A Nodewise Regression Approach to Estimating Large Portfolios
Makaleler > Tam Makale · 2019
Yazarlar: Laurent Callot, Mehmet Caner, Asiye Özlem ÖNDER, ESRA ULAŞAN
Journal of Business & Economic Statistics, SCI Expanded İndekslerine Giren Dergi, Uluslararası, Wos, 2019, cilt 39, ss 520-531
DOI: 10.1080/07350015.2019.1683018
Özet
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.