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

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  • Todd B. Walker

    (University of Iowa)

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 (i.e., form higher-order expectations). In a dynamic setting, this problem telescopes into the infinite future and the dimension of the relevant state space approaches infinity. By employing the frequency domain approach of Whiteman (1983) and Kasa (2000), this paper demonstrates how information structures previously believed to lead to disparate expectations in equilibrium (e.g., Singleton (1987)) converge to a symmetric 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, thus eliminating the need to forecast the forecasts of others.

Suggested Citation

  • Todd B. Walker, 2005. "How Equilibrium Prices Reveal Information in Time Series Models with Disparately Informed, Competitive Traders," Finance 0509021, University Library of Munich, Germany.
  • Handle: RePEc:wpa:wuwpfi:0509021
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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. Todd Walker, 2017. "Confounding Dynamics," 2017 Meeting Papers 141, Society for Economic Dynamics.
    12. 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.
    13. 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.

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    More about this item

    Keywords

    Asymmetric Information; Asset Pricing; Frequency Domain;
    All these keywords.

    JEL classification:

    • G - Financial Economics

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