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Contribution Details

Type Working Paper
Scope Discipline-based scholarship
Title Large dynamic covariance matrices
Organization Unit
Authors
  • Robert F Engle
  • Olivier Ledoit
  • Michael Wolf
Language
  • English
Institution University of Zurich
Series Name Working paper series / Department of Economics
Number 231
ISSN 1664-7041
Number of Pages 42
Date 2017
Abstract Text 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.
Official URL http://www.econ.uzh.ch/static/wp/econwp231.pdf
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Keywords Composite likelihood, dynamic conditional correlations, GARCH, Markowitz portfolio selection, nonlinear shrinkage, Portfoliomanagement, Heteroskedastizit├Ąt, Korrelation, Matrixverfahren
Additional Information Revised version