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Contribution Details

Type Working Paper
Scope Discipline-based scholarship
Title Factor models for portfolio selection in large dimensions: the good, the better and the ugly
Organization Unit
  • Gianluca De Nard
  • Olivier Ledoit
  • Michael Wolf
  • English
Institution University of Zurich
Series Name Working paper series / Department of Economics
Number 290
ISSN 1664-7041
Number of Pages 27
Date 2018
Abstract Text This paper injects factor structure into the estimation of time-varying, large-dimensional covariance matrices of stock returns. Existing factor models struggle to model the covariance matrix of residuals in the presence of time-varying conditional heteroskedasticity in large universes. Conversely, rotation-equivariant estimators of large-dimensional time-varying covariance matrices forsake directional information embedded in market-wide risk factors. We introduce a new covariance matrix estimator that blends factor structure with time-varying conditional heteroskedasticity of residuals in large dimensions up to 1000 stocks. It displays superior all-around performance on historical data against a variety of state-of-the-art competitors, including static factor models, exogenous factor models, sparsity-based models, and structure-free dynamic models. This new estimator can be used to deliver more efficient portfolio selection and detection of anomalies in the cross-section of stock returns.
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Keywords Dynamic conditional correlations, factor models, multivariate GARCH, Markowitz portfolio selection, nonlinear shrinkage, Portfoliomanagement, Heteroskedastizit├Ąt, Korrelation, Matrixverfahren, Kovarianzmatrix, ARCH-Prozess, Portfolio Selection, Aktienrendite
Additional Information Revised version