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

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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)

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.

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  • Pierre Brugière & Gabriel Turinici, 2023. "Deep learning of Value at Risk through generative neural network models : the case of the Variational Auto Encoder," Post-Print hal-03880381, HAL.
    • Handle: RePEc:hal:journl:hal-03880381
    DOI: 10.1016/j.mex.2023.102192
    Note: View the original document on HAL open archive server: https://hal.science/hal-03880381
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    Cited by:

    1. Sergio Caprioli & Emanuele Cagliero & Riccardo Crupi, 2023. "Quantifying Credit Portfolio sensitivity to asset correlations with interpretable generative neural networks," Papers 2309.08652, arXiv.org, revised Nov 2023.

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