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I am exploring de-noising and cleansing of covariance matrices via Random Matrix Theory. RMT is a competitor to shrinkage methods of covariance estimation. There are various methods expressed usually by the names of the authors: LPCB, PG+, and so on.

For each method, one can start by filtering the covariance matrix directly, or filter the correlation matrix and then covert the cleansed correlation matrix into a covariance matrix. My question involves the latter case.

I have noticed that when cleaning a correlation matrix that the resulting diagonal is not a diagonal of 1s (as one would expect to see in a correlation matrix).

My question -- when constructing a cleansed covariance matrix by first filtering its corresponding correlation matrix, does one:

  1. "Fix" the diagonals of the intermediate cleansed correlation matrix to a diagonal of 1s before finally converting it back to a covariance matrix? This seems to be the case with the PG+ method, but not the LPCB method.

  2. Or does one convert the cleansed correlation matrix to a covariance matrix and then fix the diagonal of the resulting covariance matrix to the diagonal of the original covariance matrix?

In both cases the trace is preserved.

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I think RMT only helps you filter out noise eigenvectors. How we re-construct the correlation matrix after that is totally up to us. I personally will choose an approach that yield a diagonal of 1's. As for a diagonal != 1, it feels like that they are computing the 'cross-'correlation matrix between clean (after RMT) and noisy (before) time series (cause correlation of identical time series must equal to 1). Is it what you want? I thought we are looking for a correlation matrix composed of clean time series only. – 楊祝昇 Oct 24 '11 at 17:37
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Correct - RMT will identify the upper noise band or cut-off value for eigenvalues. I suppose since that is all we can ask of RMT then after this it is just up to us on how to proceed. Yes, we are looking for a correlation matrix of clean time-series only. The issue arises because when you de-noise the eigenvalues there are invariably changes to the diagonal of the correlation matrix from this process. I will test out both procedures and post my findings in a couple of days... – Quant Guy Oct 26 '11 at 3:05

1 Answer

up vote 2 down vote accepted

I tested both procedures. The results are virtually indistinguishable - the decision is not consequential. I opted for approach #1.

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