DEEP NEURAL NETWORKS METHODS FOR ESTIMATING MARKET MICROSTRUCTURE AND SPECULATIVE ATTACKS MODELS: THE CASE OF GOVERNMENT BOND MARKET

David Alaminos, Marã A Belã‰n Salas and Manuel A. Fernã Ndez-Gã Mez
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David Alaminos: Department of Business, Universitat de Barcelona, Barcelona, Spain
Marã A Belã‰n 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

The Singapore Economic Review (SER), 2025, vol. 70, issue 04, 1069-1104

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.

Keywords: Government bond; public debt; speculative attacks; deep neural networks; market microstructure; systemic risk (search for similar items in EconPapers)
JEL-codes: C63 E44 F3 G12 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0217590822480034

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