A Robust Statistics Approach to Minimum Variance Portfolio Optimization

• Published in: IEEE transactions on signal processing Vol. 63; no. 24; pp. 6684 - 6697
• Main Authors: Liusha Yang, Couillet, Romain, McKay, Matthew R.
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.12.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Covariance matrices; Eigenvalues and eigenfunctions; Engineering Sciences; Estimation; Mathematics; Minimum variance portfolio; Optimization; Portfolios; random matrix theory; Robustness; shrinkage estimator; Statistics; Symmetric matrices; Tyler's M-estimator
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2015.2474298

We study the design of portfolios under a minimum risk criterion. The performance of the optimized portfolio relies on the accuracy of the estimated covariance matrix of the portfolio asset returns. For large portfolios, the number of available market returns is often of similar order to the number of assets, so that the sample covariance matrix performs poorly as a covariance estimator. Additionally, financial market data often contain outliers which, if not correctly handled, may further corrupt the covariance estimation. We address these shortcomings by studying the performance of a hybrid covariance matrix estimator based on Tyler's robust M-estimator and on Ledoit-Wolf's shrinkage estimator while assuming samples with heavy-tailed distribution. Employing recent results from random matrix theory, we develop a consistent estimator of (a scaled version of) the realized portfolio risk, which is minimized by optimizing online the shrinkage intensity. Our portfolio optimization method is shown via simulations to outperform existing methods both for synthetic and real market data.