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Large portfolio optimisation approaches

Author

Listed:
  • Esra Ulasan

    (Smart Capital)

  • A. Özlem Önder

    (Ege University)

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.

Suggested Citation

  • Esra Ulasan & A. Özlem Önder, 2023. "Large portfolio optimisation approaches," Journal of Asset Management, Palgrave Macmillan, vol. 24(6), pages 485-497, October.
    • Handle: RePEc:pal:assmgt:v:24:y:2023:i:6:d:10.1057_s41260-023-00322-3
    DOI: 10.1057/s41260-023-00322-3
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    References listed on IDEAS

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