A New Method for Generating Random Correlation Matrices

Ilya Archakov, Peter Reinhard Hansen*, Yiyao Luo

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review


We propose a new method for generating random correlation matrices that makes it simple to control both location and dispersion. The method is based on a vector parameterization, γ = g(C), which maps any distribution on R n(n−1)/2 to a distribution on the space of non-singular n × n correlation matrices. Correlation matrices with certain properties, such as being well-conditioned, having block structures, and having strictly positive elements, are simple to generate. We compare the new method with existing methods.
Original languageEnglish
Article numberutad027
JournalThe Econometrics Journal
Issue number2
Pages (from-to)188-212
Number of pages25
Publication statusPublished - May 2024

Bibliographical note

Published online: 21 December 2023.


  • Random correlation matrix
  • Fisher transformation
  • Covariance modeling

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