Date: 2015-10-02

Time: 15:30-16:30

Location: BURN 1205

Abstract:

In finance, the covariance matrix of many assets is a key component of financial portfolio optimization and is usually estimated from historical data. Much research in the past decade has focused on improving estimation by studying the asymptotics of large covariance matrices in the so-called high-dimensional regime, where the dimension p grows at the same pace as the sample size n, and this approach has been very successful. This choice of growth makes sense in part because, based on results for eigenvalues, it appears that there are only two limits: the high-dimensional one when p grows like n, and the classical one, when p grows more slowly than n. In this talk, I will present evidence that this binary view is false, and that there could be hidden intermediate regimes lying in between. In turn, this allows for corrections to the sample covariance matrix that are more appropriate when the dimension is large but moderate with respect to the sample size, as is often the case; this can also lead to better optimization for portfolio volatility in many situations of interest.

Speaker

Didier Chételat is a newly hired Assistant Professor in the Department of Decision Sciences at HEC Montréal.