Sample Eigenvalues Adjustment For Portfolio Performance Improvement Under Factor Models
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| Author | : Danqiao Guo |
| Publisher | : |
| Total Pages | : 32 |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
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.
| Author | : Danqiao Guo |
| Publisher | : |
| Total Pages | : 166 |
| Release | : 2019 |
| Genre | : Portfolio management |
| ISBN | : |
The thesis is written in response to emerging issues brought about by an increasing number of assets allocated in a portfolio and seeks answers to puzzling empirical findings in the portfolio management area. Over the years, researchers and practitioners working in the portfolio optimization area have been concerned with estimation errors in the first two moments of asset returns. The thesis comprises several related chapters on our statistical inquiry into this subject. Chapter 1 of the thesis contains an introduction to what will be reported in the remaining chapters. A few well-known covariance matrix estimation methods in the literature involve adjustment of sample eigenvalues. Chapter 2 of the thesis examines the effects of sample eigenvalue adjustment on the out-of-sample performance of a portfolio constructed from the sample covariance matrix. We identify a few sample eigenvalue adjustment patterns that lead to a definite improvement in the out-of-sample portfolio Sharpe ratio when the true covariance matrix admits a high-dimensional factor model. Chapter 3 shows that even when the covariance matrix is poorly estimated, it is still possible to obtain a robust maximum Sharpe ratio (MSR) portfolio by exploiting the uneven distribution of estimation errors across principal components. This is accomplished by approximating the vector of expected future asset returns using a few relatively accurate sample principal components. We discuss two approximation methods. The first method leads to a subtle connection to existing approaches in the literature, while the second one named the ``spectral selection method" is novel and able to address main shortcomings of existing methods in the literature. A few academic studies report an unsatisfactory performance of the optimized portfolios relative to that of the 1/N portfolio. Chapter 4 of the thesis reports an in-depth investigation into the reasons behind the reported superior performance of the 1/N portfolio. It is supported by both theoretical and empirical evidence that the success of the 1/N portfolio is by no means due to the failure of the portfolio optimization theory. Instead, a major reason behind the superiority of the 1/N portfolio is its adjacency to the mean-variance optimal portfolio. Chapter 5 examines the performance of randomized 1/N stock portfolios over time. During the last four decades these portfolios outperformed the market. The construction of these portfolios implies that their constituent stocks are in general older than those in the market as a whole. We show that the differential performance can be explained by the relation between stock returns and firm age. We document a significant relation between age and returns in the US stock market. Since 1977 stock returns have been an increasing function of age apart from the oldest ages. For this period the age effect completely dominates the size effect.
| Author | : Gregory Connor |
| Publisher | : Princeton University Press |
| Total Pages | : 400 |
| Release | : 2010-03-15 |
| Genre | : Business & Economics |
| ISBN | : 1400835291 |
Portfolio risk forecasting has been and continues to be an active research field for both academics and practitioners. Almost all institutional investment management firms use quantitative models for their portfolio forecasting, and researchers have explored models' econometric foundations, relative performance, and implications for capital market behavior and asset pricing equilibrium. Portfolio Risk Analysis provides an insightful and thorough overview of financial risk modeling, with an emphasis on practical applications, empirical reality, and historical perspective. Beginning with mean-variance analysis and the capital asset pricing model, the authors give a comprehensive and detailed account of factor models, which are the key to successful risk analysis in every economic climate. Topics range from the relative merits of fundamental, statistical, and macroeconomic models, to GARCH and other time series models, to the properties of the VIX volatility index. The book covers both mainstream and alternative asset classes, and includes in-depth treatments of model integration and evaluation. Credit and liquidity risk and the uncertainty of extreme events are examined in an intuitive and rigorous way. An extensive literature review accompanies each topic. The authors complement basic modeling techniques with references to applications, empirical studies, and advanced mathematical texts. This book is essential for financial practitioners, researchers, scholars, and students who want to understand the nature of financial markets or work toward improving them.
| Author | : Srichander Ramaswamy |
| Publisher | : John Wiley & Sons |
| Total Pages | : 256 |
| Release | : 2004-03-29 |
| Genre | : Business & Economics |
| ISBN | : 0471488321 |
Expert guidance on managing credit risk in bond portfolios Managing Credit Risk in Corporate Bond Portfolios shows readers howto measure and manage the risks of a corporate bond portfolioagainst its benchmark. This comprehensive guide explores a widerange of topics surrounding credit risk and bond portfolios,including the similarities and differences between corporate andgovernment bond portfolios, yield curve risk, default and creditmigration risk, Monte Carlo simulation techniques, and portfolioselection methods. Srichander Ramaswamy, PhD (Basel, Switzerland), is Head ofInvestment Analysis at the Bank for International Settlements (BIS)in Basel, Switzerland, and Adjunct Professor of Banking andFinance, University of Lausanne.
| Author | : Frank J. Fabozzi |
| Publisher | : John Wiley & Sons |
| Total Pages | : 513 |
| Release | : 2007-04-27 |
| Genre | : Business & Economics |
| ISBN | : 0470164891 |
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
| Author | : Attilio Meucci |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 547 |
| Release | : 2009-05-22 |
| Genre | : Business & Economics |
| ISBN | : 3642009646 |
Discusses in the practical and theoretical aspects of one-period asset allocation, i.e. market Modeling, invariants estimation, portfolia evaluation, and portfolio optimization in the prexence of estimation risk The book is software based, many of the exercises simulate in Matlab the solution to practical problems and can be downloaded from the book's web-site
| Author | : Albert Y. Zomaya |
| Publisher | : Springer |
| Total Pages | : 890 |
| Release | : 2017-02-25 |
| Genre | : Computers |
| ISBN | : 331949340X |
This handbook offers comprehensive coverage of recent advancements in Big Data technologies and related paradigms. Chapters are authored by international leading experts in the field, and have been reviewed and revised for maximum reader value. The volume consists of twenty-five chapters organized into four main parts. Part one covers the fundamental concepts of Big Data technologies including data curation mechanisms, data models, storage models, programming models and programming platforms. It also dives into the details of implementing Big SQL query engines and big stream processing systems. Part Two focuses on the semantic aspects of Big Data management including data integration and exploratory ad hoc analysis in addition to structured querying and pattern matching techniques. Part Three presents a comprehensive overview of large scale graph processing. It covers the most recent research in large scale graph processing platforms, introducing several scalable graph querying and mining mechanisms in domains such as social networks. Part Four details novel applications that have been made possible by the rapid emergence of Big Data technologies such as Internet-of-Things (IOT), Cognitive Computing and SCADA Systems. All parts of the book discuss open research problems, including potential opportunities, that have arisen from the rapid progress of Big Data technologies and the associated increasing requirements of application domains. Designed for researchers, IT professionals and graduate students, this book is a timely contribution to the growing Big Data field. Big Data has been recognized as one of leading emerging technologies that will have a major contribution and impact on the various fields of science and varies aspect of the human society over the coming decades. Therefore, the content in this book will be an essential tool to help readers understand the development and future of the field.
| Author | : Frank J. Fabozzi |
| Publisher | : John Wiley & Sons |
| Total Pages | : 528 |
| Release | : 2010-03-01 |
| Genre | : Business & Economics |
| ISBN | : 0470262478 |
A comprehensive look at the tools and techniques used in quantitative equity management Some books attempt to extend portfolio theory, but the real issue today relates to the practical implementation of the theory introduced by Harry Markowitz and others who followed. The purpose of this book is to close the implementation gap by presenting state-of-the art quantitative techniques and strategies for managing equity portfolios. Throughout these pages, Frank Fabozzi, Sergio Focardi, and Petter Kolm address the essential elements of this discipline, including financial model building, financial engineering, static and dynamic factor models, asset allocation, portfolio models, transaction costs, trading strategies, and much more. They also provide ample illustrations and thorough discussions of implementation issues facing those in the investment management business and include the necessary background material in probability, statistics, and econometrics to make the book self-contained. Written by a solid author team who has extensive financial experience in this area Presents state-of-the art quantitative strategies for managing equity portfolios Focuses on the implementation of quantitative equity asset management Outlines effective analysis, optimization methods, and risk models In today's financial environment, you have to have the skills to analyze, optimize and manage the risk of your quantitative equity investments. This guide offers you the best information available to achieve this goal.
| Author | : Robert Kissell |
| Publisher | : Academic Press |
| Total Pages | : 492 |
| Release | : 2013-10-01 |
| Genre | : Business & Economics |
| ISBN | : 0124016936 |
The Science of Algorithmic Trading and Portfolio Management, with its emphasis on algorithmic trading processes and current trading models, sits apart from others of its kind. Robert Kissell, the first author to discuss algorithmic trading across the various asset classes, provides key insights into ways to develop, test, and build trading algorithms. Readers learn how to evaluate market impact models and assess performance across algorithms, traders, and brokers, and acquire the knowledge to implement electronic trading systems. This valuable book summarizes market structure, the formation of prices, and how different participants interact with one another, including bluffing, speculating, and gambling. Readers learn the underlying details and mathematics of customized trading algorithms, as well as advanced modeling techniques to improve profitability through algorithmic trading and appropriate risk management techniques. Portfolio management topics, including quant factors and black box models, are discussed, and an accompanying website includes examples, data sets supplementing exercises in the book, and large projects. - Prepares readers to evaluate market impact models and assess performance across algorithms, traders, and brokers. - Helps readers design systems to manage algorithmic risk and dark pool uncertainty. - Summarizes an algorithmic decision making framework to ensure consistency between investment objectives and trading objectives.
| Author | : Richard O. Michaud |
| Publisher | : Oxford University Press |
| Total Pages | : 207 |
| Release | : 2008-03-03 |
| Genre | : Business & Economics |
| ISBN | : 0199887195 |
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.
