# Cleaning correlation matrix, Bun Bouchaud Potters (2016) method

Stock returns correlation matrices are notoriously hard to estimate, especially when the number of assets $N$ is large with respect to the size of the readily available historical returns $T$. Many methods exist to ease the estimation (and work-around the problem's ill-posedness).

I wondered if anyone tried to implement the unbiased version of the Rotational Invariant correlation matrix Estimator (RIE) described in this paper.

I've written some python code to test it and it does not seem to offer a significant advantage over the standard sample estimator of the correlation matrix.

I've already checked my code several time so I'd be interested to know if someone managed to implement it adequately (I don't need the code, just to know whether there is a typo in the paper for instance).