Estimation of factors using higher-order multi-cumulants in weak factor models
- Author
- Guanglin Huang, Wanbo Lu and Kris Boudt (UGent)
- Organization
- Project
- Abstract
- 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.
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01KR49ZHMQNSREQERTMAR2N5DM
- MLA
- Huang, Guanglin, et al. “Estimation of Factors Using Higher-Order Multi-Cumulants in Weak Factor Models.” JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 2026, doi:10.1080/07350015.2026.2671240.
- APA
- Huang, G., Lu, W., & Boudt, K. (2026). Estimation of factors using higher-order multi-cumulants in weak factor models. JOURNAL OF BUSINESS & ECONOMIC STATISTICS. https://doi.org/10.1080/07350015.2026.2671240
- Chicago author-date
- Huang, Guanglin, Wanbo Lu, and Kris Boudt. 2026. “Estimation of Factors Using Higher-Order Multi-Cumulants in Weak Factor Models.” JOURNAL OF BUSINESS & ECONOMIC STATISTICS. https://doi.org/10.1080/07350015.2026.2671240.
- Chicago author-date (all authors)
- Huang, Guanglin, Wanbo Lu, and Kris Boudt. 2026. “Estimation of Factors Using Higher-Order Multi-Cumulants in Weak Factor Models.” JOURNAL OF BUSINESS & ECONOMIC STATISTICS. doi:10.1080/07350015.2026.2671240.
- Vancouver
- 1.Huang G, Lu W, Boudt K. Estimation of factors using higher-order multi-cumulants in weak factor models. JOURNAL OF BUSINESS & ECONOMIC STATISTICS. 2026;
- IEEE
- [1]G. Huang, W. Lu, and K. Boudt, “Estimation of factors using higher-order multi-cumulants in weak factor models,” JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 2026.
@article{01KR49ZHMQNSREQERTMAR2N5DM,
abstract = {{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.}},
author = {{Huang, Guanglin and Lu, Wanbo and Boudt, Kris}},
issn = {{0735-0015}},
journal = {{JOURNAL OF BUSINESS & ECONOMIC STATISTICS}},
language = {{eng}},
title = {{Estimation of factors using higher-order multi-cumulants in weak factor models}},
url = {{http://doi.org/10.1080/07350015.2026.2671240}},
year = {{2026}},
}
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