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

Pierre Brugière () and Gabriel Turinici ()
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Pierre Brugière: CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique
Gabriel Turinici: CEREMADE - CEntre de REcherches en MAthématiques de la DEcision - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique

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Abstract: 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.

Date: 2023-04-24
New Economics Papers: this item is included in nep-big, nep-cmp, nep-net and nep-rmg
Note: View the original document on HAL open archive server: https://hal.science/hal-03880381v1
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Citations: View citations in EconPapers (1)

Published in MethodX, 2023, 10, pp.102192. ⟨10.1016/j.mex.2023.102192⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03880381

DOI: 10.1016/j.mex.2023.102192

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