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Title: A novel approach to denoising correlation matrices with applications to global portfolio management with a large number of assets
Author: Lakshtanov, Evgeny
Molyboga, Marat
Issue Date: 1-Mar-2023
Publisher: IOP Publishing
Abstract: We introduce a new approach to denoising correlation matrices that imposes a block structure with a fixed block-dependent pair-wise correlation within each block and a constant correlation specified for each pair of blocks. We characterize the eigenvalue spectrum and modify the Marchenko-Pastur distribution of eigenvalues. We present approximate analytic solutions for the inverse problem of determining the block sizes and the correlation parameters under a broad set of assumptions. Our solution is based on a novel unsupervised approach for improving correlation matrix estimation by a functional transformation of the original data rather than popular shrinkage estimation or standard random-matrix-theory-based techniques. However, a correlation matrix produced by our method can serve as an attractive target correlation matrix within shrinkage frameworks. Our correlation matrix denoising method has broad applications for global portfolio management with a large number of assets from many diverse asset classes.
Peer review: yes
DOI: 10.1088/1402-4896/acc2f3
ISSN: 0031-8949
Appears in Collections:CIDMA - Artigos
OGTCG - Artigos

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