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Deep learning of value at risk through generative neural network models: The case of the Variational auto encoder
Pierre Brugière1, Gabriel Turinici1
1CEREMADE, University Paris Dauphine-PSL, Paris, FRANCE.
Abstract:
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 with more classical methods.•Estimation of the Value at Risk with generative neural networks•No a priori assumptions on the distribution of the returns•Good practical behavior.
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