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Filtering Returns for Unspecified Biases in Priors when Testing Asset PricingTheory

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  • Peter Bossaerts

Abstract

Procedures are presented that allow the empiricist to estimate and test asset pricing models on limited-liability securities without the assumption that thehistorical payoff distribution provides a consistent estimate of the market's priorbeliefs. 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. Copyright The Review of Economic Studies Limited, 2004.

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Article provided by Wiley Blackwell in its journal Review of Economic Studies.

Volume (Year): 71 (2004)
Issue (Month): 1 (01)
Pages: 63-86

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Handle: RePEc:bla:restud:v:71:y:2004:i:1:p:63-86

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Cited by:
  1. Klaus Adam & Albert Marcet & Juan Pablo Nicolini, 2013. "Stock Market Volatility and Learning," Working Papers 336, Barcelona Graduate School of Economics.
  2. Chen, Anlin & Chiou, Sue L. & Wu, Chinshun, 2004. "Efficient learning under price limits: evidence from IPOs in Taiwan," Economics Letters, Elsevier, vol. 85(3), pages 373-378, December.
  3. Cogley, Timothy & Sargent, Thomas J., 2008. "The market price of risk and the equity premium: A legacy of the Great Depression?," Journal of Monetary Economics, Elsevier, vol. 55(3), pages 454-476, April.

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