Download or read book A Robust Bayesian Approach to Portfolio Selection written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis aims at studying the local robustness properties of Bayesian posterior summaries and deriving a robust procedure to estimate Bayesian Mean-Variance weights in a portfolio selection problem. In the first part, we study the local robustness of Bayesian estimators. In particular, we build a framework wherein any Bayesian quantity can be seen as a posterior functional. In this way it becomes possible to construct different robustness measures. We derive local influence measures for posterior summaries with respect both to prior and sampling distributions and to observations. Then we address the issue of efficient implementation of the derived measures through MCMC algorithms. In the second part, we deal with the problem of robust estimation in a Bayesian context, providing a useful result to generalize univariate robust distributions to the multivariate case. We also propose criteria to assess in which cases a robust model is recommended and how to choose among estimates obtained with different distributions. Finally, we consider in the third part the Mean-Variance portfolio selection problem. We provide evidence that if the data are normally distributed the Bayesian approach works better than the Certainty Equivalence approach, nevertheless this is no longer true when the data contain few outlying observations. Moreover, we compute useful measures of sensitivity of Bayesian weights and we construct and implement a new estimator which is robust with respect to the presence of 'extreme' observations.
Download or read book A Bayesian Approach to Portfolio Selection and Revision written by Robert L. Winkler and published by . This book was released on 1973 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robust Bayesian Analysis written by David Rios Insua and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust Bayesian analysis aims at overcoming the traditional objection to Bayesian analysis of its dependence on subjective inputs, mainly the prior and the loss. Its purpose is the determination of the impact of the inputs to a Bayesian analysis (the prior, the loss and the model) on its output when the inputs range in certain classes. If the impact is considerable, there is sensitivity and we should attempt to further refine the information the incumbent classes available, perhaps through additional constraints on and/ or obtaining additional data; if the impact is not important, robustness holds and no further analysis and refinement would be required. Robust Bayesian analysis has been widely accepted by Bayesian statisticians; for a while it was even a main research topic in the field. However, to a great extent, their impact is yet to be seen in applied settings. This volume, therefore, presents an overview of the current state of robust Bayesian methods and their applications and identifies topics of further in terest in the area. The papers in the volume are divided into nine parts covering the main aspects of the field. The first one provides an overview of Bayesian robustness at a non-technical level. The paper in Part II con cerns foundational aspects and describes decision-theoretical axiomatisa tions leading to the robust Bayesian paradigm, motivating reasons for which robust analysis is practically unavoidable within Bayesian analysis.
Download or read book Does a Bayesian Approach Generate Robust Forecasts Evidence from Applications in Portfolio Investment Decisions written by Chih-Ling Tsai and published by . This book was released on 2009 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: We employ a statistical criterion (out-of-sample hit rate) and a financial market measure (portfolio performance) to compare the forecasting accuracy of three model selection approaches: Bayesian information criterion (BIC), model averaging, and model mixing. While the more recent approaches of model averaging and model mixing surpass the Bayesian information criterion in their out-of-sample hit rates, the predicted portfolios from these new approaches do not significantly outperform the portfolio obtained via the BIC subset selection method.
Download or read book Robust Bayesian Portfolio Optimisation written by Byron Wilson and published by . This book was released on 2011 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Modern Portfolio Optimization with NuOPTTM S PLUS and S BayesTM written by Bernd Scherer and published by Springer Science & Business Media. This book was released on 2007-09-05 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years portfolio optimization and construction methodologies have become an increasingly critical ingredient of asset and fund management, while at the same time portfolio risk assessment has become an essential ingredient in risk management. This trend will only accelerate in the coming years. This practical handbook fills the gap between current university instruction and current industry practice. It provides a comprehensive computationally-oriented treatment of modern portfolio optimization and construction methods using the powerful NUOPT for S-PLUS optimizer.
Download or read book Bayesian Methods in Finance written by Svetlozar T. Rachev and published by John Wiley & Sons. This book was released on 2008-02-13 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Methods in Finance provides a detailed overview of the theory of Bayesian methods and explains their real-world applications to financial modeling. While the principles and concepts explained throughout the book can be used in financial modeling and decision making in general, the authors focus on portfolio management and market risk management—since these are the areas in finance where Bayesian methods have had the greatest penetration to date.
Download or read book Robust Portfolio Optimization and Management written by Frank J. Fabozzi and published by John Wiley & Sons. This book was released on 2007-04-27 with total page 513 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book High Performance Optimization written by Hans Frenk and published by Springer Science & Business Media. This book was released on 2000 with total page 506 pages. Available in PDF, EPUB and Kindle. Book excerpt: For a long time the techniques of solving linear optimization (LP) problems improved only marginally. Fifteen years ago, however, a revolutionary discovery changed everything. A new `golden age' for optimization started, which is continuing up to the current time. What is the cause of the excitement? Techniques of linear programming formed previously an isolated body of knowledge. Then suddenly a tunnel was built linking it with a rich and promising land, part of which was already cultivated, part of which was completely unexplored. These revolutionary new techniques are now applied to solve conic linear problems. This makes it possible to model and solve large classes of essentially nonlinear optimization problems as efficiently as LP problems. This volume gives an overview of the latest developments of such `High Performance Optimization Techniques'. The first part is a thorough treatment of interior point methods for semidefinite programming problems. The second part reviews today's most exciting research topics and results in the area of convex optimization. Audience: This volume is for graduate students and researchers who are interested in modern optimization techniques.
Download or read book Robust Portfolio Optimization and Management written by Frank J. Fabozzi and published by John Wiley & Sons. This book was released on 2007-06-04 with total page 517 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Online Portfolio Selection written by Bin Li and published by CRC Press. This book was released on 2018-10-30 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the aim to sequentially determine optimal allocations across a set of assets, Online Portfolio Selection (OLPS) has significantly reshaped the financial investment landscape. Online Portfolio Selection: Principles and Algorithms supplies a comprehensive survey of existing OLPS principles and presents a collection of innovative strategies that leverage machine learning techniques for financial investment. The book presents four new algorithms based on machine learning techniques that were designed by the authors, as well as a new back-test system they developed for evaluating trading strategy effectiveness. The book uses simulations with real market data to illustrate the trading strategies in action and to provide readers with the confidence to deploy the strategies themselves. The book is presented in five sections that: Introduce OLPS and formulate OLPS as a sequential decision task Present key OLPS principles, including benchmarks, follow the winner, follow the loser, pattern matching, and meta-learning Detail four innovative OLPS algorithms based on cutting-edge machine learning techniques Provide a toolbox for evaluating the OLPS algorithms and present empirical studies comparing the proposed algorithms with the state of the art Investigate possible future directions Complete with a back-test system that uses historical data to evaluate the performance of trading strategies, as well as MATLAB® code for the back-test systems, this book is an ideal resource for graduate students in finance, computer science, and statistics. It is also suitable for researchers and engineers interested in computational investment. Readers are encouraged to visit the authors’ website for updates: http://olps.stevenhoi.org.
Download or read book Portfolio Selection with Parameter and Model Uncertainty written by Lorenzo Garlappi and published by . This book was released on 2005 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Optimal Portfolio Choice Under Uncertainty written by Stephen J. Brown and published by . This book was released on 1976 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Optimal portfolio choice under uncertainty written by Stephen Jeffery Brown and published by . This book was released on 1976 with total page 422 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Fully Bayesian Approach to Financial Forecasting and Portfolio Selection written by Andrew Simpson and published by . This book was released on 2002 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robust Bayesian Analysis of Selection Models written by M. J. Bayarri and published by . This book was released on 1993 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Applied Computer Sciences in Engineering written by Juan Carlos Figueroa-García and published by Springer Nature. This book was released on with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt:
