Feasible Weighted Projected Principal Component Analysis for Factor Models with an Application to Bond Risk Premia

TL;DR

This paper introduces a new weighted projected principal component analysis method for factor models with observable characteristics, improving estimation efficiency and accuracy in bond risk premia forecasting.

Contribution

The paper proposes a feasible weighted projected PCA method that accounts for cross-sectional dependence and heteroskedasticity, enhancing factor and loading estimation in high-dimensional models.

Findings

FPPC estimators have faster convergence rates.

The model outperforms conventional PCA in bond return forecasting.

The approach compares favorably with machine learning techniques.

Abstract

I develop a feasible weighted projected principal component (FPPC) analysis for factor models in which observable characteristics partially explain the latent factors. This novel method provides more efficient and accurate estimators than existing methods. To increase estimation efficiency, I take into account both cross-sectional dependence and heteroskedasticity by using a consistent estimator of the inverse error covariance matrix as the weight matrix. To improve accuracy, I employ a projection approach using characteristics because it removes noise components in high-dimensional factor analysis. By using the FPPC method, estimators of the factors and loadings have faster rates of convergence than those of the conventional factor analysis. Moreover, I propose an FPPC-based diffusion index forecasting model. The limiting distribution of the parameter estimates and the rate of convergence for forecast errors are obtained. Using U.S. bond market and macroeconomic data, I demonstrate that the proposed model outperforms models based on conventional principal component estimators. I also show that the proposed model performs well among a large group of machine learning techniques in forecasting excess bond returns.