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cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Distributions in the Elliptope (2107.10606v1)
Published 22 Jul 2021 in q-fin.ST and cs.LG
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.