Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/222571
Authors: 
De Nard, Gianluca
Engle, Robert F.
Ledoit, Olivier
Wolf, Michael
Year of Publication: 
2020
Series/Report no.: 
Working Paper No. 356
Abstract: 
Modeling and forecasting dynamic (or time-varying) covariance matrices has many important applications in finance, such as Markowitz portfolio selection. A popular tool to this end are multivariate GARCH models. Historically, such models did 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 covariances, is the concept of a regularized return, obtained from a volatility proxy in conjunction with a smoothed sign (function) of the observed return.
Subjects: 
dynamic conditional correlations
intraday data
Markowitz portfolio selection
multivariate GARCH
nonlinear shrinkage
JEL: 
C13
C58
G11
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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