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Computer Science > Machine Learning

arXiv:2109.10072 (cs)
[Submitted on 21 Sep 2021 (v1), last revised 25 Aug 2023 (this version, v5)]

Title:Scenario generation for market risk models using generative neural networks

Authors:Solveig Flaig, Gero Junike
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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.
Subjects: Machine Learning (cs.LG); Mathematical Finance (q-fin.MF); Risk Management (q-fin.RM)
Cite as: arXiv:2109.10072 [cs.LG]
  (or arXiv:2109.10072v5 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2109.10072
Journal reference: Risks 10.11 (2022): 199

Submission history

From: Solveig Flaig [view email]
[v1] Tue, 21 Sep 2021 10:22:29 UTC (711 KB)
[v2] Thu, 3 Mar 2022 13:36:25 UTC (2,159 KB)
[v3] Fri, 4 Mar 2022 07:03:51 UTC (569 KB)
[v4] Mon, 29 Aug 2022 11:13:51 UTC (467 KB)
[v5] Fri, 25 Aug 2023 07:19:16 UTC (692 KB)
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