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 seen as a data-driven alternative way of market risk modeling.

翻译：本研究展示了如何将使用生成对抗网络（GANs）作为经济场景生成器（ESG）的现有方法扩展到完整的内部市场风险模型中——该模型包含足够多的风险因子，能够模拟保险公司在偿付能力II（Solvency 2）要求下一年期时间范围内的全部投资带宽。我们证明了基于GAN的内部模型结果与欧洲监管批准的内部模型具有相似性。因此，基于GAN的模型可被视为市场风险建模的一种数据驱动替代方案。