Estimation of factors using higher-order multi-cumulants in weak factor models

Bibliographic Data

ID19418326
AuthorsGuanglin Huang (0000-0001-5902-5681, Southwestern University of Finance and Economics), Wanbo Lu (0000-0002-4220-0276, Southwestern University of Finance and Economics, corresponding author), Kris Boudt (0000-0002-1000-5142, Ghent University)
Year2026
Pages1-31
Publication date2026-05-08
Open AccessNo
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.2026.2671240
OpenAlexW7160773246
LanguageEN
References cited38

When factors are weak, covariance-based factor analysis methods tend to exhibit poor performance. To address this issue in the case of non-Gaussian data, we propose a new method called Higher-order multi-cumulant Factor Analysis (HFA). HFA estimates factors and factor loadings via the eigenvalue decomposition of the product of a higher-order multi-cumulant matrix and its transpose. We derive the asymptotic properties of HFA under a weak factor model where non-Gaussianity originates solely from the latent factors, while idiosyncratic errors remain Gaussian. Simulation studies demonstrate that HFA significantly improves both factor selection and estimation when factors are weak and non-Gaussian, compared with traditional methods. Applied to the FRED-MD dataset, HFA identifies factors that improve out-of-sample forecasting performance for the S&P 500 monthly equity premium

Estimation · Estimation theory · Factor analysis · Principal component analysis · Bayesian Methods and Mixture Models · Statistical Methods and Bayesian Inference · Tensor decomposition and applications

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