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A micro-foundation of a simple financial model with finite-time singularity bubble and its agent-based simulation

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  • Naohiro Yoshida

Abstract

This paper proposes a mathematical model of financial security prices in continuous time with bubbles in which prices may diverge and crash in finite time. Just before the bubbles burst, prices increase super-exponentially. In addition, a discrete-time excess demand model is proposed to provide a micro-foundation for the continuous-time model. The derived discrete-time security price model has the same characteristics as the continuous-time price model and expresses the finite-time singularity. Furthermore, based on the excess demand model, an agent-based simulation is performed to check the price behavior. As expected, we can confirm that prices can diverge in finite time and increase super-exponentially.

Suggested Citation

  • Naohiro Yoshida, 2023. "A micro-foundation of a simple financial model with finite-time singularity bubble and its agent-based simulation," Economics and Business Letters, Oviedo University Press, vol. 12(4), pages 277-283.
  • Handle: RePEc:ove:journl:aid:19771
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    File URL: https://reunido.uniovi.es/index.php/EBL/article/view/19771
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    1. L. Lin & D. Sornette, 2013. "Diagnostics of rational expectation financial bubbles with stochastic mean-reverting termination times," The European Journal of Finance, Taylor & Francis Journals, vol. 19(5), pages 344-365, May.
    2. Rheinlaender Thorsten & Steinkamp Marcus, 2004. "A Stochastic Version of Zeeman's Market Model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(4), pages 1-25, December.
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