Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/251443 
Erscheinungsjahr: 
2022
Schriftenreihe/Nr.: 
Working Paper No. 356
Versionsangabe: 
This version: January 2022
Verlag: 
University of Zurich, Department of Economics, Zurich
Zusammenfassung: 
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.
Schlagwörter: 
Dynamic conditional correlations
intraday data
Markowitz portfolio selection
multivariate GARCH
nonlinear shrinkage
JEL: 
C13
C58
G11
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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