Scenario Generation for Market Risk Models Using Generative Neural Networks

Solveig Flaig () and Gero Junike
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Solveig Flaig: Deutsche Rueckversicherung AG, Market Risk Management, Hansaallee 177, 40549 Duesseldorf, Germany
Gero Junike: Institut für Mathematik, Carl von Ossietzky Universität, 26111 Oldenburg, Germany

Risks, 2022, vol. 10, issue 11, 1-28

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.

Keywords: generative adversarial networks; economic scenario generators; market risk modeling; Solvency 2 (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:10:y:2022:i:11:p:199-:d:950343

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