A Nodewise Regression Approach to Estimating Large Portfolios
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...
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Zusammenfassung: | 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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DOI: | 10.48550/arxiv.1611.07347 |