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

Gianluca De Nard, Olivier Ledoit and Michael Wolf ()

No 290, ECON - Working Papers from Department of Economics - 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.

Keywords: Dynamic conditional correlations; factor models; multivariate GARCH; Markowitz portfolio selection; nonlinear shrinkage (search for similar items in EconPapers)
JEL-codes: C13 C58 G11 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-knm and nep-ore
Date: 2018-06, Revised 2018-12
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