Deep learning of value at risk through generative neural network models: The case of the Variational auto encoder
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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Deep learning of value at risk through generative neural network models: The case of the Variational auto encoder
Semantic Scholar · Computer Science · 2023
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