Title: Forecast US bond risk premia with partially observed factors
Authors: Jianqing Fan - Princeton University (United States)
Yuan Ke - University of Georgia (United States) [presenting]
Yuan Liao - Rutgers University (United States)
Abstract: The forecast of US bond risk premia with factor models is studied when the latent factors can be partially explained by observed covariates. With those covariates, both the factors and loadings are identifiable up to a rotation matrix even only with a finite dimension. To incorporate the explanatory power of these covariates, we propose a smoothed principal component analysis (PCA): (i) regress the data onto the observed covariates, and (ii) take the principal components of the fitted data to estimate the loadings and factors. We show that both the estimated factors and loadings can be estimated with improved rates of convergence compared to the benchmark method. The degree of improvement depends on the strength of the signals, representing the explanatory power of the covariates on the factors. We can also accurately estimate the percentage of unexplained components in factors. The proposed estimator is robust to possibly heavy-tailed distributions, which are encountered in many high-dimensional applications for factor analysis.