Download or read book Improving Portfolios Global Performance with Robust Covariance Matrix Estimation written by Jay Emmanuelle and published by . This book was released on 2018 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper presents how the most recent improvements made on covariance matrix estimation and model order selection can be applied to the portfolio optimisation problem. The particular case of the Maximum Variety Portfolio is treated but the same improvements apply also in the other optimisation problems such as the Minimum Variance Portfolio. We assume that the most important information (or the latent factors) are embedded in correlated Elliptical Symmetric noise extending classical Gaussian assumptions. We propose here to focus on a recent method of model order selection allowing to efficiently estimate the subspace of main factors describing the market. This non-standard model order selection problem is solved through Random Matrix Theory and robust covariance matrix estimation. The proposed procedure will be explained through synthetic data and be applied and compared with standard techniques on real market data showing promising improvements.

Download or read book How Robust Is Robust Covariance Evidence from International Portfolio Selection written by Tsung-Wu Ho and published by . This book was released on 2015 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper investigates whether the use of robust covariance improves portfolio performance and, in the presence of uncertainty, whether the 1/N strategy is as good as you think. In addition to sample covariance, we use a battery of robust covariance matrix. Our empirical evidence has two findings: First, the range of in-sample estimation horizon and out-of-sample holding period matter the most; secondly, generally, assets selected by robust covariance does not matter, the only exception is covariance estimated by multivariate t distribution Although the 1/N strategy is as optimal as the literature suggests, it does not cover all assets, yet n assets selected out of N by certain strategy perform better. Whether the out-of-sample holding period is set to be 1 and 3 months, our empirical illustration shows that: n assets selected from 90-day estimation window performs best, portfolio with 1/n weight consistently outperforms that with pre-optimized weights. As a result, robust investment strategy matters the most, rather than robust covariance. The factors that affect covariance estimator is not outliers, but the one that balance information.

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.

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.

Download or read book Adaptive Asset Allocation written by Adam Butler and published by John Wiley & Sons. This book was released on 2016-02-02 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build an agile, responsive portfolio with a new approach to global asset allocation Adaptive Asset Allocation is a no-nonsense how-to guide for dynamic portfolio management. Written by the team behind Gestaltu.com, this book walks you through a uniquely objective and unbiased investment philosophy and provides clear guidelines for execution. From foundational concepts and timing to forecasting and portfolio optimization, this book shares insightful perspective on portfolio adaptation that can improve any investment strategy. Accessible explanations of both classical and contemporary research support the methodologies presented, bolstered by the authors' own capstone case study showing the direct impact of this approach on the individual investor. Financial advisors are competing in an increasingly commoditized environment, with the added burden of two substantial bear markets in the last 15 years. This book presents a framework that addresses the major challenges both advisors and investors face, emphasizing the importance of an agile, globally-diversified portfolio. Drill down to the most important concepts in wealth management Optimize portfolio performance with careful timing of savings and withdrawals Forecast returns 80% more accurately than assuming long-term averages Adopt an investment framework for stability, growth, and maximum income An optimized portfolio must be structured in a way that allows quick response to changes in asset class risks and relationships, and the flexibility to continually adapt to market changes. To execute such an ambitious strategy, it is essential to have a strong grasp of foundational wealth management concepts, a reliable system of forecasting, and a clear understanding of the merits of individual investment methods. Adaptive Asset Allocation provides critical background information alongside a streamlined framework for improving portfolio performance.

Download or read book High Dimensional Covariance Estimation written by Mohsen Pourahmadi and published by John Wiley & Sons. This book was released on 2013-06-24 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences. High-Dimensional Covariance Estimation provides accessible and comprehensive coverage of the classical and modern approaches for estimating covariance matrices as well as their applications to the rapidly developing areas lying at the intersection of statistics and machine learning. Recently, the classical sample covariance methodologies have been modified and improved upon to meet the needs of statisticians and researchers dealing with large correlated datasets. High-Dimensional Covariance Estimation focuses on the methodologies based on shrinkage, thresholding, and penalized likelihood with applications to Gaussian graphical models, prediction, and mean-variance portfolio management. The book relies heavily on regression-based ideas and interpretations to connect and unify many existing methods and algorithms for the task. High-Dimensional Covariance Estimation features chapters on: Data, Sparsity, and Regularization Regularizing the Eigenstructure Banding, Tapering, and Thresholding Covariance Matrices Sparse Gaussian Graphical Models Multivariate Regression The book is an ideal resource for researchers in statistics, mathematics, business and economics, computer sciences, and engineering, as well as a useful text or supplement for graduate-level courses in multivariate analysis, covariance estimation, statistical learning, and high-dimensional data analysis.

Download or read book Robust Equity Portfolio Management written by Woo Chang Kim and published by John Wiley & Sons. This book was released on 2015-11-30 with total page 259 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.

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 Portfolio Diversification and the Use of Shrinking written by Maja Kuhnell Frederiksen and published by . This book was released on 2008 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: An examination of the performance of the improved estimator of the covariance matrix was conducted on the Danish stock market, the UK stock market and the German stock market. The test results of the Danish and German stock markets revealed a very bad performance of the improved estimator. Even though the levels of the shrinkage intensities were very much in accordance with previous research conducted on the US stock market by Ledoit and Wolf (2003) the improved estimator failed to perform better than the estimated covariance matrix based on Sharpe's (1963) single index model. The test results of the UK stock market were in favor of the improved estimator. The shrinkage intensity yielded an investment strategy that was significantly less volatile than similar strategies based on the traditional sample covariance matrix by Markowitz's (1959) and the single index model. Furthermore, the shrinkage intensity was found to be rather stable over time whereas the shrinkage intensities of the Danish market tended to decrease over time and shrinkage intensities of the German market showed indications of an upward sloping trend. A benchmark test conducted on a small sample of UK stocks showed that the performance of the improved method weakens when the sample size decreases. The weak performance of the improved estimator of Danish and German stock portfolios may therefore have been caused from using too small samples of stocks in the estimation process. Finally the results of the German market may indicate that the improved covariance matrix estimator is sensitive to thin trading.

Download or read book High Dimensional Covariance Matrix Estimation written by Aygul Zagidullina and published by Springer Nature. This book was released on 2021-10-29 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents covariance matrix estimation and related aspects of random matrix theory. It focuses on the sample covariance matrix estimator and provides a holistic description of its properties under two asymptotic regimes: the traditional one, and the high-dimensional regime that better fits the big data context. It draws attention to the deficiencies of standard statistical tools when used in the high-dimensional setting, and introduces the basic concepts and major results related to spectral statistics and random matrix theory under high-dimensional asymptotics in an understandable and reader-friendly way. The aim of this book is to inspire applied statisticians, econometricians, and machine learning practitioners who analyze high-dimensional data to apply the recent developments in their work.

Download or read book Sample Eigenvalues Adjustment for Portfolio Performance Improvement Under Factor Models written by Danqiao Guo and published by . This book was released on 2018 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: We identify a few sample eigenvalues adjustment patterns that lead to an improvement in the out-of-sample portfolio Sharpe ratio when the population covariance matrix admits a high-dimensional factor model. These patterns unveil the key to portfolio performance improvement and shed light on the effectiveness of a few well-known covariance matrix estimation methods which were not designed to improve the out-of-sample portfolio performance.

Download or read book Estimating a Covariance Matrix for Market Risk Management and the Case of Credit Default Swaps written by Richard Neuberg and published by . This book was released on 2018 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt: We analyze covariance matrix estimation from the perspective of market risk management, where the goal is to obtain accurate estimates of portfolio risk across essentially all portfolios--even those with small standard deviations. We propose a simple but effective visualization tool to assess bias across a wide range of portfolios. We employ a portfolio perspective to determine covariance matrix loss functions particularly suitable for market risk management. Proper regularization of the covariance matrix estimate significantly improves performance. These methods are applied to credit default swaps, for which covariance matrices are used to set portfolio margin requirements for central clearing. Among the methods we test, the graphical lasso estimator performs particularly well. The graphical lasso and a hierarchical clustering estimator also yield economically meaningful representations of market structure through a graphical model and a hierarchy, respectively.

Download or read book On Portfolio Selection written by and published by . This book was released on 2002 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Oxford Handbook of Random Matrix Theory written by Gernot Akemann and published by Oxford Handbooks. This book was released on 2015-08-09 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With a foreword by Freeman Dyson, the handbook brings together leading mathematicians and physicists to offer a comprehensive overview of random matrix theory, including a guide to new developments and the diverse range of applications of this approach.In part one, all modern and classical techniques of solving random matrix models are explored, including orthogonal polynomials, exact replicas or supersymmetry.

Download or read book Sparse Optimization Methods and Statistical Modeling with Applications to Finance written by Michael Ho and published by . This book was released on 2016 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is well known that the out-of-sample performance of Markowitz's mean-variance portfolio criterion can be negatively affected by estimation errors in the mean and covariance. In this dissertation we examine methods to address this problem through application of methods and techniques from sparse optimization and modeling. Two new techniques are developed with the aim of improving the performance of mean-variance portfolio optimization.In the first technique a pairwise weighted elastic net penalized mean-variance criterion for portfolio design in proposed. Here we motivate the use of this penalty through a robust optimization interpretation. This interpretation is then employed to develop a bootstrap calibration technique for the pairwise elastic net. The benefit of the pairwise weighted elastic net and calibration is shown in portfolio performance results using recent U.S. stock market data.In the second application robust Kalman filtering techniques are applied to return covariance estimation from high frequency financial price data. The methods developed address three factors which make covariance estimation from high frequency data difficult: 1) microstructure noise, 2) asynchronous trading, and 3) jumps. The performance of these robust Kalman filtering techniques are tested against simulated high frequency data and are compared with other existing covariance estimators. The results indicate that the robust Kalman filtering techniques substantially improve covariance estimation performance versus other approaches.

Download or read book Improving Portfolio Allocation Through Covariance Matrix Filtering written by Daron Golden and published by . This book was released on 2017 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: The sample covariance matrix is known to contain substantial statistical noise, making it inappropriate for use in financial decision making. Leading researchers have proposed various filtering methods that attempt to reduce the level of noise in the covariance matrix estimator. In most cases, these methods can be interpreted by analysing how they adjust the eigen-structure of the sample correlation matrix. This paper compares the filtering methods using a theoretical eigen-framework as well as a practical South African experiment. By focussing on the eigen-structure, the sources of statistical noise are identified. The sample correlation matrix suffers from excess dispersion in its eigenvalues and excess dispersion in its pairwise correlations. Bayesian shrinkage estimators that effectively remove the excess dispersion provide superior performance in terms of out-of-sample portfolio risk and turnover. Specifically, the optimal filtering method is a blend between the sample covariance matrix, its diagonal elements and the covariance matrix based on the constant correlation model.

Download or read book Advanced Derivatives Pricing and Risk Management written by Claudio Albanese and published by Academic Press. This book was released on 2006 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: Book and CDROM include the important topics and cutting-edge research in financial derivatives and risk management.