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Scenario Generation for Market Risk Models Using Generative Neural Networks

Author

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  • Solveig Flaig

    (Deutsche Rueckversicherung AG, Market Risk Management, Hansaallee 177, 40549 Duesseldorf, Germany
    Institut für Mathematik, Carl von Ossietzky Universität, 26111 Oldenburg, Germany)

  • Gero Junike

    (Institut für Mathematik, Carl von Ossietzky Universität, 26111 Oldenburg, Germany)

Abstract

In this research study, we show how existing approaches of using generative adversarial networks (GANs) as economic scenario generators (ESG) can be extended to an entire internal market risk model—with enough risk factors to model the full band-width of investments for an insurance company and for a time horizon of one year, 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 an alternative data-driven method for market risk modeling.

Suggested Citation

  • Solveig Flaig & Gero Junike, 2022. "Scenario Generation for Market Risk Models Using Generative Neural Networks," Risks, MDPI, vol. 10(11), pages 1-28, October.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:11:p:199-:d:950343
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    References listed on IDEAS

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    1. Marc Hallin & Gilles Mordant & Johan Segers, 2020. "Multivariate Goodness-of-Fit Tests Based on Wasserstein Distance," Working Papers ECARES 2020-06, ULB -- Universite Libre de Bruxelles.
    2. Magnus Wiese & Lianjun Bai & Ben Wood & Hans Buehler, 2019. "Deep Hedging: Learning to Simulate Equity Option Markets," Papers 1911.01700, arXiv.org.
    3. Florian Eckerli & Joerg Osterrieder, 2021. "Generative Adversarial Networks in finance: an overview," Papers 2106.06364, arXiv.org, revised Jul 2021.
    4. Dietmar Pfeifer & Olena Ragulina, 2018. "Generating VaR Scenarios under Solvency II with Product Beta Distributions," Risks, MDPI, vol. 6(4), pages 1-15, October.
    5. Kwanda Sydwell Ngwenduna & Rendani Mbuvha, 2021. "Alleviating Class Imbalance in Actuarial Applications Using Generative Adversarial Networks," Risks, MDPI, vol. 9(3), pages 1-33, March.
    6. Magnus Wiese & Robert Knobloch & Ralf Korn & Peter Kretschmer, 2020. "Quant GANs: deep generation of financial time series," Quantitative Finance, Taylor & Francis Journals, vol. 20(9), pages 1419-1440, September.
    7. Thomas Viehmann, 2019. "Variants of the Smith-Wilson method with a view towards applications," Papers 1906.06363, arXiv.org.
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    Citations

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    Cited by:

    1. Szymon Kubiak & Tillman Weyde & Oleksandr Galkin & Dan Philps & Ram Gopal, 2023. "Improved Data Generation for Enhanced Asset Allocation: A Synthetic Dataset Approach for the Fixed Income Universe," Papers 2311.16004, arXiv.org.
    2. Gero Junike & Hauke Stier & Marcus C. Christiansen, 2022. "Profit and loss decomposition in continuous time and approximations," Papers 2212.06733, arXiv.org, revised Dec 2024.
    3. Francesca Biagini & Lukas Gonon & Niklas Walter, 2024. "Universal randomised signatures for generative time series modelling," Papers 2406.10214, arXiv.org, revised Sep 2024.
    4. Gero Junike & Solveig Flaig & Ralf Werner, 2023. "Validation of machine learning based scenario generators," Papers 2301.12719, arXiv.org, revised Dec 2024.
    5. Borgonovo, Emanuele & Clemente, Gian Paolo & Rabitti, Giovanni, 2024. "Why insurance regulators need to require sensitivity settings of internal models for their approval," Finance Research Letters, Elsevier, vol. 60(C).

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