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Deep learning of Value at Risk through generative neural network models : the case of the Variational Auto Encoder

Brugière, Pierre; Turinici, Gabriel (2022), Deep learning of Value at Risk through generative neural network models : the case of the Variational Auto Encoder. https://basepub.dauphine.psl.eu/handle/123456789/23737

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Type
Document de travail / Working paper
Date
2022
Series title
Cahier de recherche CEREMADE, Université Paris Dauphine-PSL
Published in
Paris
Pages
4
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Author(s)
Brugière, Pierre cc
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Turinici, Gabriel cc
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Abstract (EN)
We present in this paper a method to compute, using generative neural networks, an estimator of the "Value at Risk" for a nancial asset. The method uses a Variational Auto Encoder with a 'energy' (a.k.a. Radon- Sobolev) kernel. The result behaves according to intuition and is in line with more classical methods.