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Information and volatility

Author

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  • Bergemann, Dirk
  • Heumann, Tibor
  • Morris, Stephen

Abstract

In an economy of interacting agents with both idiosyncratic and aggregate shocks, we examine how the structure of private information influences aggregate volatility. The maximal aggregate volatility is attained in a noise free information structure in which the agents confound idiosyncratic and aggregate shocks, and display excess response to the aggregate shocks, as in Lucas [14]. For any given variance of aggregate shocks, the upper bound on aggregate volatility is linearly increasing in the variance of the idiosyncratic shocks. Our results hold in a setting of symmetric agents with linear best responses and normal uncertainty. We establish our results by providing a characterization of the set of all joint distributions over actions and states that can arise in equilibrium under any information structure. This tractable characterization, extending results in Bergemann and Morris [8], can be used to address a wide variety of questions linking information with the statistical moments of the economy.

Suggested Citation

  • Bergemann, Dirk & Heumann, Tibor & Morris, Stephen, 2015. "Information and volatility," Journal of Economic Theory, Elsevier, vol. 158(PB), pages 427-465.
  • Handle: RePEc:eee:jetheo:v:158:y:2015:i:pb:p:427-465
    DOI: 10.1016/j.jet.2014.12.002
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    References listed on IDEAS

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    1. Dirk Bergemann & Stephen Morris, 2013. "Robust Predictions in Games With Incomplete Information," Econometrica, Econometric Society, vol. 81(4), pages 1251-1308, July.
    2. George-Marios Angeletos & Jennifer La'O, 2010. "Noisy Business Cycles," NBER Chapters, in: NBER Macroeconomics Annual 2009, Volume 24, pages 319-378, National Bureau of Economic Research, Inc.
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    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Dirk Bergemann & Stephen Morris, 2019. "Information Design: A Unified Perspective," Journal of Economic Literature, American Economic Association, vol. 57(1), pages 44-95, March.
    2. Yamashita, Takuro & Smolin, Alex, 2022. "Information Design in Concave Games," TSE Working Papers 22-1313, Toulouse School of Economics (TSE).
    3. Bergemann, Dirk & Morris, Stephen, 2016. "Bayes correlated equilibrium and the comparison of information structures in games," Theoretical Economics, Econometric Society, vol. 11(2), May.
    4. Martin Ellison & Andreas Tischbirek, 2021. "Beauty Contests and the Term Structure [Risk Premia and Term Premia in General Equilibrium]," Journal of the European Economic Association, European Economic Association, vol. 19(4), pages 2234-2282.
    5. Bergemann, Dirk & Morris, Stephen & Heumann, Tibor, 2015. "Information and Market Power," CEPR Discussion Papers 10791, C.E.P.R. Discussion Papers.
    6. Christian Myohl & Yannic Stucki, 2018. "Confidence and the Financial Accelerator," Diskussionsschriften dp1823, Universitaet Bern, Departement Volkswirtschaft.
    7. Dirk Bergemann & Stephen Morris, 2016. "Information Design, Bayesian Persuasion, and Bayes Correlated Equilibrium," American Economic Review, American Economic Association, vol. 106(5), pages 586-591, May.
    8. Shen, Dehua & Li, Xiao & Zhang, Wei, 2018. "Baidu news information flow and return volatility: Evidence for the Sequential Information Arrival Hypothesis," Economic Modelling, Elsevier, vol. 69(C), pages 127-133.
    9. Acharya, Sushant & Benhabib, Jess & Huo, Zhen, 2021. "The anatomy of sentiment-driven fluctuations," Journal of Economic Theory, Elsevier, vol. 195(C).
    10. Juan Passadore & Juan Xandri, 2019. "Robust Predictions in Dynamic Policy Games," 2019 Meeting Papers 1345, Society for Economic Dynamics.
    11. Andrea Galeotti & Benjamin Golub & Sanjeev Goyal, 2020. "Targeting Interventions in Networks," Econometrica, Econometric Society, vol. 88(6), pages 2445-2471, November.
    12. Alex Smolin & Takuro Yamashita, 2022. "Information Design in Smooth Games," Papers 2202.10883, arXiv.org, revised Dec 2023.
    13. 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.
    14. Dávila, Eduardo & Parlatore, Cecilia, 2023. "Volatility and informativeness," Journal of Financial Economics, Elsevier, vol. 147(3), pages 550-572.
    15. Gaetano Gaballo, 2018. "Price Dispersion, Private Uncertainty, and Endogenous Nominal Rigidities," Review of Economic Studies, Oxford University Press, vol. 85(2), pages 1070-1110.
    16. Carroll, Gabriel, 2016. "Informationally robust trade and limits to contagion," Journal of Economic Theory, Elsevier, vol. 166(C), pages 334-361.
    17. Pavan, Alessandro & Vives, Xavier, 2015. "Information, Coordination, and Market Frictions: An Introduction," Journal of Economic Theory, Elsevier, vol. 158(PB), pages 407-426.
    18. Wu, Jieran, 2022. "Comments on “Sentiments and real business cycles”," Journal of Economic Dynamics and Control, Elsevier, vol. 141(C).
    19. Chahrour, Ryan & Gaballo, Gaetano, 2017. "Learning from prices: amplication and business fluctuations," Working Paper Series 2053, European Central Bank.
    20. Dirk Bergemann & Tibor Heumann & Stephen Morris, 2021. "Information, market power, and price volatility," RAND Journal of Economics, RAND Corporation, vol. 52(1), pages 125-150, March.
    21. Annie Liang & Xiaosheng Mu & Vasilis Syrgkanis, 2019. "Optimal and Myopic Information Acquisition," Working Papers 2019-25, Princeton University. Economics Department..
    22. Firouzi, Shahrokh & Wang, Xiangning, 2021. "The interrelationship between order flow, exchange rate, and the role of American economic news," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    23. Heumann, Tibor, 2021. "Efficiency in trading markets with multi-dimensional signals," Journal of Economic Theory, Elsevier, vol. 191(C).

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    More about this item

    Keywords

    Idiosyncratic shocks; Aggregate shocks; Volatility; Confounding information; Moment restrictions; Bayes correlated equilibrium;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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