Distance-Based Metrics: A Bayesian Solution to the Power and Extreme-Error Problems in Asset-Pricing Tests
56 Pages Posted: 11 Dec 2018 Last revised: 9 Jan 2019
Date Written: November 17, 2018
We propose a unified set of distance-based performance metrics that address the power and extreme-error problems inherent in traditional measures for asset-pricing tests. From a Bayesian perspective, the distance metrics coherently incorporate both pricing errors and their standard errors. Measured in units of return, they have an economic interpretation as the minimum cost of holding a dogmatic belief in a model. Our metrics identify Fama and French (2015) factor model (augmented with the momentum factor and/or without the value factor) as the best model and thus highlight the importance of the momentum factor. In contrast, the traditional alpha-based statistics often lead to inconsistent and counter-intuitive model rankings.
Keywords: Asset-Pricing Tests, Power Problem, Extreme-Error Problem, Distance-Based Metrics, Optimal Transport Theory, Bayesian Interpretations, Model Comparisons and Rankings
JEL Classification: C11, G11, G12
Suggested Citation: Suggested Citation