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Covariance matrix filtering and portfolio optimisation: the average oracle vs non-linear shrinkage and all the variants of DCC-NLS

Author

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  • Christian Bongiorno
  • Damien Challet

Abstract

The Average Oracle, a simple and very fast covariance filtering method, is shown to yield superior Sharpe ratios than the current state-of-the-art (and complex) methods, Dynamic Conditional Covariance coupled to Non-Linear Shrinkage (DCC-NLS). We pit all the known variants of DCC-NLS (quadratic shrinkage, gross-leverage or turnover limitations, and factor-augmented NLS) against the Average Oracle in large-scale randomized experiments. We find generically that while some variants of DCC-NLS sometimes yield the lowest average realized volatility, albeit with a small improvement, their excessive gross leverage and investment concentration, and their 10-time larger turnover contribute to smaller average portfolio returns, which mechanically result in smaller realized Sharpe ratios than the Average Oracle. We also provide simple analytical arguments about the origin of the advantage of the Average Oracle over NLS in a changing world.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:quantf:v:24:y:2024:i:9:p:1227-1234
    DOI: 10.1080/14697688.2024.2372053
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    Cited by:

    1. Christian Bongiorno & Efstratios Manolakis & Rosario Nunzio Mantegna, 2025. "End-to-End Large Portfolio Optimization for Variance Minimization with Neural Networks through Covariance Cleaning," Papers 2507.01918, arXiv.org, revised Jul 2025.
    2. JD Opdyke, 2025. "Beyond Correlation: Positive Definite Dependence Measures for Robust Inference, Flexible Scenarios, and Causal Modeling for Financial Portfolios," Papers 2504.15268, arXiv.org, revised Jan 2026.
    3. Paul Ruelloux & Christian Bongiorno & Damien Challet, 2025. "Noise-proofing Universal Portfolio Shrinkage," Papers 2511.10478, arXiv.org.

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