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Noise-proofing Universal Portfolio Shrinkage

Author

Listed:
  • Paul Ruelloux
  • Christian Bongiorno
  • Damien Challet

Abstract

We enhance the Universal Portfolio Shrinkage Approximator (UPSA) of Kelly et al. (2023) by making it more robust with respect to estimation noise and covariate shift. UPSA optimizes the realized Sharpe ratio using a relatively small calibration window, leveraging ridge penalties and cross-validation to yield better portfolios. Yet, it still suffers from the staggering amount of noise in financial data. We propose two methods to make UPSA more robust and improve its efficiency: time-averaging of the optimal penalty weights and using the Average Oracle correlation eigenvalues to make covariance matrices less noisy and more robust to covariate shift. Combining these two long-term averages outperforms UPSA by a large margin in most specifications.

Suggested Citation

  • Paul Ruelloux & Christian Bongiorno & Damien Challet, 2025. "Noise-proofing Universal Portfolio Shrinkage," Papers 2511.10478, arXiv.org.
    • Handle: RePEc:arx:papers:2511.10478
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    References listed on IDEAS

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    1. Christian Bongiorno & Damien Challet, 2024. "Covariance matrix filtering and portfolio optimisation: the average oracle vs non-linear shrinkage and all the variants of DCC-NLS," Quantitative Finance, Taylor & Francis Journals, vol. 24(9), pages 1227-1234, September.
    2. Christian Bongiorno & Damien Challet & Grégoire Loeper, 2023. "Filtering time-dependent covariance matrices using time-independent eigenvalues," Post-Print hal-03481441, HAL.
    3. Joël Bun & Jean-Philippe Bouchaud & Marc Potters, 2017. "Cleaning large correlation matrices: tools from random matrix theory," Post-Print hal-01491304, HAL.
    4. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
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