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How Equilibrium Prices Reveal Information in Time Series Models with Disparately Informed, Competitive Traders

Author

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  • Todd B. Walker

    (Indiana University Bloomington)

Abstract
Accommodating asymmetric information in a dynamic asset pricing model is technically challenging due to the problems associated with higher-order expectations. That is, rational investors are forced into a situation where they must forecast the forecasts of other agents. In a dynamic setting, this problem telescopes into the infinite future and the dimension of the relevant state space approaches infinity. By using the frequency domain approach of Whiteman (1983) and Kasa (2000), this paper demonstrates how information structures previously believed to preserve asymmetric information in equilibrium, converge to a symmetric information, rational expectations equilibrium. The revealing aspect of the price process lies in the invertibility of the observed state space, which makes it possible for agents to infer the economically fundamental shocks and thus eliminating the need to forecast the forecasts of others.

Suggested Citation

  • Todd B. Walker, 2006. "How Equilibrium Prices Reveal Information in Time Series Models with Disparately Informed, Competitive Traders," CAEPR Working Papers 2006-011, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    • Handle: RePEc:inu:caeprp:2006011
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Tan, Fei, 2018. "A Frequency-Domain Approach to Dynamic Macroeconomic Models," MPRA Paper 90487, University Library of Munich, Germany.
    2. Philippe Bacchetta & Eric Van Wincoop, 2008. "Higher Order Expectations in Asset Pricing," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(5), pages 837-866, August.
    3. Kristoffer Nimark, 2007. "Dynamic Higher Order Expectations," 2007 Meeting Papers 542, Society for Economic Dynamics.
    4. Eric R. Young & Ponpoje Porapakkarm, 2008. "Information Heterogeneity in the Macroeconomy," 2008 Meeting Papers 67, Society for Economic Dynamics.
    5. Angeletos, G.-M. & Lian, C., 2016. "Incomplete Information in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 1065-1240, Elsevier.
    6. Pengfei Wang & Yi Wen, 2007. "Incomplete information and self-fulfilling prophecies," Working Papers 2007-033, Federal Reserve Bank of St. Louis.
    7. George-Marios Angeletos & Chen Lian, 2016. "Incomplete Information in Macroeconomics: Accommodating Frictions in Coordination," NBER Working Papers 22297, National Bureau of Economic Research, Inc.
    8. Lanne, Markku & Saikkonen, Pentti, 2013. "Noncausal Vector Autoregression," Econometric Theory, Cambridge University Press, vol. 29(3), pages 447-481, June.
    9. Rondina, Giacomo & Walker, Todd B., 2021. "Confounding dynamics," Journal of Economic Theory, Elsevier, vol. 196(C).
    10. Lanne Markku & Saikkonen Pentti, 2011. "Noncausal Autoregressions for Economic Time Series," Journal of Time Series Econometrics, De Gruyter, vol. 3(3), pages 1-32, October.
    11. P. Seiler & B. Taub, 2008. "The dynamics of strategic information flows in stock markets," Finance and Stochastics, Springer, vol. 12(1), pages 43-82, January.
    12. Kristoffer Nimark, 2009. "Speculative dynamics in the term structure of interest rates," Economics Working Papers 1194, Department of Economics and Business, Universitat Pompeu Fabra, revised Sep 2012.
    13. repec:zbw:bofrdp:2009_018 is not listed on IDEAS

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    More about this item

    Keywords

    Asset Pricing; Asymmetric Information;

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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