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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252930.pdf | 233.57 kB | Adobe PDF | View/Open |
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