In Search of Sparsity: Bayesian Sparse Factor Models and the Factor Zoo (JMP)

with Fahiz Baba-Yara (Indiana University) and Massimiliano Bondatti (CUNEF)

We develop a Bayesian Sparse Factor Model for asset pricing in factor zoo environments with correlated and weak factors. Observed factors are modeled as noisy measurements of latent priced risks, with hierarchical spike-and-slab priors on loadings to induce sparsity and quantify uncertainty about the factor structure. We embed the extracted factors in a Bayesian two-pass pricing framework that delivers inference on risk premia, pricing errors, and the implied stochastic discount factor (SDF). Empirically, three sparse latent factors summarize the zoo, remain competitive on the Bryzgalova, Huang and Julliard (2022) benchmark test sets, and produce tighter posteriors and a more stable SDF.

Panel Thresholds Predictive Regressions: An Application to Emerging Market Exchange Rates

with Paulo Rodrigues (Bank of Portugal)

slides

A methodology is proposed for identifying threshold effects in panel predictive regressions in which the predictor variables are strongly persistent. Our statistic of interest is the average of individual supremum type statistics across the units of the panel. In certain cases, this statistic converges to a standard distribution while in other cases we show that a block bootstrap methodology can be applied and offers good finite sample characteristics when cross sectional dependence and serial correlation are present. This methodology is applied to assess the existence of threshold effects in an exchange rate predictability setting for emerging Sub-Saharan African economies during the post-2008 crisis period.