Large portfolio optimisation approaches

Large portfolio optimisation approaches

This paper makes an empirical comparison of prominent methods in portfolio optimisation, such as nodewise regression, the sample covariance matrix, observable factor model-based covariance matrices, linear and nonlinear shrinkage methods, and principal orthogonal complement thresholding (POET) estim...

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Bibliographic Details
Published in:Journal of asset management Vol. 24; no. 6; pp. 485 - 497
Main Authors: Ulasan, Esra, Önder, A. Özlem
Format: Journal Article
Language:English
Published: London Palgrave Macmillan UK 01-10-2023
Palgrave Macmillan
Subjects:
Bull markets
Comparative analysis
Economics and Finance
Emerging markets
Finance
Financial Services
Original Article
Portfolio performance
Risk Management
Securities markets
Stock exchanges
Volatility
Nodewise regression
High-dimensionality
Sparse precision matrix
Emerging markets
Portfolio optimisation
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Summary:This paper makes an empirical comparison of prominent methods in portfolio optimisation, such as nodewise regression, the sample covariance matrix, observable factor model-based covariance matrices, linear and nonlinear shrinkage methods, and principal orthogonal complement thresholding (POET) estimators. Empirically, we find that the nodewise regression approach that uses a direct estimator of the sparse inverse covariance matrix improves portfolio performance among existing methods in mean-variance portfolio optimisation when the number of stocks is greater than the number of observations.
ISSN:1470-8272
1479-179X
DOI:10.1057/s41260-023-00322-3