Learning the shrinkage intensity: a data-driven approach for risk-optimized portfolios

Gianluca De Nard and Damjan Kostovic

No 470, ECON - Working Papers from Department of Economics - University of Zurich

Abstract: We introduce a new type of shrinkage estimator that is not based on asymptotic optimality, but instead learns a state-dependent shrinkage policy via supervised learning in a contextual bandit setup. The proposed estimator applies to both linear and nonlinear shrinkage and shows improved performance compared to classical shrinkage estimators. Our results demonstrate that our estimator identifies a downward bias in classical shrinkage intensity estimates derived under the i.i.d. assumption and automatically corrects for it in response to prevailing market conditions. Additionally, our data-driven approach enables more efficient implementation of risk-optimized portfolios and is well-suited for real-world investment applications, including portfolios with practical optimization constraints.

Keywords: Covariance matrix estimation; linear and nonlinear shrinkage; policy learning; portfolio management; reinforcement learning; risk optimization (search for similar items in EconPapers)
JEL-codes: C13 C58 G11 (search for similar items in EconPapers)
Date: 2025-05, Revised 2025-11
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp, nep-ecm and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:zur:econwp:470

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