Scenario generation for market risk models using generative neural networks

Solveig Flaig and Gero Junike

Papers from arXiv.org

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.

Date: 2021-09, Revised 2023-08
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ias, nep-isf and nep-rmg
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Citations: View citations in EconPapers (2)

Published in Risks 10.11 (2022): 199

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2109.10072

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