State-Varying Factor Models of Large Dimensions

By Markus Pelger, Ruoxuan Xiong. Journal of business & economic statistics, 2018

51 citations recorded by the source corpus.

Abstract This article develops an inferential theory for state-varying factor models of large dimensions. Unlike constant factor models, loadings are general functions of some recurrent state process. We develop an estimator for the latent factors and state-varying loadings under a large cross-section and time dimension. Our estimator combines nonparametric methods with principal component analysis. We derive the rate of convergence and limiting normal distribution for the factors, loadings, and common components. In addition, we develop a statistical test for a change in the factor structure in different states. We apply the estimator to the U.S. Treasury yields and S&P500 stock returns. The systematic factor structure in treasury yields differs in times of booms and recessions as well as in periods of high market volatility. State-varying factors based on the VIX capture significantly more variation and pricing information in individual stocks than constant factor models.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/edd174ad8aac19a88b133f56fc9be58438ee5cf5

Citations in the corpus

• Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference (2019, unknown): “…factor model to a time-varying loading model by using a projection in the cross-section dimension (Fan et al., 2016; Kelly et al., 2018), a local window or high frequency approach (Pelger, 2019; Aı̈t-Sahalia and Xiu, 2018) or a kernel projection in the time dimension (Pelger and Xiong, 2018).”