Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models
提出一个简单稳健的贝叶斯框架，用于分析高维线性资产定价模型，能处理不可交易因子和弱识别问题，并自动选择最优模型或给出贝叶斯模型平均随机贴现因子。
ABSTRACT We propose a novel framework for analyzing linear asset pricing models: simple, robust, and applicable to high‐dimensional problems. For a (potentially misspecified) stand‐alone model, it provides reliable price of risk estimates for both tradable and nontradable factors, and detects those weakly identified. For competing factors and (possibly nonnested) models, the method automatically selects the best specification— if a dominant one exists—or provides a Bayesian model averaging–stochastic discount factor (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.