  In this research, we propose a novel approach for the quantification of
credit portfolio Value-at-Risk (VaR) sensitivity to asset correlations with the
use of synthetic financial correlation matrices generated with deep learning
models. In previous work Generative Adversarial Networks (GANs) were employed
to demonstrate the generation of plausible correlation matrices, that capture
the essential characteristics observed in empirical correlation matrices
estimated on asset returns. Instead of GANs, we employ Variational Autoencoders
(VAE) to achieve a more interpretable latent space representation. Through our
analysis, we reveal that the VAE latent space can be a useful tool to capture
the crucial factors impacting portfolio diversification, particularly in
relation to credit portfolio sensitivity to asset correlations changes.
