Scenario generation for market risk models using generative neural networks

• URL: http://arxiv.org/abs/2109.10072v5
• Date: Fri, 25 Aug 2023 07:19:16 GMT
• Title: Scenario generation for market risk models using generative neural networks
• Authors: Solveig Flaig and Gero Junike
• Abstract summary: We show how to expand existing approaches of using generative adversarial networks (GANs) to a whole internal market risk model. We demonstrate that the results of a GAN-based internal model are similar to regulatory approved internal models in Europe.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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