Assets / Covariance Matrix Estimation
Clustering-Based Linearly Shrunk Empirical Covariance Matrix
Compute a clustering-based linearly shrunk empirical asset covariance matrix, which is a convex combination of the empirical covariance matrix of these assets' and a clustering-based target covariance matrix.
The available clustering-based target covariance matrices are the same as those available in the endpoint /assets/covariance/matrix/estimation/empirical/shrunk, extended to a clustering-based context.
References
- Olivier Ledoit, Michael Wolf, The Power of (Non-)Linear Shrinking: A Review and Guide to Covariance Matrix Estimation, Journal of Financial Econometrics, Volume 20, Issue 1, Winter 2022, Pages 187–218
- Gianluca De Nard, Oops! I Shrunk the Sample Covariance Matrix Again: Blockbuster Meets Shrinkage, Journal of Financial Econometrics, Volume 20, Issue 4, Fall 2022, Pages 569–611
- O. Ledoit, M. Wolf, Honey, I Shrunk the Sample Covariance Matrix, The Journal of Portfolio Management Summer 2004, 30 (4) 110-119
- Schafer J, Strimmer K. A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Stat Appl Genet Mol Biol. 2005;4:Article32
- Guillaume Becquin and Saher Esmeir. 2023. Semantic Similarity Covariance Matrix Shrinkage. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 9977–9992, Singapore. Association for Computational Linguistics
post/assets/covariance/matrix/estimation/empirical/shrunk/clustering-based
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