Robust Portfolio Construction

Abstract

Outliers in asset returns factors are a frequently occurring phenomenon across all asset classes and can have an adverse influence on the performance of mean–variance optimized (MVO) portfolios. This occurs by virtue of the unbounded influence that outliers can have on the mean returns and covariance matrix estimates (alternatively, correlations and variances estimates) that are inputs are optimizer inputs. A possible solution to the problem of such outlier sensitivity of MVO is to use robust estimates of mean returns and covariance matrices in place of the classical estimates of these quantities thereby providing robust MVO portfolios. We show that the differences occurring between classical and robust estimates for these portfolios are such as to be of considerable concern to a portfolio manager. It turns out that robust distances based on a robust covariance matrix can provide reliable identification of multidimensional outliers in both portfolio returns and the exposures matrix of a fundamental factor model, something that is not possible with one-dimensional Winsorization. Multidimensional visualization combined with clustering methods is also useful for returns outlier identification. The question of using robust and classical MVO vs. optimization-based fat-tailed skewed distribution fits and downside risk measure is briefly discussed. Some other applications of robust methods in portfolio management are described, and we point out some future research that is needed on the topic.