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Hidden noise structure and random matrix models of stock correlations


  • Ivailo I. Dimov
  • Petter N. Kolm
  • Lee Maclin
  • Dan Y. C. Shiber


We find a novel correlation structure in the residual noise of stock market returns that is remarkably linked to the composition and stability of the top few significant factors driving the returns, and, moreover, indicates that the noise band is composed of multiple sub-bands that do not fully mix. Our findings allow us to construct effective generalized random matrix theory market models that are closely related to correlation and eigenvector clustering. We show how to use these models in a simulation that incorporates heavy tails. Finally, we demonstrate how a subtle purely stationary risk estimation bias can arise in the conventional cleaning prescription.

Suggested Citation

  • Ivailo I. Dimov & Petter N. Kolm & Lee Maclin & Dan Y. C. Shiber, 2012. "Hidden noise structure and random matrix models of stock correlations," Quantitative Finance, Taylor & Francis Journals, vol. 12(4), pages 567-572, November.
  • Handle: RePEc:taf:quantf:v:12:y:2012:i:4:p:567-572
    DOI: 10.1080/14697688.2012.664931

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    Cited by:

    1. Joel Bun & Jean-Philippe Bouchaud & Marc Potters, 2016. "Cleaning large correlation matrices: tools from random matrix theory," Papers 1610.08104,
    2. Joongyeub Yeo & George Papanicolaou, 2016. "Random matrix approach to estimation of high-dimensional factor models," Papers 1611.05571,, revised Nov 2017.

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