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Macro-financial volatility under dispersed information

Author

Listed:
  • Miao, Jianjun

    (Department of Economics, Boston University)

  • Wu, Jieran

    (School of Economics, Zhejiang University)

  • Young, Eric R.

    (Department of Economics, University of Virginia and School of Economics, Zhejiang University)

Abstract

We provide a production-based asset pricing model with dispersed information and small deviations from full rational expectations. In the model, aggregate output and equity prices depend on the higher-order beliefs about aggregate demand and individual stochastic discount factors. We prove that equity price volatility becomes arbitrarily large as the volatility of idiosyncratic shocks diverges to infinity due to the interaction of signal-extraction with idiosyncratic trading decisions, while aggregate output volatility falls. We propose a two-step spectral factorization method that permits closed-form solutions in the frequency domain applicable to a wide range of models with more hidden states than signals. Our model can quantitatively match output and equity volatilities observed in US data.

Suggested Citation

  • Miao, Jianjun & Wu, Jieran & Young, Eric R., 2021. "Macro-financial volatility under dispersed information," Theoretical Economics, Econometric Society, vol. 16(1), January.
    • Handle: RePEc:the:publsh:3872
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    References listed on IDEAS

    as
    1. Kenneth Kasa & Todd B. Walker & Charles H. Whiteman, 2014. "Heterogeneous Beliefs and Tests of Present Value Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(3), pages 1137-1163.
    2. Hansen, Lars Peter & Sargent, Thomas J., 1980. "Formulating and estimating dynamic linear rational expectations models," Journal of Economic Dynamics and Control, Elsevier, vol. 2(1), pages 7-46, May.
    3. Greenwood, Jeremy & Hercowitz, Zvi & Huffman, Gregory W, 1988. "Investment, Capacity Utilization, and the Real Business Cycle," American Economic Review, American Economic Association, vol. 78(3), pages 402-417, June.
    4. Todd Walker & Giacomo Rondina, 2017. "Confounding Dynamics," 2017 Meeting Papers 525, Society for Economic Dynamics.
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    Cited by:

    1. Han, Zhao & Tan, Fei & Wu, Jieran, 2022. "Analytic policy function iteration," Journal of Economic Theory, Elsevier, vol. 200(C).
    2. Jurado, Kyle, 2023. "Rational inattention in the frequency domain," Journal of Economic Theory, Elsevier, vol. 208(C).

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    More about this item

    Keywords

    Dispersed information; frequency domain analysis; higher-order beliefs; asset pricing; business cycles; incomplete markets;
    All these keywords.

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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