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Large dynamic covariance matrices: Enhancements based on intraday data

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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.

Language: Englisch

Bibliographic citation: Series: Working Paper ; No. 356

Classification: Wirtschaft
Estimation: General
Financial Econometrics
Portfolio Choice; Investment Decisions

Subject: Dynamic conditional correlations
intraday data
Markowitz portfolio selection
multivariate GARCH
nonlinear shrinkage

Event: Geistige Schöpfung

(who): De Nard, Gianluca
Engle, Robert F.
Ledoit, Olivier
Wolf, Michael

Event: Veröffentlichung

(who): University of Zurich, Department of Economics

(where): Zurich

(when): 2021

DOI: doi:10.5167/uzh-188753

Handle: http://hdl.handle.net/10419/235609

Last update: 10.03.2025, 11:45 AM CET

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Object type

Arbeitspapier

Associated

De Nard, Gianluca
Engle, Robert F.
Ledoit, Olivier
Wolf, Michael
University of Zurich, Department of Economics

Time of origin

2021

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