Risk and Asset Allocation (Anglais) Broché – 20 mai 2009
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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 .
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Commentaires client les plus utiles sur Amazon.com (beta)
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.
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.
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