How Robust Is Robust Covariance Evidence From International Portfolio Selection
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| Author | : Tsung-Wu Ho |
| Publisher | : |
| Total Pages | : 35 |
| Release | : 2015 |
| Genre | : |
| ISBN | : |
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.
| Author | : Jay Emmanuelle |
| Publisher | : |
| Total Pages | : 5 |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
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.
| Author | : Larry R. Gorman |
| Publisher | : |
| Total Pages | : 36 |
| Release | : 2003 |
| Genre | : |
| ISBN | : |
The observed international home bias has traditionally been viewed as an anomaly. We provide statistical evidence contrary to this view within a mean-variance framework. We investigate two methods of estimating the expected return and covariance parameters: (i) the Bayes-Stein quot;shrinkagequot; algorithm, and (ii) the traditional Markowitz approach. In in-sample tests, neither the Bayes-Stein tangency allocation vector, nor the Markowitz tangency allocation vectors are significantly different from a 100% domestic allocation (i.e. extreme home bias). The result is robust to the shorting of equity, and across foreign exchange hedge strategies. We also conduct out-of-sample tests with a view toward investment performance. We find that a 100% domestic allocation typically outperforms both the Bayes-Stein and Markowitz tangency portfolios. Overall, the theorized gains to international diversification appear difficult to capture in practice, and hence investors exhibiting a strong home bias are not necessarily acting irrationally.
| 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 | : Alexandre Ferreira |
| Publisher | : |
| Total Pages | : 25 |
| Release | : 2017 |
| Genre | : |
| ISBN | : |
Two crucial aspects to the problem of portfolio selection are the specification of the model for expected returns and their covariances, as well as the choice of the investment policy to be adopted. A common trade-off is to consider dynamic covariance specifications vis-a-vis static models such as those based on shrinkage methods. This work empirically shows that these two aspects are intrinsically attached to the impact of transaction costs. To address this question, we implement a broad range of covariance specifications to generate a set of 16 portfolio selection policies over a period of 25 years in a high dimensional sample composed of 69 stocks belonging to the S&P100 index. We find that, in the absence of transaction costs, GARCH-type dynamic covariances deliver portfolios with similar risk-adjusted performance with respect to those obtained with static covariance specifications. In more realistic scenarios involving alternative levels of transaction costs, portfolios based on static covariance models consistently outperform as they demand a much lower level of portfolio turnover. In particular, we find that a risk-averse investor with quadratic utility function is willing to pay an annualized fee of 291 basis points (bp) on average in order to switch from the dynamic covariance models to a benchmark static covariance specification when the level of transaction costs is 20 bp. Finally, portfolio policies that seek to alleviate estimation error by ignoring off-diagonal covariance elements such as those proposed in Kirby and Ostdiek (2012) are more robust specially in scenarios with higher transaction costs.
| Author | : Canh Thien Dang |
| Publisher | : Springer Nature |
| Total Pages | : 1874 |
| Release | : 2023-10-07 |
| Genre | : Political Science |
| ISBN | : 9819964415 |
This proceedings volume contains papers accepted by the 2nd International Conference on Business and Policy Studies (CONF-BPS 2023), which are carefully selected and reviewed by professional reviewers from corresponding research fields and the editorial team of the conference. This volume presents the latest research achievements, inspirations, and applications in applied economy, finance, enterprise management, public administration, and policy studies. CONF-BPS 2023 was a hybrid conference that includes several workshops (offline and online) around the world in Cardiff (Jan, 2023), London(Feb, 2023) and Sydney (Feb, 2023). Prof. Canh Thien Dang from King's College London, Prof. Arman Eshraghi from Cardiff Business School, and Prof. Kristle Romero Cortés from UNSW Business School have chaired those offline workshop.
| Author | : Bin Li |
| Publisher | : CRC Press |
| Total Pages | : 227 |
| Release | : 2018-10-30 |
| Genre | : Business & Economics |
| ISBN | : 1482249642 |
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.
| Author | : John B. Guerard, Jr. |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 796 |
| Release | : 2009-12-12 |
| Genre | : Business & Economics |
| ISBN | : 0387774394 |
Portfolio construction is fundamental to the investment management process. In the 1950s, Harry Markowitz demonstrated the benefits of efficient diversification by formulating a mathematical program for generating the "efficient frontier" to summarize optimal trade-offs between expected return and risk. The Markowitz framework continues to be used as a basis for both practical portfolio construction and emerging research in financial economics. Such concepts as the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theory (APT), for example, provide the foundation for setting benchmarks, for predicting returns and risk, and for performance measurement. This volume showcases original essays by some of today’s most prominent academics and practitioners in the field on the contemporary application of Markowitz techniques. Covering a wide spectrum of topics, including portfolio selection, data mining tests, and multi-factor risk models, the book presents a comprehensive approach to portfolio construction tools, models, frameworks, and analyses, with both practical and theoretical implications.
| Author | : Claude Diderich |
| Publisher | : John Wiley & Sons |
| Total Pages | : 364 |
| Release | : 2009-02-18 |
| Genre | : Business & Economics |
| ISBN | : 0470742879 |
Diderich describes tools and techniques, which can be used to develop quantitative models for actively managing investment products, and focuses on how theoretical models can and should be used in practice. He describes the interaction between different elements of an investment process's value chain in a single and consistent framework. A key focus is placed on illustrating the theory with real world examples. At the end of the book the reader will be capable of designing or enhancing an investment process for an investment or portfolio managers products from start to finish. * Increased pressure to add value through investments makes this a hot topic in the investment world * Combined theoretical and practical approach makes this book appealing to a wide audience of quants and investors * The only book to show how to design and implement quantitative models for gaining positive alpha
| Author | : Xiaoping Zhou |
| Publisher | : |
| Total Pages | : 38 |
| Release | : 2014 |
| Genre | : |
| ISBN | : |
Sample covariance is known to be a poor estimate when the data are scarce compared with the dimension. To reduce the estimation error, various structures are usually imposed on the covariance such as low-rank plus diagonal (factor models), banded models and sparse inverse covariances. We investigate a different non-parametric regularization method which assumes that the covariance is monotone and smooth. We study the smooth monotone covariance by analysing its performance in reducing various statistical distances and improving optimal portfolio selection. We also extend its use in non-Gaussian cases by incorporating various robust covariance estimates for elliptical distributions. Finally, we provide two empirical examples using Eurodollar futures and corporate bonds where the smooth monotone covariance improves the out-of-sample covariance prediction and portfolio optimization.
