Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/222926
Title: Deep Neural Networks Methods for Estimating Market Microstructure and Speculative Attacks Models: The case of Government Bond Market
Keywords: Bons
Xarxes neuronals (Informàtica)
Economia de mercat
Fons especulatius
Deute públic
Bonds
Neural networks (Computer science)
Market economy
Hedge funds
Public debt
Issue Date: 1-Jun-2025
Publisher: World Scientific Publishing
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.
Note: Versió postprint del document publicat a: https://doi.org/10.1142/S0217590822480034
It is part of: The Singapore Economic Review, 2022, vol. 70, num.4, p. 1069-1104
URI: https://hdl.handle.net/2445/222926
Related resource: https://doi.org/10.1142/S0217590822480034
ISSN: 0217-5908
Appears in Collections:Articles publicats en revistes (Empresa)

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