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Shrinkage=factor model

Author

Listed:
  • Zura Kakushadze

    (Quantigic® Solutions LLC)

Abstract

Shrunk sample covariance matrix is a factor model of a special form combining some (typically, style) risk factor(s) and principal components with a (block-)diagonal factor covariance matrix. As such, shrinkage, which essentially inherits out-of-sample instabilities of the sample covariance matrix, is not an alternative to multifactor risk models but one out of myriad possible regularization schemes. We give an example of a scheme designed to be less prone to said instabilities. We contextualize this within multifactor models.

Suggested Citation

  • Zura Kakushadze, 2016. "Shrinkage=factor model," Journal of Asset Management, Palgrave Macmillan, vol. 17(2), pages 69-72, March.
    • Handle: RePEc:pal:assmgt:v:17:y:2016:i:2:d:10.1057_jam.2015.40
    DOI: 10.1057/jam.2015.40
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    Citations

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    Cited by:

    1. Zura Kakushadze & Willie Yu, 2017. "How to combine a billion alphas," Journal of Asset Management, Palgrave Macmillan, vol. 18(1), pages 64-80, January.
    2. Zura Kakushadze & Willie Yu, 2021. "ETF Risk Models," Papers 2110.07138, arXiv.org.
    3. Zura Kakushadze, 2020. "Quant Bust 2020," Papers 2006.05632, arXiv.org.
    4. Zura Kakushadze & Willie Yu, 2019. "Machine Learning Risk Models," Papers 1903.06334, arXiv.org, revised Apr 2019.

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