Learning, large deviations and rare events

Jess Benhabib, Chetan Dave

Research output: Contribution to journalArticlepeer-review


We examine the role of generalized stochastic gradient constant gain (SGCG) learning in generating large deviations of an endogenous variable from its rational expectations value. We show analytically that these large deviations can occur with a frequency associated with a fat-tailed distribution even though the model is driven by thin-tailed exogenous stochastic processes. We characterize these large deviations, driven by sequences of consistently low or consistently high shocks and then apply our model to the canonical asset pricing framework. We demonstrate that the tails of the stationary distribution of the price-dividend ratio will follow a power law.

Original languageEnglish (US)
Pages (from-to)367-382
Number of pages16
JournalReview of Economic Dynamics
Issue number3
StatePublished - Jul 2014


  • Adaptive learning
  • Asset prices
  • Fat tails
  • Large deviations

ASJC Scopus subject areas

  • Economics and Econometrics


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