Abstract
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 language | English (US) |
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Pages (from-to) | 367-382 |
Number of pages | 16 |
Journal | Review of Economic Dynamics |
Volume | 17 |
Issue number | 3 |
DOIs | |
State | Published - Jul 2014 |
Keywords
- Adaptive learning
- Asset prices
- Fat tails
- Large deviations
ASJC Scopus subject areas
- Economics and Econometrics