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Robust Portfolio Optimisation Under Sparse Contamination

Author

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  • Carlo E. Autiero

    (University of Rome “Tor Vergata”)

  • Alessio Farcomeni

    (University of Rome “Tor Vergata”)

Abstract

We introduce novel methods for mean-variance portfolio optimisation in the presence of component-wise contamination. Methods are obtained by combining component-wise robust location-scatter estimation and optimisation based on genetic algorithms. The newly proposed approaches are compared with classical and row-wise robust methods in a simulation study and a real-data application on data from the Italian stock exchange. Results show a strong advantage of cell-wise resistant methodologies over competitors, both in terms of absolute risk and Sharpe ratio.

Suggested Citation

  • Carlo E. Autiero & Alessio Farcomeni, 2025. "Robust Portfolio Optimisation Under Sparse Contamination," Computational Economics, Springer;Society for Computational Economics, vol. 66(2), pages 1137-1155, August.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:2:d:10.1007_s10614-024-10733-y
    DOI: 10.1007/s10614-024-10733-y
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    References listed on IDEAS

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