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Generally it is better to shrink the covariance matrix—since the variances of your data probably vary a lot, and the correlation matrix treats them all as essentially equal variance, you throw out the baby with the bath water by pausing to the correlation matrix. In effect, when you shrink the correlation matrix, you correct a lot of stuff that is not ...


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Yes all you have to do is estimate the Black Litterman covariance matrix that includes investor views using a shrinkage estimator. Covariance shrinkage like Ledoit Wolf is an old technique, however, that has been outperformed by the denoised or detoned covariance matrix estimated by random matrix theory, as well as the nested clustered optimization (NCO) ...


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