Size Matters: Optimal Calibration of Shrinkage Estimators for Portfolio Selection

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Nonlinear Shrinkage of the Covariance Matrix for Portfolio Selection: Markowitz Meets Goldilocks (2017, unknown): “Recent proposals by Ledoit and Wolf (2003, 2004a,b), Kan and Zhou (2007), Brandt et al. (2009), DeMiguel et al. (2009a, 2013), Frahm and Memmel (2010), and Tu and Zhou (2011), among others, show that this topic is currently gathering a significant amount of attention.”
Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly (2019, background): “…expansion over the last decade or so and is still growing; to list only a few examples, see Ledoit and Wolf (2003); Jagannathan and Ma (2003); Fan, Fan, and Lv (2008); DeMiguel et al. (2009a); Fan, Liao, and Mincheva (2013); DeMiguel, Martin-Utrera, and Nogales (2013); and Ledoit and Wolf (2017).”
Covariance Matrix Estimation under Total Positivity for Portfolio Selection* (2019, methodology): “In particular, various methods considered regularizing the eigenvalues of S [LW04, LW12, ELW17, JM03, DMUN13]; collectively, these methods can be regarded as particular instances of empirical Bayesian shrinkage [Haf80, LW04, Ste56].”