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)
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Article provided by Elsevier for the Society for Economic Dynamics in its journal Review of Economic Dynamics.
Volume (Year): 11 (2008) Issue (Month): 3 (July) Pages: 629-651 Download reference. The following formats are available: HTML,
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Find related papers by JEL classification: G12 - Financial Economics - - General Financial Markets - - - Asset Pricing D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
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Ravi Jagannathan & Ellen R. McGrattan & Anna Scherbina., 2000.
"The declining U.S. equity premium,"
Quarterly Review,
Federal Reserve Bank of Minneapolis, issue Fall, pages 3-19.
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Campbell, John Y., 2003.
"Consumption-based asset pricing,"
Handbook of the Economics of Finance,
in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, edition 1, volume 1, chapter 13, pages 803-887
Elsevier.
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