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Microfounding GARCH Models and Beyond: A Kyle-inspired Model with Adaptive Agents

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  • Michele Vodret
  • Iacopo Mastromatteo
  • Bence Toth
  • Michael Benzaquen

Abstract

We relax the strong rationality assumption for the agents in the paradigmatic Kyle model of price formation, thereby reconciling the framework of asymmetrically informed traders with the Adaptive Market Hypothesis, where agents use inductive rather than deductive reasoning. Building on these ideas, we propose a stylised model able to account parsimoniously for a rich phenomenology, ranging from excess volatility to volatility clustering. While characterising the excess-volatility dynamics, we provide a microfoundation for GARCH models. Volatility clustering is shown to be related to the self-excited dynamics induced by traders' behaviour, and does not rely on clustered fundamental innovations. Finally, we propose an extension to account for the fragile dynamics exhibited by real markets during flash crashes.

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  • Michele Vodret & Iacopo Mastromatteo & Bence Toth & Michael Benzaquen, 2022. "Microfounding GARCH Models and Beyond: A Kyle-inspired Model with Adaptive Agents," Papers 2206.06764, arXiv.org.
    • Handle: RePEc:arx:papers:2206.06764
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    References listed on IDEAS

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    1. Bouchaud,Jean-Philippe & Potters,Marc, 2003. "Theory of Financial Risk and Derivative Pricing," Cambridge Books, Cambridge University Press, number 9780521819169.
    2. Mantegna,Rosario N. & Stanley,H. Eugene, 2007. "Introduction to Econophysics," Cambridge Books, Cambridge University Press, number 9780521039871.
    3. Roger Farmer & Jean-Philippe Bouchaud, 2020. "Self-Fulfilling Prophecies, Quasi Non-Ergodicity & Wealth Inequality," NBER Working Papers 28261, National Bureau of Economic Research, Inc.
    4. Robert J. Shiller, 2014. "Speculative Asset Prices," American Economic Review, American Economic Association, vol. 104(6), pages 1486-1517, June.
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