The paper “Estimation of factors using higher-order multi-cumulants in weak factor models”, co-authored by Dr. Guanglin Huang of the team, Prof. Wanbo Lu of the Southwestern University of Finance and Economics, China, Prof. Kris Boudt of Ghent University, Belgium, has been officially accepted by the Journal of Business & Economic Statistics, one of the leading international journals in the field of statistics.
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 Introduction
Guanglin Huang is a postdoctoral researcher at the Joint Laboratory of Data Science and Business Intelligence, Southwestern University of Finance and Economics. His main research interests include time series analysis and financial risk management.
Wanbo Lu is a Professor at Southwestern University of Finance and Economics. His research interests include financial time series analysis and econometric theory.
Kris Boudt is a Professor at Ghent University. His research interests include financial econometrics, high-dimensional data analysis, risk management, and quantitative investment methods.
