Arbeitspapier
Large dynamic covariance matrices
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Working Paper ; No. 231
- Klassifikation
-
Wirtschaft
Estimation: General
Financial Econometrics
Portfolio Choice; Investment Decisions
- Thema
-
Composite likelihood
dynamic conditional correlations
GARCH
Markowitz portfolio selection
nonlinear shrinkage
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Engle, Robert F.
Ledoit, Olivier
Wolf, Michael
- Ereignis
-
Veröffentlichung
- (wer)
-
University of Zurich, Department of Economics
- (wo)
-
Zurich
- (wann)
-
2017
- DOI
-
doi:10.5167/uzh-125469
- Handle
- Letzte Aktualisierung
-
03.05.2025, 12:48 MESZ
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
- Engle, Robert F.
- Ledoit, Olivier
- Wolf, Michael
- University of Zurich, Department of Economics
Entstanden
- 2017
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