Zobraziť v EDS

Deep Neural Networks Methods for Estimating Market Microstructure and Speculative Attacks Models: The case of Government Bond Market

Uložené v:
Podrobná bibliografia
Názov: Deep Neural Networks Methods for Estimating Market Microstructure and Speculative Attacks Models: The case of Government Bond Market
Informácie o vydavateľovi: World Scientific Publishing, 2025.
Rok vydania: 2025
Predmety: Neural networks (Computer science), Hedge funds, Public debt, Fons especulatius, Bonds, Market economy, Xarxes neuronals (Informàtica), Bons, Economia de mercat, Deute públic
Popis: 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.
Druh dokumentu: Article
Popis súboru: application/pdf
Jazyk: English
Prístupová URL adresa: https://hdl.handle.net/2445/222926
Prístupové číslo: edsair.od.......963..9e43f6f4779ad8294a78412aa5bda942
Databáza: OpenAIRE
Popis
Abstrakt: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.