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Factor models for portfolio selection in large dimensions: the good, the better and the ugly


De Nard, Gianluca; Ledoit, Olivier; Wolf, Michael (2018). Factor models for portfolio selection in large dimensions: the good, the better and the ugly. Working paper series / Department of Economics 290, University of Zurich.

Abstract

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.

Abstract

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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Additional indexing

Item Type:Working Paper
Communities & Collections:03 Faculty of Economics > Department of Economics
Working Paper Series > Department of Economics
Dewey Decimal Classification:330 Economics
JEL Classification:C13, C58, G11
Uncontrolled Keywords:Dynamic conditional correlations, factor models, multivariate GARCH, Markowitz portfolio selection, nonlinear shrinkage, Portfoliomanagement, Heteroskedastizität, Korrelation, Matrixverfahren, Kovarianzmatrix, ARCH-Prozess, Portfolio Selection, Aktienrendite
Language:English
Date:December 2018
Deposited On:12 Jun 2018 11:31
Last Modified:27 Nov 2020 07:29
Series Name:Working paper series / Department of Economics
Number of Pages:27
ISSN:1664-7041
Additional Information:Revised version
OA Status:Green
Official URL:http://www.econ.uzh.ch/static/workingpapers.php?id=969
Related URLs:https://www.zora.uzh.ch/id/eprint/180957/

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