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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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  1. 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.
  2. Carceles-Poveda, Eva & Giannitsarou, Chryssi, 2007. "Adaptive learning in practice," Journal of Economic Dynamics and Control, Elsevier, vol. 31(8), pages 2659-2697, August.
  3. Bacon, Robert W, 1980. "A Note on the Properties of Products of Random Variables with Reference to Economic Applications," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 42(4), pages 337-44, November.
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  11. Giannitsarou, Chryssi, 2005. "E-Stability Does Not Imply Learnability," Macroeconomic Dynamics, Cambridge University Press, vol. 9(02), pages 276-287, April.
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  14. Brock, William A., 1980. "Asset Prices in a Production Economy," Working Papers 275, California Institute of Technology, Division of the Humanities and Social Sciences.
  15. James Bullard & John Duffy, 1999. "Learning and Excess Volatility," Computing in Economics and Finance 1999 224, Society for Computational Economics.
  16. Pok-sang Lam & Stephen G. Cecchetti & Nelson C. Mark, 2000. "Asset Pricing with Distorted Beliefs: Are Equity Returns Too Good to Be True?," American Economic Review, American Economic Association, vol. 90(4), pages 787-805, September.
  17. Martin Lettau, 2003. "Inspecting The Mechanism: Closed-Form Solutions For Asset Prices In Real Business Cycle Models," Economic Journal, Royal Economic Society, vol. 113(489), pages 550-575, 07.
  18. Timmermann, Allan G, 1993. "How Learning in Financial Markets Generates Excess Volatility and Predictability in Stock Prices," The Quarterly Journal of Economics, MIT Press, vol. 108(4), pages 1135-45, November.
  19. Timmermann, Allan, 1996. "Excess Volatility and Predictability of Stock Prices in Autoregressive Dividend Models with Learning," Review of Economic Studies, Wiley Blackwell, vol. 63(4), pages 523-57, October.
  20. Brock, W.A. & Hommes, C.H., 1996. "Hetergeneous Beliefs and Routes to Chaos in a Simple Asset Pricing Model," Working papers 9621, Wisconsin Madison - Social Systems.
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  23. repec:cup:macdyn:v:5:y:2001:i:2:p:272-302 is not listed on IDEAS
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