cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Distributions in the Elliptope
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Abstract (arXiv)
We propose a methodology to approximate conditional distributions in the elliptope of correlation matrices based on conditional generative adversarial networks. We illustrate the methodology with an application from quantitative finance: Monte Carlo simulations of correlated returns to compare risk-based portfolio construction methods. Finally, we discuss about current limitations and advocate for further exploration of the elliptope geometry to improve results.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 7 of its 27 references that are in the map. First posted on arXiv 22 Jul 2021.
Builds on
7 of 27 references.
References · 7
- 2016A Riemannian Network for SPD Matrix Learningcited by 162
- 2019Quant GANs: Deep Generation of Financial Time Seriescited by 108
- 2019Riemannian batch normalization for SPD neural networkscited by 72
- 2019Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combinationcited by 22
- 2019Time Series Simulation by Conditional Generative Adversarial Netcited by 16
- 2019CorrGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networkscited by 16
- 2017Clustering in Hilbert simplex geometrycited by 4
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Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.
2 papers.