Learning the Shrinkage Intensity: A Data-Driven Approach for Risk-Optimized Portfolios.
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...
| Publicado en: | Journal of Financial Econometrics Vol. 24; no. 2; pp. 1 - 31 |
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| Autores principales: | , |
| Formato: | Artículo |
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Oxford University Press / USA
2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=192849867&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 192849867 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 14798409 T2Y jtl: Journal of Financial Econometrics issn: 14798409 maglogo: N pubinfo: dt: 2026 vid: 24 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 192849867 10.1093/jjfinec/nbag002 ppf: 1 ppct: 30 formats: tig: atl: Learning the Shrinkage Intensity: A Data-Driven Approach for Risk-Optimized Portfolios. aug: au: Nard, Gianluca De Kostovic, Damjan affil: Liechtenstein Business School, University of Liechtenstein, Vaduz, LiechtensteinDepartment of Economics, University of Zurich, Zurich, SwitzerlandZürcher Kantonalbank, Zurich, Switzerland OLZ AG, Zurich, Switzerland su: Portfolio management (Investments) Supervised learning Algorithms Financial markets Multi-armed bandit problem (Probability theory) Investment policy sug: subj: Portfolio Management Investment Banking and Securities Dealing Securities and Commodity Exchanges Portfolio management (Investments) Supervised learning Algorithms Financial markets Multi-armed bandit problem (Probability theory) Investment policy keyword: C13 C58 copyrightHolder:Oxford University Press copyrightYear:2026 covariance matrix estimation G11 inLanguage:en linear and nonlinear shrinkage policy learning portfolio management publisher:Oxford University Press reinforcement learning risk optimization sameAs:https://dx.doi.org/10.1093/jjfinec/nbag002 C13 C58 copyrightHolder:Oxford University Press copyrightYear:2026 covariance matrix estimation G11 inLanguage:en linear and nonlinear shrinkage policy learning portfolio management publisher:Oxford University Press reinforcement learning risk optimization sameAs:https://dx.doi.org/10.1093/jjfinec/nbag002 ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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