Large portfolio optimisation approaches

Esra Ulasan () and A. Özlem Önder
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Esra Ulasan: Smart Capital

Journal of Asset Management, 2023, vol. 24, issue 6, No 4, 485-497

Abstract: Abstract 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.

Keywords: High-dimensionality; Nodewise regression; Sparse precision matrix; Portfolio optimisation; Emerging markets (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1057/s41260-023-00322-3

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