Factor Models for Portfolio Selection in Large Dimensions: The Good, the Better and the Ugly

Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-frequency Data

Gianluca De Nard, Olivier Ledoit and Michael Wolf

Journal of Financial Econometrics, 2021, vol. 19, issue 2, 236-257

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)
Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (27)

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