---
title: Covariance Matrix Shrinkage
url: https://www.ml-quant.com/papers/repec/eee-ecmode-v-144-y-2025-i-c-s0264999324003389/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:eee:ecmode:v:144:y:2025:i:c:s0264999324003389
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0264999324003389%3Bh%3Drepec%3Aeee%3Aecmode%3Av%3A144%3Ay%3A2025%3Ai%3Ac%3As0264999324003389
featured: 2025-02-05
citations: unknown
topic: Portfolio & Allocation
---


# Covariance Matrix Shrinkage

The study suggests an optimal shrinkage intensity selection for the linear shrinkage estimator family, which results in more stable covariance matrix estimators and improves global minimum-variance portfolios.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0264999324003389%3Bh%3Drepec%3Aeee%3Aecmode%3Av%3A144%3Ay%3A2025%3Ai%3Ac%3As0264999324003389
- Identifier: RePEc:eee:ecmode:v:144:y:2025:i:c:s0264999324003389
- Released: 2025-02-05
- First featured: Quant Letter No. 84 (2025-02-05): https://www.ml-quant.com/issues/2025-02-05/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Portfolio & Allocation

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