will actually be far from the best choice. This is especially a drawback when managing equity portfolios because the equity market is one of the more volatile markets, making it difficult to estimate values such as expected returns.
In equity portfolio management, there has been increased interest in the construction of portfolios that offer the potential for more robust performance even during more volatile equity market periods. One common approach for doing so is to increase the robustness of the input values of mean-variance analysis by adopting estimators that are more robust to outliers. It is also possible to achieve higher robustness by focusing on the outputs of the mean-variance model by performing simulations for collecting many possible portfolios and then finally arriving at one optimal portfolio based on all the possible ones. There are other methods that are based on the equilibrium of the equity market for gaining robustness.2
Although various techniques have been applied to improve the stability of portfolios, one of the approaches that has received much attention is robust portfolio optimization. Robust optimization is a method that incorporates parameter uncertainty by defining a set of possible values, referred to as an uncertainty set. The optimal solution represents the best choice when considering all possibilities from the uncertainty set. Robust optimization was developed for addressing optimization problems where the true values of the model's parameters are not known with certainty, but the bounds are assumed to be known. In 1973, Allen Soyster discussed inexact linear programming; in the 1990s, the initial approach expanded to incorporate a number of ways for defining uncertainty sets and addressing more complex optimization problems. When robust optimization is extended to portfolio selection, the inputs used in mean-variance analysis – the vector of mean returns and the covariance matrix of returns – become the uncertain parameters for finding the optimal portfolio. Since the turn of the century, there have been numerous proposals for formulating robust portfolio optimization problems. Much of the focus has been on mathematical theories behind uncertainty set construction and reformulations resulting in optimization problems that can be solved efficiently; and, as a result, there are many formulations that can be used to build robust equity portfolios.
Even though there has been considerable development on robust portfolio management, most approaches require skills far beyond perfecting mean-variance analysis. For example, it is not an easy task for a portfolio manager without extensive background knowledge in optimization and mathematics to understand robust portfolio optimization formulations. More importantly, being able to interpret robust formulations is only the first step. The second step requires solving the optimization problem to arrive at the optimal decision. Programming expertise, in addition to optimization and mathematics, is necessary in the second step because most robust formulations require complex computations. Thus, while the need and the value of robust portfolio management are apparent, only those with appropriate training will be equipped to explore the advanced methods for improving portfolio robustness.
This book is aimed at providing a step-by-step guide for using robust models for optimal portfolio construction. It is not assumed that the reader has prior knowledge in portfolio management and optimization. In this book, the basics of portfolio theory and optimization, along with programming examples, will allow the reader to gain familiarity with portfolio optimization. Once the fundamentals of portfolio management are outlined, robust approaches for managing portfolios are explained with an emphasis on robust portfolio optimization. Details on robust formulations, implementation of robust portfolio optimization, attributes of robust portfolios, and robust portfolio performance will prepare the reader to utilize robust portfolio optimization for managing portfolios. In this book, we not only review theoretical developments but provide numerous programming examples to demonstrate their use in practice. The programming examples that appear throughout the book illustrate the details of implementing various techniques including methods for constructing robust equity portfolios.
1.1 Overview of the Chapters
The book is divided into three parts. The first part, Chapters 2 through 4, introduces the mean-variance model, discusses its shortcomings, and explains common approaches for increasing the robustness of portfolios. The second part, Chapters 5 and 6, contains an overview of optimization and details the steps involved in formulating a robust portfolio optimization problem. The third part, Chapters 7 through 10, focuses on analyzing robust portfolios constructed from robust portfolio optimization by identifying attributes and summarizing performances.
Chapter 2 begins by describing how portfolio return and risk are measured, which leads to formulating the mean-variance portfolio problem. Mean-variance analysis finds the optimal portfolio from the trade-off between return and risk, and the framework also explains the benefits of diversification. Chapter 3 investigates shortcomings of the mean-variance model, which limit its use as a strategy for managing equity portfolios; improvements can be made with respect to measuring risk, estimating the input variables, and reducing the sensitivity of portfolio weights. In particular, the combination of estimation errors in the input values and high sensitivity of the resulting portfolio is a major issue with the mean-variance model. Therefore, in Chapter 4, practices for reducing the sensitivity of portfolios are demonstrated, including robust statistics, simulation methods, and stochastic programming.
Chapter 5 presents a comprehensive overview of optimization, including definitions of linear programming, quadratic programming, and conic optimization. The chapter also discusses how robust optimization transforms basic optimization problems so as to incorporate parameter uncertainty. The discussion is extended to applying robust optimization to portfolio selection in Chapter 6. While concentrating on the uncertainty caused by estimating expected returns of stocks, two robust formulations are shown with specific instructions provided as to their implementation.
Chapters 7, 8, and 9 analyze portfolio attributes that are revealed when portfolios are formed from robust portfolio optimization. In Chapter 7, we provide empirical evidence that indicates that some uncertainty sets lead to portfolios that favor skewness but penalize kurtosis. The high factor exposure of robust portfolios at the portfolio level is addressed in Chapter 8, and Chapter 9 examines portfolio weights allocated to individual stocks for comparing the composition of robust portfolios with mean-variance portfolios that assume no uncertainty. Chapter 10 illustrates the robustness of robust portfolios by observing their historical performance.
The final chapter, Chapter 11, discusses software packages that can help solve robust portfolio optimization and provides examples for finding robust portfolios.
1.2 Use of MATLAB
Financial modeling often requires computer programs for solving complex computations. The use of powerful computing tools is inevitable in portfolio management because portfolio selection problems are mathematically expressed as optimization problems. Thus, tools that efficiently solve optimization problems give portfolio managers a great advantage; the tools are more valuable for robust portfolio management because approaches such as robust portfolio optimization involve more intense computations.
Therefore,
An example of improving the robustness of inputs is to use shrinkage estimators, introduced in Philippe Jorion, “Bayes-Stein Estimation for Portfolio Analysis,”
