  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.
