Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/162434 
Year of Publication: 
2017
Series/Report no.: 
Working Paper No. 231
Publisher: 
University of Zurich, Department of Economics, Zurich
Abstract: 
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.
Subjects: 
Composite likelihood
dynamic conditional correlations
GARCH
Markowitz portfolio selection
nonlinear shrinkage
JEL: 
C13
C58
G11
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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