Large dynamic covariance matrices

Robert Engle, Olivier Ledoit and Michael Wolf

No 231, ECON - Working Papers from Department of Economics - University of Zurich

Abstract: 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.

Keywords: Composite likelihood; dynamic conditional correlations; GARCH; Markowitz portfolio selection; nonlinear shrinkage. (search for similar items in EconPapers)
JEL-codes: C13 C58 G11 (search for similar items in EconPapers)
Date: 2016-07, Revised 2017-04
New Economics Papers: this item is included in nep-cse, nep-ecm, nep-ets, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (31)

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Related works:
Journal Article: Large Dynamic Covariance Matrices (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:zur:econwp:231

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