Filtering returns for unspecified biases in priors when testing asset pricing theory

Procedures are presented that allow the empiricist to estimate and test asset pricing models on limited-liability securities without the assumption that the historical payoff distribution provides a consistent estimate of the market's prior beliefs. The procedures effectively filter return data for unspecified historical biases in the market's priors. They do not involve explicit estimation of the market's priors, and hence, economize on parameters. The procedures derive from a new but simple property of Bayesian learning, namely: if the correct likelihood is used, the inverse posterior at the true parameter value forms a martingale process relative to the learner's information filtration augmented with the true parameter value. Application of this central result to tests of asset pricing models requires a deliberate selection bias. Hence, as a by-product, the article establishes that biased samples contain information with which to falsify an asset pricing model or estimate its parameters. These include samples subject to, e.g. survivorship bias or Peso problems.

Published in:
Review of Economic Studies, 71, 1, 63-86
California Institute of Technology, CEPR, Pasadena, CA 91125, United States
Cited By (since 1996): 1
Export Date: 10 March 2008
Source: Scopus
Other identifiers:
Scopus: 2-s2.0-0842269287

 Record created 2008-03-12, last modified 2018-03-17

Rate this document:

Rate this document:
(Not yet reviewed)