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Estimation of Non-Gaussian Factors Using Higher-order Multi-cumulants in Weak Factor Models

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  • Wanbo Lu
  • Guanglin Huang
  • Kris Boudt

Abstract

We estimate the latent factors in high-dimensional non-Gaussian panel data using the eigenvalue decomposition of the product between the higher-order multi-cumulant and its transpose. The proposed Higher order multi-cumulant Factor Analysis (HFA) approach comprises an eigenvalue ratio test to select the number of non-Gaussian factors and uses the eigenvector to estimate the factor loadings. Unlike covariance-based approaches, HFA remains reliable for estimating the nonGaussian factors in weak factor models with Gaussian error terms. Simulation results confirm that HFA estimators improve the accuracy of factor selection and estimation compared to covariancebased approaches. We illustrate the use of HFA to detect and estimate the factors for the FREDMD data set and use them to forecast the monthly S&P 500 equity premium.

Suggested Citation

  • Wanbo Lu & Guanglin Huang & Kris Boudt, 2024. "Estimation of Non-Gaussian Factors Using Higher-order Multi-cumulants in Weak Factor Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 24/1085, Ghent University, Faculty of Economics and Business Administration.
    • Handle: RePEc:rug:rugwps:24/1085
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    References listed on IDEAS

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    More about this item

    Keywords

    Higher-order multi-cumulants; High-dimensional factor models; Weak factors; Consistency; Eigenvalues;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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