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.

翻译：我们提出一种方法,在有条件的基因对抗性网络基础上,在相关矩阵的ellipope 中大致有条件分布。我们用定量融资的应用来说明这一方法:蒙特卡洛相关回报模拟,以比较基于风险的组合建设方法。最后,我们讨论了目前的局限性,并主张进一步探索ellipope几何方法,以改善结果。