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Online Appendix to Asset Pricing with Adaptive Learning

  • Eva Carceles-Poveda

    (SUNY Stony Brook)

  • Chryssi Giannitsarou

    (University of Cambridge)

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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Paper provided by Review of Economic Dynamics in its series Technical Appendices with number carceles08.

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Length: 7 pages
Date of creation: Oct 2007
Date of revision:
Handle: RePEc:red:append:carceles08
Note: The original article was published in the Review of Economic Dynamics, forthcoming
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  6. Carceles-Poveda, Eva & Giannitsarou, Chryssi, 2007. "Adaptive learning in practice," Journal of Economic Dynamics and Control, Elsevier, vol. 31(8), pages 2659-2697, August.
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