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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...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier
2023
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| 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 |