010

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--Ce texte fait référence à l'édition Relié.
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Descriptions du produit

Revue de presse

This exciting new book takes a fresh look at asset allocation and offers up a masterly account of this important subject. The quantitative emphasis and included MATLAB software make it a must-read for the mathematically oriented investment professional. --Peter Carr, Head of Quantitative Research, Bloomberg LP, Director of Masters in Mathematical Finance program, NYU

Meucci s Risk and Asset Allocation is one of those rare books that takes a completely fresh look at a well-studied problem, optimal financial portfolio allocation based on statistically estimated models of risk and expected return. Designed for graduate students or quantitatively oriented asset managers, Meucci provides a sophisticated and integrated treatment, from investment theory, to optimization methods, to statistical analysis of multi-variate return data, through computational implementation of the results. This is rigorous and relevant! --Darrel Duffie, Professor of Graduate Business School, Stanford University

A wonderful book! Mathematically rigorous and yet practical, heavily illustrated with graphs and worked examples, Attilio Meucci has written a comprehensive treatment of asset allocation starting from statistical concepts, covering investment primitives, and leading to portfolio optimization in a Bayesian context with parameter uncertainty. --Bob Litterman, Head of Quantitative Resources, Goldman Sachs Asset Management --Ce texte fait référence à l'édition Relié .

Présentation de l'éditeur

This encyclopedic, detailed exposition spans all the steps of one-period allocation from the foundations to the most advanced developments. Multivariate estimation methods are analyzed in depth, including non-parametric, maximum-likelihood under non-normal hypotheses, shrinkage, robust, and very general Bayesian techniques. Evaluation methods such as stochastic dominance, expected utility, value at risk and coherent measures are thoroughly discussed in a unified setting and applied in a variety of contexts, including prospect theory, total return and benchmark allocation. Portfolio optimization is presented with emphasis on estimation risk, which is tackled by means of Bayesian, resampling and robust optimization techniques. All the statistical and mathematical tools, such as copulas, location-dispersion ellipsoids, matrix-variate distributions, cone programming, are introduced from the basics. Comprehension is supported by a large number of figures and examples, as well as real trading and asset management case studies. At the reader will find freely downloadable complementary materials: the Exercise Book; a set of thoroughly documented MATLAB® applications; and the Technical Appendices with all the proofs. More materials and complete reviews can also be found at --Ce texte fait référence à l'édition Relié .

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Commentaires client les plus utiles sur (beta) 4.3 étoiles sur 5 4 commentaires
24 internautes sur 26 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 outstanding professional resource 24 août 2010
Par NY reviewer - Publié sur
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.
3.0 étoiles sur 5 Historical irony 1 octobre 2016
Par A. J. Sutter - Publié sur
Format: Broché
I recently noticed that when this book was first published (2005), the author was at -- Lehman Brothers, Inc.

I hope the "corrected" edition is REALLY corrected...
28 internautes sur 39 ont trouvé ce commentaire utile 
4.0 étoiles sur 5 Not for the faint-hearted 31 janvier 2007
Par Frank Ashe - Publié sur
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 
5.0 étoiles sur 5 Mathematical, but extremely informative 15 mars 2014
Par E. Lau - Publié sur
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"( 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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