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.
@article{arxiv.2107.10606,
title = {cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Distributions in the Elliptope},
author = {Gautier Marti and Victor Goubet and Frank Nielsen},
journal= {arXiv preprint arXiv:2107.10606},
year = {2021}
}
Comments
International Conference on Geometric Science of Information