Deep learning of Value at Risk through generative neural network models : the case of the Variational Auto Encoder
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24 avril 2023
- handle: 10670/1.dtg33v
- hal: hal-03880381
- doi: 10.1016/j.mex.2023.102192
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info:eu-repo/semantics/altIdentifier/doi/10.1016/j.mex.2023.102192
info:eu-repo/semantics/OpenAccess
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Pierre Brugière et al., « Deep learning of Value at Risk through generative neural network models : the case of the Variational Auto Encoder », HAL-SHS : économie et finance, ID : 10.1016/j.mex.2023.102192
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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.