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Non-linear shrinkage of the price return covariance matrix is far from optimal for portfolio optimization

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  • Bongiorno, Christian
  • Challet, Damien

Abstract

Portfolio optimization requires sophisticated covariance estimators that are able to filter out estimation noise. Non-linear shrinkage is a popular estimator based on how the Oracle eigenvalues can be computed using only data from the calibration window. Contrary to common belief, NLS is not optimal for portfolio optimization because it does not minimize the right cost function when the asset dependence structure is non-stationary. We instead derive the optimal target. Using historical data, we quantify by how much NLS can be improved. Our findings reopen the question of how to build the covariance matrix estimator for portfolio optimization in realistic conditions.

Suggested Citation

  • Bongiorno, Christian & Challet, Damien, 2023. "Non-linear shrinkage of the price return covariance matrix is far from optimal for portfolio optimization," Finance Research Letters, Elsevier, vol. 52(C).
    • Handle: RePEc:eee:finlet:v:52:y:2023:i:c:s1544612322005608
    DOI: 10.1016/j.frl.2022.103383
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