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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 (2021). Factor models for portfolio selection in large dimensions: the good, the better and the ugly. Journal of Financial Econometrics, 19(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.

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:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:330 Economics
Uncontrolled Keywords:Economics and Econometrics, Finance
Language:English
Date:3 August 2021
Deposited On:14 Jan 2020 09:03
Last Modified:04 Aug 2021 01:01
Publisher:Oxford University Press
ISSN:1479-8409
Additional Information:This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Journal of Financial Econometrics following peer review. The definitive publisher-authenticated version "De Nard, Gianluca; Ledoit, Olivier; Wolf, Michael (2019). Factor models for portfolio selection in large dimensions: the good, the better and the ugly. Journal of Financial Econometrics, nby033" is available online at: dx.doi.org/10.1093/jjfinec/nby033
OA Status:Green
Publisher DOI:https://doi.org/10.1093/jjfinec/nby033
Related URLs:https://www.zora.uzh.ch/id/eprint/151986/

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