Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks

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Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly (2019, methodology): “…daily data for the estimation seems to lead to better results even if the investment period is 1 month; for examples, compare Tables 1 and 10 of Ledoit and Wolf (2017).1 Therefore, if anything, we would rather go to higher frequencies toward realized GARCH than to lower frequencies and monthly…”
High‐dimensional covariance matrix estimation (2019, methodology): “Ledoit and Wolf (2017) applied (cid:6) NS to portfolio allocation and also developed further a portfolio allocation strategy using a mean target return.”
Cleaning large correlation matrices: tools from random matrix theory (2016, unknown): “Note that this result has been obtained in [140]where the authors also showed that this estimator maximizes the Sharpe ratio, i.”
The Power of (Non-)Linear Shrinking: A Review and Guide to Covariance Matrix Estimation (2020, unknown): “In Ledoit and Wolf (2017a), we compared the nonlinear shrinkage estimator S•T to a variety of other covariance matrix estimators from the literature for the purpose of Markowitz’s portfolio selection, using backtest exercises with real-life stock return data; we used both daily and monthly return…”