# Quantifying Credit Portfolio sensitivity to asset correlations with interpretable generative neural networks

arXiv: https://arxiv.org/abs/2309.08652 · HTML: https://mlsanity.com/paper/2309.08652/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 15 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [From Principal Subspaces to Principal Components with Linear Autoencoders](https://mlsanity.com/paper/1804.10253/) (2018)
- [CorrGAN: Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks](https://mlsanity.com/paper/1910.09504/) (2019)
- [Construction of Minimum Spanning Trees from Financial Returns using Rank Correlation](https://mlsanity.com/paper/2005.03963/) (2020)

## Built on by (most cited first, 1 of 1)

- [Synthetic Data for Portfolios: A Throw of the Dice Will Never Abolish Chance](https://mlsanity.com/paper/2501.03993/) (2025)

## Abstract (arXiv)

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.