A Nodewise Regression Approach to Estimating Large Portfolios

Laurent Callot, Mehmet Caner (), A. Özlem Önder and Esra Ulaşan

Journal of Business & Economic Statistics, 2021, vol. 39, issue 2, 520-531

Abstract: This article investigates the large sample properties of the variance, weights, and risk of high-dimensional portfolios where the inverse of the covariance matrix of excess asset returns is estimated using a technique called nodewise regression. Nodewise regression provides a direct estimator for the inverse covariance matrix using the least absolute shrinkage and selection operator to estimate the entries of a sparse precision matrix. We show that the variance, weights, and risk of the global minimum variance portfolios and the Markowitz mean-variance portfolios are consistently estimated with more assets than observations. We show, empirically, that the nodewise regression-based approach performs well in comparison to factor models and shrinkage methods.Supplementary materials for this article are available online.

Date: 2021
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DOI: 10.1080/07350015.2019.1683018

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