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Biased Bayesian Learning with an Application to the Risk-Free Rate Puzzle

  • Alexander Ludwig and Alexander Zimper

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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Paper provided by Economic Research Southern Africa in its series Working Papers with number 390.

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Length: 46 pages
Date of creation: 2013
Date of revision:
Handle: RePEc:rza:wpaper:390
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