Characterizing Correlation Matrices that Admit a Clustered Factor Representation

Chen Tong; Peter Reinhard  Hansen

doi:10.1016/j.econlet.2023.111433

Characterizing Correlation Matrices that Admit a Clustered Factor Representation

Chen Tong, Peter Reinhard Hansen^*

^*Corresponding author for this work

Department of Finance

Research output: Contribution to journal › Journal article › Research › peer-review

Abstract

The Clustered Factor (CF) model is commonly used to parametrize block correlation matrices. We show that the CF model imposes additional superfluous restrictions. This can be avoided by a different parametrization, based on the logarithmic block correlation matrix.

Original language	English
Article number	111433
Journal	Economics Letters
Volume	233
Number of pages	4
ISSN	0165-1765
DOIs	https://doi.org/10.1016/j.econlet.2023.111433
Publication status	Published - Dec 2023

Keywords

Block correlation matrix
Copula
Clustering
Factor models

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Cite this

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title = "Characterizing Correlation Matrices that Admit a Clustered Factor Representation",

abstract = "The Clustered Factor (CF) model is commonly used to parametrize block correlation matrices. We show that the CF model imposes additional superfluous restrictions. This can be avoided by a different parametrization, based on the logarithmic block correlation matrix.",

keywords = "Block correlation matrix, Copula, Clustering, Factor models, Block correlation matrix, Copula, Clustering, Factor models",

author = "Chen Tong and Hansen, {Peter Reinhard}",

year = "2023",

month = dec,

doi = "10.1016/j.econlet.2023.111433",

language = "English",

volume = "233",

journal = "Economics Letters",

issn = "0165-1765",

publisher = "Elsevier",

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TY - JOUR

T1 - Characterizing Correlation Matrices that Admit a Clustered Factor Representation

AU - Tong, Chen

AU - Hansen, Peter Reinhard

PY - 2023/12

Y1 - 2023/12

N2 - The Clustered Factor (CF) model is commonly used to parametrize block correlation matrices. We show that the CF model imposes additional superfluous restrictions. This can be avoided by a different parametrization, based on the logarithmic block correlation matrix.

AB - The Clustered Factor (CF) model is commonly used to parametrize block correlation matrices. We show that the CF model imposes additional superfluous restrictions. This can be avoided by a different parametrization, based on the logarithmic block correlation matrix.

KW - Block correlation matrix

KW - Copula

KW - Clustering

KW - Factor models

KW - Block correlation matrix

KW - Copula

KW - Clustering

KW - Factor models

U2 - 10.1016/j.econlet.2023.111433

DO - 10.1016/j.econlet.2023.111433

M3 - Journal article

SN - 0165-1765

VL - 233

JO - Economics Letters

JF - Economics Letters

M1 - 111433

ER -