Distance-Based Metrics: A Bayesian Solution to the Power and Extreme-Error Problems in Asset-Pricing Tests
Amit Goyal (),
Zhongzhi Lawrence He and
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Zhongzhi Lawrence He: Brock University, Goodman School of Business
Sahn-Wook Huh: State University of New York (SUNY) - Department of Finance
No 18-78, Swiss Finance Institute Research Paper Series from Swiss Finance Institute
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 (search for similar items in EconPapers)
JEL-codes: C11 G11 G12 (search for similar items in EconPapers)
Pages: 56 pages
New Economics Papers: this item is included in nep-ecm and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp1878
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