Scenario Generation for Market Risk Models Using Generative Neural Networks

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 factors model the full band-width investments for insurance company and a time horizon one year, required in Solvency 2. We demonstrate that results GAN-based are similar regulatory-approved models Europe. Therefore, seen alternative data-driven method modeling.