In this research study, we show how existing approaches of using generative adversarial networks (GANs) as economic scenario generators (ESG) can be extended to an entire internal market risk model-with enough risk factors to model the full band-width of investments for an insurance company and for a time horizon of one year, 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 seen as an alternative data-driven method for market risk modeling.
