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

Wanbo Lu, Guanglin Huang () and Kris Boudt

Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium from Ghent University, Faculty of Economics and Business Administration

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.

Keywords: Higher-order multi-cumulants; High-dimensional factor models; Weak factors; Consistency; Eigenvalues (search for similar items in EconPapers)
JEL-codes: G11 G12 G15 (search for similar items in EconPapers)
Pages: 88 pages
Date: 2024-03
New Economics Papers: this item is included in nep-ecm and nep-mac
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Persistent link: https://EconPapers.repec.org/RePEc:rug:rugwps:24/1085

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