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

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  • Ludwig, Alexander
  • Zimper, Alexander

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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Bibliographic Info

Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

Volume (Year): 39 (2014)
Issue (Month): C ()
Pages: 79-97

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Handle: RePEc:eee:dyncon:v:39:y:2014:i:c:p:79-97

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Web page: http://www.elsevier.com/locate/jedc

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Keywords: Ambiguity; Non-additive probability measures; Bayesian learning; Truncated normal distribution; Risk-free rate puzzle;

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References

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Cited by:
  1. Alexander Ludwig & Alexander Zimper, 2012. "A decision-theoretic model of asset-price underreaction and overreaction to dividend news," Working Papers 296, Economic Research Southern Africa.
  2. Alexander Zimper, 2011. "Do Bayesians learn their way out of ambiguity?," Working Papers 240, Economic Research Southern Africa.

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