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

By G. D. Nard, Olivier Ledoit, Michael Wolf. Journal of Financial Econometrics, 2019

106 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/f409f0c5af966a961f901734dc86c016d3c5eeed

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Citations in the corpus

Covariance Matrix Estimation under Total Positivity for Portfolio Selection* (2019, methodology): “nt universe because many stocks do not have data for the entire period and the most relevant stocks (i.e. by market capitalization or volume) naturally vary over time. We use the same procedure as in [DNLW18] to construct the investment universe. Specically, we consider the set of stocks that have (”
The Power of (Non-)Linear Shrinking: A Review and Guide to Covariance Matrix Estimation (2020, methodology): “A more flexible approach, explored in De Nard, Ledoit, and Wolf (2021), is to use shrinkage estimation for the residual covariance matrix of a general factor model.”