Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/322286 
Erscheinungsjahr: 
2025
Schriftenreihe/Nr.: 
Working Paper No. 470
Verlag: 
University of Zurich, Department of Economics, Zurich
Zusammenfassung: 
The paper introduces a new type of shrinkage estimation that is not based on asymptotic optimality but uses artificial intelligence (AI) techniques to shrink the sample eigenvalues. The proposed AI Shrinkage estimator applies to both linear and nonlinear shrinkage, demonstrating improved performance compared to the classic shrinkage estimators. Our results demonstrate that reinforcement learning solutions identify a downward bias in classic shrinkage intensity estimates derived under the i.i.d. assumption and automatically correct 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 various optimization constraints.
Schlagwörter: 
Covariance matrix estimation
linear and nonlinear shrinkage
portfolio management reinforcement learning
risk optimization
JEL: 
C13
C58
G11
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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