# Scenario generation for market risk models using generative neural networks

arXiv: https://arxiv.org/abs/2109.10072 · HTML: https://mlsanity.com/paper/2109.10072/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 72 references that are in the map.
- First posted on arXiv 21 Sep 2021.

## Builds on (most cited first, 22 of 22)

- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [A note on the evaluation of generative models](https://mlsanity.com/paper/1511.01844/) (2015)
- [NIPS 2016 Tutorial: Generative Adversarial Networks](https://mlsanity.com/paper/1701.00160/) (2016)
- [Are GANs Created Equal? A Large-Scale Study](https://mlsanity.com/paper/1711.10337/) (2017)
- [Pros and Cons of GAN Evaluation Measures](https://mlsanity.com/paper/1802.03446/) (2018)
- [On Wasserstein Two Sample Testing and Related Families of Nonparametric Tests](https://mlsanity.com/paper/1509.02237/) (2015)
- [Quant GANs: Deep Generation of Financial Time Series](https://mlsanity.com/paper/1907.06673/) (2019)
- [The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling](https://mlsanity.com/paper/2001.00570/) (2020)
- [On GANs and GMMs](https://mlsanity.com/paper/1805.12462/) (2018)
- [On Gradient-Based Learning in Continuous Games](https://mlsanity.com/paper/1804.05464/) (2018)
- [Deep Hedging: Learning to Simulate Equity Option Markets](https://mlsanity.com/paper/1911.01700/) (2019)
- [Time Series Simulation by Conditional Generative Adversarial Net](https://mlsanity.com/paper/1904.11419/) (2019)
- [CorrGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks](https://mlsanity.com/paper/1910.09504/) (2019)
- [Multivariate goodness-of-Fit tests based on Wasserstein distance](https://mlsanity.com/paper/2003.06684/) (2020)
- [Bayesian Renewables Scenario Generation via Deep Generative Networks](https://mlsanity.com/paper/1802.00868/) (2018)
- [Generative Synthesis of Insurance Datasets](https://mlsanity.com/paper/1912.02423/) (2019)
- [A Novel Framework for Selection of GANs for an Application](https://mlsanity.com/paper/2002.08641/) (2020)
- [Improving the Robustness of Trading Strategy Backtesting with Boltzmann Machines and Generative Adversarial Networks](https://mlsanity.com/paper/2007.04838/) (2020)
- [Multivariate goodness-of-fit on flat and curved spaces via nearest neighbor distances](https://mlsanity.com/paper/1612.06601/) (2016)
- [Generating virtual scenarios of multivariate financial data for quantitative trading applications](https://mlsanity.com/paper/1802.01861/) (2018)
- [Synthesizing Property & Casualty Ratemaking Datasets using Generative Adversarial Networks](https://mlsanity.com/paper/2008.06110/) (2020)

## Built on by (most cited first, 7 of 7)

- [New Money: A Systematic Review of Synthetic Data Generation for Finance](https://mlsanity.com/paper/2510.26076/) (2025)
- [Universal randomised signatures for generative time series modelling](https://mlsanity.com/paper/2406.10214/) (2024)
- [AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models](https://mlsanity.com/paper/2508.02269/) (2025)
- [Diffusion-Augmented Reinforcement Learning for Robust Portfolio Optimization under Stress Scenarios](https://mlsanity.com/paper/2510.07099/) (2025)
- [Improved Data Generation for Enhanced Asset Allocation: A Synthetic Dataset Approach for the Fixed Income Universe](https://mlsanity.com/paper/2311.16004/) (2023)
- [Batch normalization does not improve initialization](https://mlsanity.com/paper/2502.17913/) (2025)
- [LLM-Generated Counterfactual Stress Scenarios for Portfolio Risk Simulation via Hybrid Prompt-RAG Pipeline](https://mlsanity.com/paper/2512.07867/) (2025)

## Abstract (arXiv)

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.