Public lecture: “Covariance Matrix Estimation for Portfolio Selection: Markowitz Meets Goldilocks and Sharknadoes”

Venue/Location: C2-714, VIASM

Speaker: Prof. Michael Wolf, University of Zurich, Switzerland

Time:  15:00 - 16:15 Monday, 17/06/2019


Many econometric and data-science applications require a reliable estimate of the covariance matrix, such as Markowitz portfolio selection. When the number of variables is of the same magnitude as the number of observations, this constitutes a difficult estimation problem; the sample covariance matrix certainly will not do. In this talk we review our work in this area going back 15+ years. We have promoted various shrinkage estimators, which can be classified into linear and nonlinear. Linear shrinkage is simpler to understand, to derive, and to implement. But nonlinear shrinkage can deliver another level of performance improvement, especially if overlaid with stylized facts such as time-varying co-volatility or factor models.

About the speaker:      

Michael Wolf is a Professor of Econometrics and Applied Statistics at the University of Zurich and holds a Ph.D. in Statistics from Stanford University. Before joining the Department of Economics at the University of Zurich, he held previous positions at The University of California (Los Angeles), Universidad Carlos III (Madrid), and Universitat Pompeu Fabra (Barcelona). Michael Wolf's research interests include resampling-based inference, multiple testing methods, the estimation of large-dimensional covariance matrices, and financial econometrics.  His research has been published in leading journals, such as The Annals of Statistics, Biometrika, Econometrica, Journal of the American Statistical Association, and The Review of Financial Studies.