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Deep learning of value at risk through generative neural network models: The case of the Variational auto encoder

We present in this paper a method to compute, using generative neural networks, an estimator of the “Value at Risk” for a financial asset. The method uses a Variational Auto Encoder with an 'energy' (a.k.a. Radon-Sobolev) kernel. The result behaves according to intuition and is in line wit...

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Detalles Bibliográficos
Autores principales: Brugière, Pierre, Turinici, Gabriel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10165165/
https://www.ncbi.nlm.nih.gov/pubmed/37168774
http://dx.doi.org/10.1016/j.mex.2023.102192

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