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Market Microstructure Invariance: A Dynamic Equilibrium Model

Author

Listed:
  • Albert S. Kyle

    (Robert H. Smith School of Business, University of Maryland)

  • Anna Obizhaeva

    (New Economic School)

Abstract

We derive invariance relationships for a dynamic infinite-horizon model of market microstructure with risk-neutral informed trading, noise trading, market making, and endogenous production of information. Equilibrium prices follow a martingale with endogenously derived stochastic volatility. The invariance relationships for bet sizes and transaction costs are obtained under the assumption that the effort required to generate one discrete bet does not vary across securities and time. The invariance relationships for pricing accuracy and market resiliency require the additional assumption that private information has the same signal-to-noise ratio across markets. Since bets are based on the arrival of discrete chunks of information, the structural model describes how the invariance relationships reflect differences in the granularity of information flows across markets. The model links proportionality coefficients in invariance relationships to fundamental parameters.

Suggested Citation

  • Albert S. Kyle & Anna Obizhaeva, 2016. "Market Microstructure Invariance: A Dynamic Equilibrium Model," Working Papers w0228, New Economic School (NES).
    • Handle: RePEc:abo:neswpt:w0228
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    File URL: https://www.nes.ru/files/Preprints-resh/WP228.pdf
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    References listed on IDEAS

    as
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    5. Harris, Lawrence, 1987. "Transaction Data Tests of the Mixture of Distributions Hypothesis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(2), pages 127-141, June.
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    Cited by:

    1. Torben G. Andersen & Oleg Bondarenko & Albert S. Kyle & Anna Obizhaeva, 2016. "Intraday Trading Invariance in the E-mini S&P 500 Futures Market," Working Papers w0229, Center for Economic and Financial Research (CEFIR).
    2. Mathias Pohl & Alexander Ristig & Walter Schachermayer & Ludovic Tangpi, 2017. "The amazing power of dimensional analysis: Quantifying market impact," Papers 1702.05434, arXiv.org, revised Sep 2017.

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