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| Type | Working Paper |
| Scope | Discipline-based scholarship |
| Title | Factor models for portfolio selection in large dimensions: the good, the better and the ugly |
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| Institution | University of Zurich |
| Series Name | Working paper series / Department of Economics |
| Number | 290 |
| ISSN | 1664-7041 |
| Number of Pages | 27 |
| Date | 2018 |
| Abstract Text | 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. |
| Official URL | http://www.econ.uzh.ch/static/workingpapers.php?id=969 |
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| Keywords | Dynamic conditional correlations, factor models, multivariate GARCH, Markowitz portfolio selection, nonlinear shrinkage, Portfoliomanagement, Heteroskedastizität, Korrelation, Matrixverfahren, Kovarianzmatrix, ARCH-Prozess, Portfolio Selection, Aktienrendite |
| Additional Information | Revised version |