Asset Pricing with Adaptive Learning
Eva Carceles-Poveda () and
Chryssi Giannitsarou ()
Review of Economic Dynamics, 2008, vol. 11, issue 3, 629-651
We study the extent to which self-referential adaptive learning can explain stylized asset pricing facts in a general equilibrium framework. In particular, we analyze the effects of recursive least squares and constant gain algorithms in a production economy and a Lucas type endowment economy. We find that (a) recursive least squares learning has almost no effects on asset price behavior, since the algorithm converges relatively fast to rational expectations, (b) constant gain learning may contribute towards explaining the stock price and return volatility as well as the predictability of excess returns in the endowment economy but (c) in the production economy the effects of constant gain learning are mitigated by the persistence induced by capital accumulation. We conclude that in the context of these two commonly used models, standard linear self-referential learning does not resolve the asset pricing puzzles observed in the data. (Copyright: Elsevier)
Keywords: Asset pricing; Adaptive learning; Excess returns; Predictability (search for similar items in EconPapers)
JEL-codes: G12 D83 D84 (search for similar items in EconPapers)
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Working Paper: Asset Pricing with Adaptive Learning (2007)
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Working Paper: Asset pricing with adaptive learning (2006)
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