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

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Authors

Ludwig, Alexander
Zimper, Alexander

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Publisher

Elsevier

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%. This is 1 −25% closer to the empirical risk-free rate than according calibrations of the rational expectations model.

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Keywords

Ambiguity, Non-additive probability measures, Bayesian learning, Truncated normal distribution, Risk-free rate puzzle

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Citation

Ludwig, A & Zimper, A 2014, 'Biased Bayesian learning with an application to the risk-free rate puzzle', Journal of Economic Dynamics and Control, vol. 39, pp. 79-97.