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

Gautier Marti; Victor Goubet; Frank Nielsen

doi:10.1007/978-3-030-80209-7_66

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arXiv · 2107.10606

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

Gautier Marti·Victor Goubet·Frank Nielsen

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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.

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Gautier Marti, Victor Goubet, Frank Nielsen. 2021-07-22. cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Distributions in the Elliptope. https://doi.org/10.1007/978-3-030-80209-7_66

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