Posted by **s13tas** at Feb. 27, 2011

Publisher: Springer | ISBN: 3540222138 | edition 2005 | PDF | 547 pages | 11,76 mb

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.

Posted by **step778** at Sept. 20, 2013

2005 | pages: 546 | ISBN: 3540222138 | PDF | 4,3 mb

Posted by **vijaybbvv** at Nov. 18, 2009

532 pages | Springer (January 11, 2008) | 3540222138 | PDF | 5 MB

This encyclopedic, detailed exposition spans all the steps of one-period allocation from the foundations to the most advanced developments.

Posted by **Mazepa777** at May 8, 2009

Publisher Springer-Verlag Berlin Heidelberg New York | ISBN-10: 3540222138 | edition 2005 | PDF | 547 pages | 11.76 mb

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 symmys.com 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 symmys.com.

Posted by **libr** at April 12, 2017

English | 2014 | ISBN: 1783263083 | ISBN-13: 9781783263080 | 372 pages | PDF | 2,4 MB

Posted by **interes** at July 7, 2015

English | 2011 | ISBN: 0470687258 | 384 pages | PDF | 3,6 MB

Posted by **interes** at July 19, 2014

English | 2008 | ISBN: 0195331915 | 144 pages | PDF | 2 MB

In spite of theoretical benefits, Markowitz mean-variance (MV) optimized portfolios often fail to meet practical investment goals of marketability, usability, and performance, prompting many investors to seek simpler alternatives.

Posted by **interes** at June 25, 2014

English | 2014 | ISBN: 1783263083 | ISBN-13: 9781783263080 | 372 pages | PDF | 2,4 MB

Each financial crisis calls for — by its novelty and the mechanisms it shares with preceding crises — appropriate means to analyze financial risks. In Extreme Financial Risks and Asset Allocation, the authors present in an accessible and timely manner the concepts, methods, and techniques that are essential for an understanding of these risks in an environment where asset prices are subject to sudden, rough, and unpredictable changes.

Posted by **arundhati** at Oct. 15, 2013

2008 | ISBN: 0195331915 | 144 pages | PDF | 4 MB

Posted by **libr** at Sept. 3, 2012

English | 2008-03-03 | ISBN: 0195331915 | 144 pages | PDF | 1,1 MB

In spite of theoretical benefits, Markowitz mean-variance (MV) optimized portfolios often fail to meet practical investment goals of marketability, usability, and performance, prompting many investors to seek simpler alternatives. Financial experts Richard and Robert Michaud demonstrate that the limitations of MV optimization are not the result of conceptual flaws in Markowitz theory but unrealistic representation of investment information. What is missing is a realistic treatment of estimation error in the optimization and rebalancing process.