IDEAS home Printed from https://ideas.repec.org/a/wsi/serxxx/v70y2025i04ns0217590822480034.html
   My bibliography  Save this article

Deep Neural Networks Methods For Estimating Market Microstructure And Speculative Attacks Models: The Case Of Government Bond Market

Author

Listed:
  • DAVID ALAMINOS

    (Department of Business, Universitat de Barcelona, Barcelona, Spain)

  • MARà A BELÉN SALAS

    (epartment of Finance and Accounting, Universidad de Málaga, Málaga, SpainCátedra de Economía y Finanzas Sostenibles, Universidad de Málaga, Málaga, Spain)

  • MANUEL A. FERNÃ NDEZ-GÃ MEZ

    (epartment of Finance and Accounting, Universidad de Málaga, Málaga, SpainCátedra de Economía y Finanzas Sostenibles, Universidad de Málaga, Málaga, Spain)

Abstract

A sovereign bond market offers a wide range of opportunities for public and private sector financing and has drawn the interest of both scholars and professionals as they are the main instrument of most fixed-income asset markets. Numerous works have studied the behavior of sovereign bonds at the microeconomic level, given that a domestic securities market can enhance overall financial stability and improve financial market intermediation. Nevertheless, they do not deepen methods that identify liquidity risks in bond markets. This study introduces a new model for predicting unexpected situations of speculative attacks in the government bond market, applying methods of deep learning neural networks, which proactively identify and quantify financial market risks. Our approach has a strong impact in anticipating possible speculative actions against the sovereign bond market and liquidity risks, so the aspect of the potential effect on the systemic risk is of high importance.

Suggested Citation

  • David Alaminos & Marã A Belã‰N Salas & Manuel A. Fernã Ndez-Gã Mez, 2025. "Deep Neural Networks Methods For Estimating Market Microstructure And Speculative Attacks Models: The Case Of Government Bond Market," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 70(04), pages 1069-1104, June.
    • Handle: RePEc:wsi:serxxx:v:70:y:2025:i:04:n:s0217590822480034
    DOI: 10.1142/S0217590822480034
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0217590822480034
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0217590822480034?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Keywords

    Government bond; public debt; speculative attacks; deep neural networks; market microstructure; systemic risk;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • F3 - International Economics - - International Finance
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:wsi:serxxx:v:70:y:2025:i:04:n:s0217590822480034. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ser/ser.shtml .

    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.