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Robust Mean Variance Portfolio Selection
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Book Synopsis Robust Mean-Variance Portfolio Selection by : Cédric Perret-Gentil
Download or read book Robust Mean-Variance Portfolio Selection written by Cédric Perret-Gentil and published by . This book was released on 2007 with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper investigates model risk issues in the context of mean-variance portfolio selection. We analytically and numerically show that, under model misspecification, the use of statistically robust estimates instead of the widely used classical sample mean and covariance is highly beneficial for the stability properties of the mean-variance optimal portfolios. Moreover, we perform simulations leading to the conclusion that, under classical estimation, model risk bias dominates estimation risk bias. Finally, we suggest a diagnostic tool to warn the analyst of the presence of extreme returns that have an abnormally large influence on the optimization results.
Book Synopsis Robust Mean-variance Portfolio Selection by : Cedric Perret-Gentil
Download or read book Robust Mean-variance Portfolio Selection written by Cedric Perret-Gentil and published by . This book was released on 2003 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Robust Equity Portfolio Management by : Woo Chang Kim
Download or read book Robust Equity Portfolio Management written by Woo Chang Kim and published by John Wiley & Sons. This book was released on 2015-11-25 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive portfolio optimization guide, with provided MATLAB code Robust Equity Portfolio Management + Website offers the most comprehensive coverage available in this burgeoning field. Beginning with the fundamentals before moving into advanced techniques, this book provides useful coverage for both beginners and advanced readers. MATLAB code is provided to allow readers of all levels to begin implementing robust models immediately, with detailed explanations and applications in the equity market included to help you grasp the real-world use of each technique. The discussion includes the most up-to-date thinking and cutting-edge methods, including a much-needed alternative to the traditional Markowitz mean-variance model. Unparalleled in depth and breadth, this book is an invaluable reference for all risk managers, portfolio managers, and analysts. Portfolio construction models originating from the standard Markowitz mean-variance model have a high input sensitivity that threatens optimization, spawning a flurry of research into new analytic techniques. This book covers the latest developments along with the basics, to give you a truly comprehensive understanding backed by a robust, practical skill set. Get up to speed on the latest developments in portfolio optimization Implement robust models using provided MATLAB code Learn advanced optimization methods with equity portfolio applications Understand the formulations, performances, and properties of robust portfolios The Markowitz mean-variance model remains the standard framework for portfolio optimization, but the interest in—and need for—an alternative is rapidly increasing. Resolving the sensitivity issue and dramatically reducing portfolio risk is a major focus of today's portfolio manager. Robust Equity Portfolio Management + Website provides a viable alternative framework, and the hard skills to implement any optimization method.
Book Synopsis Robust Portfolio Optimization and Management by : Frank J. Fabozzi
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
Book Synopsis Portfolio Selection With Robust Estimation by : Victor DeMiguel
Download or read book Portfolio Selection With Robust Estimation written by Victor DeMiguel and published by . This book was released on 2007 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mean-variance portfolios constructed using the sample mean and covariance matrix of asset returns perform poorly out-of-sample due to estimation error. Moreover, it is commonly accepted that estimation error in the sample mean is much larger than in the sample covariance matrix. For this reason, practitioners and researchers have recently focused on the minimum-variance portfolio, which relies solely on estimates of the covariance matrix, and thus, usually performs better out-of-sample. But even the minimum-variance portfolios are quite sensitive to estimation error and have unstable weights that fluctuate substantially over time. In this paper, we propose a class of portfolios that have better stability properties than the traditional minimum-variance portfolios. The proposed portfolios are constructed using certain robust estimators and can be computed by solving a single nonlinear program, where robust estimation and portfolio optimization are performed in a single step. We show analytically that the resulting portfolio weights are less sensitive to changes in the asset-return distribution than those of the traditional minimum-variance portfolios. Moreover, our numerical results on simulated and empirical data confirm that the proposed portfolios are more stable than the traditional minimum-variance portfolios, while preserving (or slightly improving) their relatively good out-of-sample performance.
Book Synopsis Robust Mean-Variance Portfolio Selection with State-Dependent Ambiguity Aversion and Risk Aversion by : Bingyan Han
Download or read book Robust Mean-Variance Portfolio Selection with State-Dependent Ambiguity Aversion and Risk Aversion written by Bingyan Han and published by . This book was released on 2019 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper studies a class of robust mean-variance portfolio selection problems with state-dependent risk aversion. Model uncertainty, in the sense of considering alternative dominated models, is introduced to the problem to reflect the investor's ambiguity aversion. To characterize the robust portfolios, we consider closed-loop equilibrium control and spike variation approaches. Moreover, we show that the closed-loop equilibrium strategy exists and is unique under some technical conditions. That partially addresses the open problem left in Björk et al. (2017, Finance Stoch.) and Pun (2018, Automatica). By using the necessary and sufficient condition for the equilibrium, we manage to derive the analytical form of the equilibrium strategy via the unique solution to a nonlinear ordinary differential equation system. To validate the proposed closed-loop framework, we show that when there is no ambiguity, our equilibrium strategy is reduced to the strategy in Björk et al. (2014, Math. Finance), which cannot be deduced under the open-loop control framework.
Book Synopsis On the Robustness and Sparsity Trade-Off in Mean-Variance Portfolio Selection by : Yufei Yang
Download or read book On the Robustness and Sparsity Trade-Off in Mean-Variance Portfolio Selection written by Yufei Yang and published by . This book was released on 2017 with total page 26 pages. Available in PDF, EPUB and Kindle. Book excerpt: A well-managed portfolio is crucial to an investor's success. Robustness against parameter uncertainty and low trading costs are two desired properties when constructing a portfolio. Robust optimization techniques have been applied to improve the stability of a portfolio under parameter uncertainty. However, portfolios generated from robust procedures often suffer from being over-diversified. Hence, an investor has to hold a multitude of assets and pay a large amount of transaction costs. In this paper, we extend the classical mean-variance framework by incorporating an ellipsoidal uncertainty set and fixed transaction costs which penalize an over-diversified portfolio and promote sparsity. We explore several properties of the optimal portfolio under this model. In particular, we show that it can be approximated by a linear combination of three benchmark portfolios, including the mean-variance portfolio, the minimum-variance portfolio, and a fixed transaction cost induced portfolio. Moreover, we explicitly characterize how the number of traded assets changes by a sensitivity analysis. Our analytical results could help investors to maintain an appropriate trade-off between robustness and sparsity and thus lead to a quantitative interpretation of the so-called diversification paradox.
Book Synopsis Robust Portfolio Optimization and Management by : Frank J. Fabozzi
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
Book Synopsis Robust Portfolio Optimization with Multiple Experts by : Frank Lutgens
Download or read book Robust Portfolio Optimization with Multiple Experts written by Frank Lutgens and published by . This book was released on 2008 with total page 55 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider mean-variance portfolio choice of a robust investor. The investor receives advice from J experts, each with a different prior for expected returns and risk. Given this advice the investor follows a min-max portfolio strategy. We study the structure of the robust mean-variance portfolio and compare its performance with a variety of alternative portfolio strategies. We find that the robust investor combines the estimates from the different experts. When experts agree on the main factors that generate returns, the robust investor relies on the advice of the expert with the strongest prior. Dispersed advice leads the investor to combine alternative estimates. The investor is likely to outperfrom alternative strategies. The theoretical analysis is supported by numerical simulations for the 25 Fama-French portfolios and for 81 European country and value portfolios.
Book Synopsis Insights Into Robust Portfolio Optimization by : Romain Perchet
Download or read book Insights Into Robust Portfolio Optimization written by Romain Perchet and published by . This book was released on 2015 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: For a number of different formulations of robust portfolio optimization, quadratic and absolute, we show that a) in the limit of low uncertainty in estimated asset mean returns the robust portfolio converges towards the mean-variance portfolio obtained with the same inputs; and b) in the limit of high uncertainty the robust portfolio converges towards a risk-based portfolio, which is a function of how the uncertainty in estimated asset mean returns is defined. We give examples in which the robust portfolio converges toward the minimum variance, the inverse variance, the equal-risk budget and the equally weighted portfolio in the limit of sufficiently large uncertainty in asset mean returns. At intermediate levels of uncertainty we find that a weighted average of the mean-variance portfolio and the respective limiting risk-based portfolio offer a good representation of the robust portfolio, in particular in the case of the quadratic formulation. The results remain valid even in the presence of portfolio constraints, in which case the limiting portfolios are the corresponding constrained mean-variance and constrained risk-based portfolios. We believe our results are important in particular for risk-based investors who wish to take into account expected returns to gently tilt away from their current allocations, e.g. risk parity or minimum variance.
Book Synopsis The Mathematics of Financial Modeling and Investment Management by : Sergio M. Focardi
Download or read book The Mathematics of Financial Modeling and Investment Management written by Sergio M. Focardi and published by John Wiley & Sons. This book was released on 2004-04-12 with total page 802 pages. Available in PDF, EPUB and Kindle. Book excerpt: the mathematics of financial modeling & investment management The Mathematics of Financial Modeling & Investment Management covers a wide range of technical topics in mathematics and finance-enabling the investment management practitioner, researcher, or student to fully understand the process of financial decision-making and its economic foundations. This comprehensive resource will introduce you to key mathematical techniques-matrix algebra, calculus, ordinary differential equations, probability theory, stochastic calculus, time series analysis, optimization-as well as show you how these techniques are successfully implemented in the world of modern finance. Special emphasis is placed on the new mathematical tools that allow a deeper understanding of financial econometrics and financial economics. Recent advances in financial econometrics, such as tools for estimating and representing the tails of the distributions, the analysis of correlation phenomena, and dimensionality reduction through factor analysis and cointegration are discussed in depth. Using a wealth of real-world examples, Focardi and Fabozzi simultaneously show both the mathematical techniques and the areas in finance where these techniques are applied. They also cover a variety of useful financial applications, such as: * Arbitrage pricing * Interest rate modeling * Derivative pricing * Credit risk modeling * Equity and bond portfolio management * Risk management * And much more Filled with in-depth insight and expert advice, The Mathematics of Financial Modeling & Investment Management clearly ties together financial theory and mathematical techniques.
Book Synopsis Efficient Asset Management by : Richard O. Michaud
Download or read book Efficient Asset Management written by Richard O. Michaud and published by Oxford University Press. This book was released on 2008-03-03 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. The text provides a non-technical review of classical Markowitz optimization and traditional objections. The authors demonstrate that in practice the single most important limitation of MV optimization is oversensitivity to estimation error. Portfolio optimization requires a modern statistical perspective. Efficient Asset Management, Second Edition uses Monte Carlo resampling to address information uncertainty and define Resampled Efficiency (RE) technology. RE optimized portfolios represent a new definition of portfolio optimality that is more investment intuitive, robust, and provably investment effective. RE rebalancing provides the first rigorous portfolio trading, monitoring, and asset importance rules, avoiding widespread ad hoc methods in current practice. The Second Edition resolves several open issues and misunderstandings that have emerged since the original edition. The new edition includes new proofs of effectiveness, substantial revisions of statistical estimation, extensive discussion of long-short optimization, and new tools for dealing with estimation error in applications and enhancing computational efficiency. RE optimization is shown to be a Bayesian-based generalization and enhancement of Markowitz's solution. RE technology corrects many current practices that may adversely impact the investment value of trillions of dollars under current asset management. RE optimization technology may also be useful in other financial optimizations and more generally in multivariate estimation contexts of information uncertainty with Bayesian linear constraints. Michaud and Michaud's new book includes numerous additional proposals to enhance investment value including Stein and Bayesian methods for improved input estimation, the use of portfolio priors, and an economic perspective for asset-liability optimization. Applications include investment policy, asset allocation, and equity portfolio optimization. A simple global asset allocation problem illustrates portfolio optimization techniques. A final chapter includes practical advice for avoiding simple portfolio design errors. With its important implications for investment practice, Efficient Asset Management 's highly intuitive yet rigorous approach to defining optimal portfolios will appeal to investment management executives, consultants, brokers, and anyone seeking to stay abreast of current investment technology. Through practical examples and illustrations, Michaud and Michaud update the practice of optimization for modern investment management.
Book Synopsis Mean-Variance Analysis in Portfolio Choice and Capital Markets by : Harry M. Markowitz
Download or read book Mean-Variance Analysis in Portfolio Choice and Capital Markets written by Harry M. Markowitz and published by John Wiley & Sons. This book was released on 2000-02-15 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: In 1952, Harry Markowitz published "Portfolio Selection," a paper which revolutionized modern investment theory and practice. The paper proposed that, in selecting investments, the investor should consider both expected return and variability of return on the portfolio as a whole. Portfolios that minimized variance for a given expected return were demonstrated to be the most efficient. Markowitz formulated the full solution of the general mean-variance efficient set problem in 1956 and presented it in the appendix to his 1959 book, Portfolio Selection. Though certain special cases of the general model have become widely known, both in academia and among managers of large institutional portfolios, the characteristics of the general solution were not presented in finance books for students at any level. And although the results of the general solution are used in a few advanced portfolio optimization programs, the solution to the general problem should not be seen merely as a computing procedure. It is a body of propositions and formulas concerning the shapes and properties of mean-variance efficient sets with implications for financial theory and practice beyond those of widely known cases. The purpose of the present book, originally published in 1987, is to present a comprehensive and accessible account of the general mean-variance portfolio analysis, and to illustrate its usefulness in the practice of portfolio management and the theory of capital markets. The portfolio selection program in Part IV of the 1987 edition has been updated and contains exercises and solutions.
Book Synopsis A Robust Bayesian Approach to Portfolio Selection by :
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.
Book Synopsis Encyclopedia of Financial Models by : Frank J. Fabozzi
Download or read book Encyclopedia of Financial Models written by Frank J. Fabozzi and published by John Wiley & Sons. This book was released on 2012-10-15 with total page 3180 pages. Available in PDF, EPUB and Kindle. Book excerpt: An essential reference dedicated to a wide array of financial models, issues in financial modeling, and mathematical and statistical tools for financial modeling The need for serious coverage of financial modeling has never been greater, especially with the size, diversity, and efficiency of modern capital markets. With this in mind, the Encyclopedia of Financial Models, 3 Volume Set has been created to help a broad spectrum of individuals—ranging from finance professionals to academics and students—understand financial modeling and make use of the various models currently available. Incorporating timely research and in-depth analysis, the Encyclopedia of Financial Models is an informative 3-Volume Set that covers both established and cutting-edge models and discusses their real-world applications. Edited by Frank Fabozzi, this set includes contributions from global financial experts as well as academics with extensive consulting experience in this field. Organized alphabetically by category, this reliable resource consists of three separate volumes and 127 entries—touching on everything from asset pricing and bond valuation models to trading cost models and volatility—and provides readers with a balanced understanding of today's dynamic world of financial modeling. Frank Fabozzi follows up his successful Handbook of Finance with another major reference work, The Encyclopedia of Financial Models Covers the two major topical areas: asset valuation for cash and derivative instruments, and portfolio modeling Fabozzi explores the critical background tools from mathematics, probability theory, statistics, and operations research needed to understand these complex models Organized alphabetically by category, this book gives readers easy and quick access to specific topics sorted by an applicable category among them Asset Allocation, Credit Risk Modeling, Statistical Tools 3 Volumes onlinelibrary.wiley.com Financial models have become increasingly commonplace, as well as complex. They are essential in a wide range of financial endeavors, and this 3-Volume Set will help put them in perspective.
Book Synopsis Online Portfolio Selection by : Bin Li
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.
Book Synopsis Portfolio and Investment Analysis with SAS by : John B. Guerard
Download or read book Portfolio and Investment Analysis with SAS written by John B. Guerard and published by SAS Institute. This book was released on 2019-04-03 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: Choose statistically significant stock selection models using SAS® Portfolio and Investment Analysis with SAS®: Financial Modeling Techniques for Optimization is an introduction to using SAS to choose statistically significant stock selection models, create mean-variance efficient portfolios, and aggressively invest to maximize the geometric mean. Based on the pioneering portfolio selection techniques of Harry Markowitz and others, this book shows that maximizing the geometric mean maximizes the utility of final wealth. The authors draw on decades of experience as teachers and practitioners of financial modeling to bridge the gap between theory and application. Using real-world data, the book illustrates the concept of risk-return analysis and explains why intelligent investors prefer stocks over bonds. The authors first explain how to build expected return models based on expected earnings data, valuation ratios, and past stock price performance using PROC ROBUSTREG. They then show how to construct and manage portfolios by combining the expected return and risk models. Finally, readers learn how to perform hypothesis testing using Bayesian methods to add confidence when data mining from large financial databases.
