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Scenario generation for market risk models using generative neural networks

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  • Solveig Flaig
  • Gero Junike

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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  • Solveig Flaig & Gero Junike, 2021. "Scenario generation for market risk models using generative neural networks," Papers 2109.10072, arXiv.org, revised Aug 2023.
    • Handle: RePEc:arx:papers:2109.10072
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    References listed on IDEAS

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    7. Edmond Lezmi & Jules Roche & Thierry Roncalli & Jiali Xu, 2020. "Improving the Robustness of Trading Strategy Backtesting with Boltzmann Machines and Generative Adversarial Networks," Papers 2007.04838, arXiv.org.
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    Cited by:

    1. Gero Junike & Hauke Stier & Marcus C. Christiansen, 2022. "Sequential decompositions at their limit," Papers 2212.06733, arXiv.org, revised Apr 2023.
    2. Solveig Flaig & Gero Junike, 2023. "Validation of machine learning based scenario generators," Papers 2301.12719, arXiv.org, revised Nov 2023.

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