Scenario generation for market risk models using generative neural networks. (arXiv:2109.10072v4 [cs.LG] UPDATED)

In this research, we show how to expand existing approaches of using
generative adversarial networks (GANs) as economic scenario generators (ESG) to
a whole internal market risk model - with enough risk factors to model the full
band-width of investments for an insurance company and for a one year time
horizon as required in Solvency 2. We demonstrate that the results of a
GAN-based internal model are similar to regulatory approved internal models in
Europe. Therefore, GAN-based models can be …

arxiv generation networks neural networks risk