Large Dynamic Covariance Matrices
University of Zurich, Department of Economics, Working Paper No. 231, Revised version
43 Pages Posted: 28 Jul 2016 Last revised: 20 Apr 2017
Date Written: April 1, 2017
Second moments of asset returns are important for risk management and portfolio selection. The problem of estimating second moments can be approached from two angles: time series and the cross-section. In time series, the key is to account for conditional heteroskedasticity; a favored model is Dynamic Conditional Correlation (DCC), derived from the ARCH/GARCH family started by Engle (1982). In the cross-section, the key is to correct in-sample biases of sample covariance matrix eigenvalues; a favored model is nonlinear shrinkage, derived from Random Matrix Theory (RMT). The present paper marries these two strands of literature in order to deliver improved estimation of large dynamic covariance matrices.
Keywords: Composite likelihood, dynamic conditional correlations, GARCH, Markowitz portfolio selection, nonlinear shrinkage
JEL Classification: C13, C58, G11
Suggested Citation: Suggested Citation