Abstract

In my dissertation, I focus on theoretical and empirical asset pricing from a Bayesian model comparison perspective. In the first Chapter, revisiting the framework of Barillas and Shanken (2018), BS henceforth, we show that the Bayesian marginal likelihood-based model comparison method in that paper is unsound: the priors on the nuisance parameters across models must satisfy a change of variable property for densities that is violated by the Jeffreys priors used in the BS method. Extensive simulation exercises confirm that the BS method performs unsatisfactorily. We derive a new class of improper priors on the nuisance parameters, starting from a single improper prior, which leads to valid marginal likelihoods and model comparisons. The performance of our marginal likelihoods is significantly better, allowing for reliable Bayesian work on which factors are risk factors in asset pricing models. In the second Chapter, starting from the twelve distinct risk factors in four well-established asset pricing models, a pool we refer to as the winners, we construct and compare 4,095 asset pricing models and find that the model with the risk factors, Mkt, SMB, MOM, ROE, MGMT, and PEAD, performs the best in terms of Bayesian posterior probability, out-of-sample predictability, and Sharpe ratio. A more extensive model comparison of 8,388,607 models, constructed from the twelve winners plus eleven principal components of anomalies unexplained by the winners, shows the benefit of incorporating information in genuine anomalies in explaining the cross-section of expected equity returns.

Committee Chair

Siddhartha Chib

Committee Members

Gaetano Antinolfi, John Nachbar, Guofu Zhou,

Degree

Doctor of Philosophy (PhD)

Author's Department

Economics

Author's School

Graduate School of Arts and Sciences

Document Type

Dissertation

Date of Award

Spring 5-15-2020

Language

English (en)

Author's ORCID

http://orcid.org/0000-0002-8016-072X

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Economics Commons

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