We obtain general, exact formulas for the overlaps between the eigenvectors of large correlated random matrices, with additive or multiplicative noise. These results have potential applications in many different contexts, from quantum thermalisation to high dimensional statistics. We  apply our results to the case of empirical correlation matrices, that allow us to estimate reliably the width of the spectrum of the 'true ' underlying correlation matrix, even when the latter is very close to the identity matrix . We illustrate our results on the example of stock returns correlations, that clearly reveal a non trivial structure for the bulk eigenvalues  .
We obtain general, exact formulas for the overlaps between the eigenvectors of large correlated random matrices, with additive or multiplicative noise. These results have potential applications in many different contexts, from quantum thermalisation to high dimensional statistics. We find that the overlaps only depend on measurable quantities, and do not require the knowledge of the underlying "true" (noiseless) matrices. We apply our results to the case of empirical correlation matrices, that allow us to estimate reliably the width of the spectrum of the true correlation matrix, even when the latter is very close to the identity . We illustrate our results on the example of stock returns correlations, that clearly reveal a non trivial structure for the bulk eigenvalues . We also apply our results to the problem of matrix denoising in high dimension .
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find that the overlaps only depend on measurable quantities, and do not require the knowledge of the underlying "true" (noiseless) matrices. We
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'true ' underlying
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matrix
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. We also apply our results to the problem of matrix denoising in high dimension