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cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Distributions in the Elliptope

2021-07-22 12:17:23
Gautier Marti, Victor Goubet, Frank Nielsen
arXiv_CV
             
Abstract

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.

Abstract (translated)

我们提出了一种方法,以基于条件生成对抗网络的条件概率分布逼近关联矩阵的Elliptope。我们使用 quantitative finance 的一个应用来展示这种方法:对相关回报进行蒙特卡罗模拟,比较基于风险的组合构建方法。最后,我们讨论了当前的局限性,并倡导进一步探索Elliptope几何以提高结果。

URL

https://arxiv.org/abs/2107.10606

PDF

https://arxiv.org/pdf/2107.10606.pdf

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