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

Journal of Economic Dynamics and Control, Vol. 39, pp. 79-97

Alexander Ludwig,
Alexander Zimper
Research Area:
Macro and Finance
Feb 2014
Ambiguity, Non-additive probability measures, Bayesian learning, Truncated normal distribution, Risk-free rate puzzle

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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