Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models
84 Pages Posted: 4 Dec 2019 Last revised: 22 Sep 2021
Date Written: November 18, 2019
We propose a novel framework for analyzing linear asset pricing models: simple, robust, and applicable to high dimensional problems. For a (potentially misspecified) standalone model, it provides reliable price of risk estimates for both tradable and non-tradable factors, and detects those weakly identified. For competing factors and (possibly non-nested) models, the method automatically selects the best specification -- if a dominant one exists -- or provides a Bayesian model averaging (BMA-SDF), if there is no clear winner. We analyze 2.25 quadrillion models generated by a large set of factors, and find that the BMA-SDF outperforms existing models in- and out-of-sample.
Keywords: Cross-Sectional Asset Pricing, Factor Models, Model Evaluation, Multiple Testing, Data Mining, P-Hacking, Bayesian Methods, shrinkage, SDF.
JEL Classification: G12, C11, C12, C52, C58
Suggested Citation: Suggested Citation