Marcel-Dagenais Econometrics Seminar 2018-2019
joint with the Département de sciences économiques, Université de Montréal
room C-6149 (U. of Montreal, Lionel-Groulx Pavillon, 3150 Jean-Brillant Street)
Organizer : Marine Carrasco (U. of Montreal)
ABSTRACT
We show how to pick optimal portfolios by modulating the impact of estimation risk in large covariance matrices. Under the Sharpe ratio maximization framework, a portfolio consistent with an investor’s view about future expected returns can be approximated by first few eigenvectors of sample covariance matrix. We substitute the vector of expected returns by its lower-dimensional approximation, so that the portfolio is not contaminated by more severe estimation errors in tail principal components. To seek a critical balance between approximation error and estimation error in our approach, we propose a method that sets a tolerance limit for the former.
