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Is it preferable to shrink the covariance matrix vs the correlation matrix? Technically this amounts to either shrinking the sample correlation matrix and then transforming the shrunk correlation matrix using the sample variances versus just shrinking the sample covariance matrix all in one go (this has the effect of shrinking the variances to tr(A)/n)

FWIW, I’ve found in practice that shrinking the covariance matrix leads to more accurate forecasts of forward variance

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