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Multi-quantile systemic financial risk based on a monotone composite quantile regression neural network

This study proposes a novel perspective to calibrate the conditional value at risk (CoVaR) of countries based on the monotone composite quantile regression neural network (MCQRNN). MCQRNN can fix the “quantile crossing” problem, which is more robust in CoVaR estimating. In addition, we extend the MC...

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Detalles Bibliográficos
Principais autores: Chao Ren, Ziyan Zhu, Donghai Zhou
Formato: Artigo
Idioma:Inglês
Publicado: Frontiers Media S.A. 2024-11-01
Series:Frontiers in Physics
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Acceso en liña:https://www.frontiersin.org/articles/10.3389/fphy.2024.1484589/full
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