A Nodewise Regression Approach to Estimating Large Portfolios

By Laurent Callot, Mehmet Caner, A. Önder, Esra Ulaşan. Journal of business & economic statistics, 2016

55 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/4ff4a73ed6a9f99bbe153eaa4f897cf8c2018816

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Laurent Callot
2. Michael Bohlke-Schneider
3. David Salinas
4. Jan Gasthaus

In Finance

1. Laurent Callot
2. Michael Bohlke-Schneider
3. David Salinas
4. Valentin Flunkert
5. Jan Gasthaus

Citations in the corpus

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