A blocking and regularization approach to high dimensional realized covariance estimation

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Modeling and Forecasting Large Realized Covariance Matrices and Portfolio Choice (2017, unknown): “To overcome these issues, we follow Hautsch et al. (2012) and apply eigenvalue cleaning to every matrix that has eigenvalues smaller than or equal to 0 or a condition number greater than 10nT .”
Bayesian semiparametric modeling of realized covariance matrices (2016, background): “Since then, focus has shifted to improving the estimator in the presence of market microstructure dynamics (Barndorff-Nielsen et al. 2011, Hautsch et al. 2012, Corsi et al. 2013) often through a kernel based estimator.”
Econometric Analysis of Vast Covariance Matrices Using Composite Realized Kernels and Their Application to Portfolio Choice (2016, methodology): “The first is the regularization method used in Hautsch et al. (2011), which is known as eigenvalue cleaning in the statistics and engineering literature [ADD REFERENCES TO THE RANDOM MATRIX THEORY THIS COMES FROM].”
Modeling Realized Covariances and Returns (2013, background): “Hautsch et al. (2010) introduce one approach to overcome this issues.”