Arbeitspapier
Large dynamic covariance matrices: Enhancements based on intraday data
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Working Paper ; No. 356
- Klassifikation
-
Wirtschaft
Estimation: General
Financial Econometrics
Portfolio Choice; Investment Decisions
- Thema
-
Dynamic conditional correlations
intraday data
Markowitz portfolio selection
multivariate GARCH
nonlinear shrinkage
- Ereignis
-
Geistige Schöpfung
- (wer)
-
De Nard, Gianluca
Engle, Robert F.
Ledoit, Olivier
Wolf, Michael
- Ereignis
-
Veröffentlichung
- (wer)
-
University of Zurich, Department of Economics
- (wo)
-
Zurich
- (wann)
-
2021
- DOI
-
doi:10.5167/uzh-188753
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- De Nard, Gianluca
- Engle, Robert F.
- Ledoit, Olivier
- Wolf, Michael
- University of Zurich, Department of Economics
Entstanden
- 2021
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