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A new method for generating random correlation matrices

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  • Ilya Archakov
  • Peter Reinhard Hansen
  • Yiyao Luo

Abstract

SummaryWe 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, , which maps any distribution onto a distribution on the space of nonsingularcorrelation 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.

Suggested Citation

  • Ilya Archakov & Peter Reinhard Hansen & Yiyao Luo, 2024. "A new method for generating random correlation matrices," The Econometrics Journal, Royal Economic Society, vol. 27(2), pages 188-212.
    • Handle: RePEc:oup:emjrnl:v:27:y:2024:i:2:p:188-212.
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    File URL: http://hdl.handle.net/10.1093/ectj/utad027
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    References listed on IDEAS

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    1. Ilya Archakov & Peter Reinhard Hansen, 2021. "A New Parametrization of Correlation Matrices," Econometrica, Econometric Society, vol. 89(4), pages 1699-1715, July.
    2. Barndorff-Nielsen, O. & Schou, G., 1973. "On the parametrization of autoregressive models by partial autocorrelations," Journal of Multivariate Analysis, Elsevier, vol. 3(4), pages 408-419, December.
    3. Joe, Harry, 2006. "Generating random correlation matrices based on partial correlations," Journal of Multivariate Analysis, Elsevier, vol. 97(10), pages 2177-2189, November.
    4. Linton, Oliver & McCrorie, J. Roderick, 1995. "Differentiation of an Exponential Matrix Function," Econometric Theory, Cambridge University Press, vol. 11(05), pages 1182-1185, October.
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    Cited by:

    1. Chen Tong & Peter Reinhard Hansen & Ilya Archakov, 2024. "Cluster GARCH," Papers 2406.06860, arXiv.org.

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