Comparing Asset Pricing Models with Non-Traded Factors and Principal Components

74 Pages Posted: 23 Sep 2020

See all articles by Rohit Allena

Rohit Allena

C.T. Bauer College of Business, University of Houston

Date Written: August 6, 2020

Abstract

This paper develops a Bayesian methodology to compare asset pricing models containing non-traded factors and principal components. Existing comparison procedures are inadequate when models include such factors due to estimation uncertainties in mimicking portfolios and return covariances. Furthermore, regressions of test assets on such factors are interdependent, rendering comparisons with recently proposed priors sensitive to subsets of the test assets. Thus, I derive novel, non-informative priors that deliver invariant inferences. Simulations suggest that my methodology substantially outperforms existing methods in identifying true non-traded models. I find that macroeconomic models dominate several, recent benchmark models with traded factors and principal components.

Keywords: Macroeconomic Factors, Principal Components, Novel Non-Informative Priors, Conditional Test Assets Irrelevance, Invariant Comparisons, Out-of-Sample Model Comparisons

JEL Classification: G12, G11, C11, C12, C52, C58

Suggested Citation

Allena, Rohit, Comparing Asset Pricing Models with Non-Traded Factors and Principal Components (August 6, 2020). Available at SSRN: https://ssrn.com/abstract=3669940 or http://dx.doi.org/10.2139/ssrn.3669940

Rohit Allena (Contact Author)

C.T. Bauer College of Business, University of Houston ( email )

Houston, TX 77204
United States

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