# CorrGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1910.09504 · HTML: https://mlsanity.com/paper/1910.09504/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2019.

## Built on by (most cited first, 16 of 16)

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- [Universal randomised signatures for generative time series modelling](https://mlsanity.com/paper/2406.10214/) (2024)
- [cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Distributions in the Elliptope](https://mlsanity.com/paper/2107.10606/) (2021)
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- [Quantifying Credit Portfolio sensitivity to asset correlations with interpretable generative neural networks](https://mlsanity.com/paper/2309.08652/) (2023)
- [Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management](https://mlsanity.com/paper/2309.16679/) (2023)
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- [A Note on Statistically Accurate Tabular Data Generation Using Large Language Models](https://mlsanity.com/paper/2505.02659/) (2025)
- [High-Quality Synthetic Financial Time-Series using a GAN-Diffusion Framework](https://mlsanity.com/paper/2605.27113/) (2026)
- [TVGL-CFM:Generating and Forecasting Time-Varying Trajectories of Dynamic Networks with Conditional Flow Matching](https://mlsanity.com/paper/2607.16894/) (2026)
- [Graph-Based Correlation Matrix Generation: A Convex Optimization Approach](https://mlsanity.com/paper/2607.22436/) (2026)
- [CorrFlow: Exact and Permutation-Equivariant Flow Matching on the Correlation Elliptope](https://mlsanity.com/paper/2610.13606/) (2026)

## Abstract (arXiv)

We propose a novel approach for sampling realistic financial correlation matrices. This approach is based on generative adversarial networks. Experiments demonstrate that generative adversarial networks are able to recover most of the known stylized facts about empirical correlation matrices estimated on asset returns. This is the first time such results are documented in the literature. Practical financial applications range from trading strategies enhancement to risk and portfolio stress testing. Such generative models can also help ground empirical finance deeper into science by allowing for falsifiability of statements and more objective comparison of empirical methods.