Schossig, Peter Proceedings of the International Renewable Energy Storage Conference (IRES 2022) 1st ed. Dordrecht : Atlantis Press (Zeger Karssen), 2023 (2023), Seite 451-469 1 online resource (583 pages)
Tōkei-Sūri-Kenkyūsho (Tokio) Annals of the Institute of Statistical Mathematics Dordrecht [u.a.] : Springer Science + Business Media B.V, 1949 75(2023), Seite 335-368 Online-Ressource
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.
