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Validation of machine learning based scenario generators

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  • Solveig Flaig
  • Gero Junike

Abstract

Machine learning methods are getting more and more important in the development of internal models using scenario generation. As internal models under Solvency 2 have to be validated, an important question is in which aspects the validation of these data-driven models differs from a classical theory-based model. On the specific example of market risk, we discuss the necessity of two additional validation tasks: one to check the dependencies between the risk factors used and one to detect the unwanted memorizing effect. The first one is necessary because in this new method, the dependencies are not derived from a financial-mathematical theory. The latter one arises when the machine learning model only repeats empirical data instead of generating new scenarios. These measures are then applied for an machine learning based economic scenario generator. It is shown that those measures lead to reasonable results in this context and are able to be used for validation as well as for model optimization.

Suggested Citation

  • Solveig Flaig & Gero Junike, 2023. "Validation of machine learning based scenario generators," Papers 2301.12719, arXiv.org, revised Nov 2023.
    • Handle: RePEc:arx:papers:2301.12719
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    References listed on IDEAS

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    1. Magnus Wiese & Lianjun Bai & Ben Wood & Hans Buehler, 2019. "Deep Hedging: Learning to Simulate Equity Option Markets," Papers 1911.01700, arXiv.org.
    2. Solveig Flaig & Gero Junike, 2021. "Scenario generation for market risk models using generative neural networks," Papers 2109.10072, arXiv.org, revised Aug 2023.
    3. Mondal, Pronoy K. & Biswas, Munmun & Ghosh, Anil K., 2015. "On high dimensional two-sample tests based on nearest neighbors," Journal of Multivariate Analysis, Elsevier, vol. 141(C), pages 168-178.
    4. Solveig Flaig & Gero Junike, 2022. "Scenario Generation for Market Risk Models Using Generative Neural Networks," Risks, MDPI, vol. 10(11), pages 1-28, October.
    5. Dietmar Pfeifer & Olena Ragulina, 2018. "Generating VaR Scenarios under Solvency II with Product Beta Distributions," Risks, MDPI, vol. 6(4), pages 1-15, October.
    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. Ebner, Bruno & Henze, Norbert & Yukich, Joseph E., 2018. "Multivariate goodness-of-fit on flat and curved spaces via nearest neighbor distances," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 231-242.
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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.

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