Estimation of Large Covariance Matrices with Mixed Factor Structures

Abstract

Abstract We extend the Principal Orthogonal complEment Thresholding (POET) framework by Fan, J., Y. Liao, M. Mincheva (2013) to estimate large covariance matrices with a “mixed” structure of observable and unobservable strong/weak factors, and we call this method the extended POET (ePOET). Especially, the weak factor structure allows the existence of much slowly divergent eigenvalues of the covariance matrix that are frequently observed in real data. Under some mild conditions, we derive the uniform consistency of the proposed estimator for the cases with or without observable factors. Furthermore, several simulation studies show that the ePOET achieves good finite-sample performance regardless of data with strong, weak, or mixed factors structure. Finally, we conduct empirical studies to present the practical usefulness of the ePOET.