A Nodewise Regression Approach to Estimating Large Portfolios
研究用节点回归技术估计高维投资组合的方差、权重和风险，证明在资产数多于观测数时仍能一致估计，实证表现优于因子模型和收缩方法。
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.