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On Comparing Asset Pricing Models
Author(s) -
CHIB SIDDHARTHA,
ZENG XIAMING,
ZHAO LINGXIAO
Publication year - 2020
Publication title -
the journal of finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 18.151
H-Index - 299
eISSN - 1540-6261
pISSN - 0022-1082
DOI - 10.1111/jofi.12854
Subject(s) - prior probability , marginal likelihood , econometrics , asset (computer security) , bayesian probability , computer science , capital asset pricing model , marginal distribution , property (philosophy) , economics , mathematics , random variable , statistics , artificial intelligence , computer security , philosophy , epistemology
Revisiting the framework of (Barillas, Francisco, and Jay Shanken, 2018, Comparing asset pricing models, The Journal of Finance 73, 715–754). 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.