Seminar title: Large dynamic covariance matrices: Enhancements based on intraday data
Invited Speaker: Professor Michael Wolf, University of Zurich, Switzerland
Time and date: 2:00 PM, Wed – 14 September 2022
Location: Room A1.204, International University – VNUHCM, Quarter 6, Linh Trung Ward, Thu Duc City, Ho Chi Minh City
Abstract:
Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how performance can be increased further by using open/high/low/close (OHLC) price data instead of simply using daily returns. A key innovation, for the improved modeling of not only dynamic variances but also of dynamic correlations, is the concept of a regularized return, obtained from a volatility proxy in conjunction with a smoothed sign of the observed return.
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.
