IDEAS home Printed from https://ideas.repec.org/a/ove/journl/aid19771.html

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://reunido.uniovi.es/index.php/EBL/article/view/19771
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    3. Sornette, Didier & Woodard, Ryan & Yan, Wanfeng & Zhou, Wei-Xing, 2013. "Clarifications to questions and criticisms on the Johansen–Ledoit–Sornette financial bubble model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4417-4428.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. John Fry & McMillan David, 2015. "Stochastic modelling for financial bubbles and policy," Cogent Economics & Finance, Taylor & Francis Journals, vol. 3(1), pages 1002152-100, December.
    2. Lin, L. & Ren, R.E. & Sornette, D., 2014. "The volatility-confined LPPL model: A consistent model of ‘explosive’ financial bubbles with mean-reverting residuals," International Review of Financial Analysis, Elsevier, vol. 33(C), pages 210-225.
    3. Damian Smug & Peter Ashwin & Didier Sornette, 2018. "Predicting financial market crashes using ghost singularities," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-20, March.
    4. Diego Ardila & Dorsa Sanadgol & Peter Cauwels & Didier Sornette, 2017. "Identification and critical time forecasting of real estate bubbles in the USA," Quantitative Finance, Taylor & Francis Journals, vol. 17(4), pages 613-631, April.
    5. Jerome L Kreuser & Didier Sornette, 2017. "Super-Exponential RE Bubble Model with Efficient Crashes," Swiss Finance Institute Research Paper Series 17-33, Swiss Finance Institute.
    6. Zhang, Qunzhi & Sornette, Didier & Balcilar, Mehmet & Gupta, Rangan & Ozdemir, Zeynel Abidin & Yetkiner, Hakan, 2016. "LPPLS bubble indicators over two centuries of the S&P 500 index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 126-139.
    7. L. Lin & M. Schatz & D. Sornette, 2019. "A simple mechanism for financial bubbles: time-varying momentum horizon," Quantitative Finance, Taylor & Francis Journals, vol. 19(6), pages 937-959, June.
    8. Spencer Wheatley & Didier Sornette & Tobias Huber & Max Reppen & Robert N. Gantner, 2018. "Are Bitcoin Bubbles Predictable? Combining a Generalized Metcalfe's Law and the LPPLS Model," Papers 1803.05663, arXiv.org.
    9. Anja Janischewski & Michael Heinrich Baumann, 2025. "What are Asset Price Bubbles? A Survey on Definitions of Financial Bubbles," Chemnitz Economic Papers 065, Department of Economics, Chemnitz University of Technology.
    10. Thomas Lux, 2013. "Inference for systems of stochastic differential equations from discretely sampled data: a numerical maximum likelihood approach," Annals of Finance, Springer, vol. 9(2), pages 217-248, May.
    11. Grosjean, Nicolas & Huillet, Thierry, 2016. "Deterministic versus stochastic aspects of superexponential population growth models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 455(C), pages 27-37.
    12. Ludovic Tangpi & Shichun Wang, 2023. "Optimal Bubble Riding with Price-dependent Entry: a Mean Field Game of Controls with Common Noise," Papers 2307.11340, arXiv.org.
    13. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186, Elsevier.
    14. V. Filimonov & G. Demos & D. Sornette, 2017. "Modified profile likelihood inference and interval forecast of the burst of financial bubbles," Quantitative Finance, Taylor & Francis Journals, vol. 17(8), pages 1167-1186, August.
    15. Li Lin & Didier Sornette, 2015. ""Speculative Influence Network" during financial bubbles: application to Chinese Stock Markets," Papers 1510.08162, arXiv.org.
    16. Sergio Luis Náñez Alonso & Javier Jorge-Vázquez & Miguel Ángel Echarte Fernández & David Sanz-Bas, 2024. "Bitcoin’s bubbly behaviors: does it resemble other financial bubbles of the past?," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    17. Cheng, Fangzheng & Fan, Tijun & Fan, Dandan & Li, Shanling, 2018. "The prediction of oil price turning points with log-periodic power law and multi-population genetic algorithm," Energy Economics, Elsevier, vol. 72(C), pages 341-355.
    18. Riza Demirer & Guilherme Demos & Rangan Gupta & Didier Sornette, 2019. "On the predictability of stock market bubbles: evidence from LPPLS confidence multi-scale indicators," Quantitative Finance, Taylor & Francis Journals, vol. 19(5), pages 843-858, May.
    19. Fry, John & Cheah, Eng-Tuck, 2016. "Negative bubbles and shocks in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 343-352.
    20. Ludovic Tangpi & Shichun Wang, 2022. "Optimal Bubble Riding: A Mean Field Game with Varying Entry Times," Papers 2209.04001, arXiv.org, revised Jan 2024.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ove:journl:aid:19771. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Francisco J. Delgado (email available below). General contact details of provider: https://edirc.repec.org/data/deovies.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.