Cleaning large correlation matrices: Eigenvector overlaps, rotationally invariant estimators and financial applications

Printer-friendly version
Speaker: 
Marc Potters
Institution: 
Capital Fund Management (Paris) and UCLA Applied Mathematics
Time: 
Tue, 10/17/2017 - 11:00am
Location: 
RH 306

Modern financial portfolio construction uses mean-variance optimisation that requiers the knowledge of a very large covariance matrix. Replacing the unknown covariance matrix by the sample covariance matrix (SCM) leads to disastrous out-of-sample results that can be explained by properties of large SCM understood since Marcenko and Pastur.  A better estimate of the true covariance can be built by studying the eigenvectors of SCM via the average matrix resolvent. This object can be computed using a matrix generalisation of Voiculescu’s addition and multiplication of free matrices.  The original result of Ledoit and Peche on SCM can be generalise to estimate any rotationally invariant matrix corrupted by additive or multiplicative noise. Note that the level of rigor of the seminar will be that of statistical physics.

This is a joint applied math/probability seminar.