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Portfolio optimisation via strategy-specific eigenvector shrinkage

Author

Listed:
  • Lisa R. Goldberg

    (UC Berkeley
    Aperio Group LLC, a wholly-owned indirect subsidiary of BlackRock, Inc.)

  • Hubeyb Gurdogan

    (UCLA)

  • Alec Kercheval

    (Florida State University)

Abstract

We estimate covariance matrices that are tailored to portfolio optimisation constraints. We rely on a generalised version of James–Stein for eigenvectors (JSE), a data-driven operator that reduces estimation error in the leading sample eigenvector by shrinking towards a target subspace determined by constraint gradients. Unchecked, this error gives rise to excess volatility for optimised portfolios. Our results include a formula for the asymptotic improvement of JSE over the leading sample eigenvector as an estimate of ground truth, and provide improved optimal portfolio estimates when variance is to be minimised subject to finitely many linear constraints.

Suggested Citation

  • Lisa R. Goldberg & Hubeyb Gurdogan & Alec Kercheval, 2025. "Portfolio optimisation via strategy-specific eigenvector shrinkage," Finance and Stochastics, Springer, vol. 29(3), pages 665-706, July.
    • Handle: RePEc:spr:finsto:v:29:y:2025:i:3:d:10.1007_s00780-025-00566-4
    DOI: 10.1007/s00780-025-00566-4
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    References listed on IDEAS

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    Keywords

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    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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