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arXiv · 1611.07347

A Nodewise Regression Approach to Estimating Large Portfolios

Laurent Callot·Mehmet Caner·Esra Ulasan·A. Özlem Önder
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Abstract

This paper 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 (Lasso) of Tibshirani (1994) 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.

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Laurent Callot, Mehmet Caner, Esra Ulasan, A. Özlem Önder. 2016-11-22. A Nodewise Regression Approach to Estimating Large Portfolios. https://arxiv.org/abs/1611.07347

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