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

  • Alexander Ludwig

    ()

    (CMR, University of Cologne; Albertus-Magnus-Platz; 50923 Koln; Germany)

  • Alexander Zimper

    ()

    (Department of Economics, University of Pretoria)

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.

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Paper provided by University of Pretoria, Department of Economics in its series Working Papers with number 201366.

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Length: 45 pages
Date of creation: Nov 2013
Date of revision:
Handle: RePEc:pre:wpaper:201366
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