Large Dynamic Covariance Matrices: Enhancements Based on Intraday Data
University of Zurich, Department of Economics, Working Paper No. 356, Revised version
39 Pages Posted: 25 Sep 2020 Last revised: 30 Jun 2021
Date Written: June 2021
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.
Keywords: Dynamic conditional correlations, intraday data, Markowitz portfolio selection, multivariate GARCH, nonlinear shrinkage
JEL Classification: C13, C58, G11
Suggested Citation: Suggested Citation