Biased Bayesian learning with an application to the risk-free rate puzzle

Alexander Ludwig and Alexander Zimper

No 201366, Working Papers from University of Pretoria, Department of Economics

Abstract: Based on the axiomatic framework of Choquet decision theory, we develop a closed-form model of Bayesian learning with ambiguous beliefs about the mean of a normal distribution. In contrast to rational models of Bayesian learning the resulting Choquet Bayesian estimator results in a long-run bias that reflects the agent's ambiguity attitudes. By calibrating the standard equilibrium conditions of the consumption based asset pricing model we illustrate that our approach contributes towards a resolution of the risk-free rate puzzle. For a plausible parameterization we obtain a risk-free rate in the range of 3.5-5 percent. This is 1-2.5 percent closer to the empirical risk-free rate than according calibrations of the rational expectations model.

Keywords: Ambiguity; Non-additive probability measures; Bayesian learning; Truncated normal distribution; Risk-free rate puzzle (search for similar items in EconPapers)
JEL-codes: C79 D83 (search for similar items in EconPapers)
Pages: 45 pages
Date: 2013-11
New Economics Papers: this item is included in nep-upt
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Biased Bayesian learning with an application to the risk-free rate puzzle (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:pre:wpaper:201366

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