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

Abstract

We estimate the latent factors in high-dimensional panel non-Gaussian data using Higher-order multi-cumulant Factor Analysis (HFA). HFA consists of an eigenvalue ratio test to select the number of non-Gaussian factors and uses alternating regressions to estimate both Gaussian and non-Gaussian factors. In contrast with covariance-based approaches, HFA remains reliable for estimating the non-Gaussian factors in weak factor models. Simulation results confirm that HFA estimators improve the accuracy of factor selection and factor estimation as compared to covariance-based approaches. The empirical use of the HFA approach is shown to detect and estimate the weak factors in a FRED-MD data set and to forecast the S&P 500 equity premium.