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

Paper: arXiv 2309.08652

Authors: Sergio Caprioli, Emanuele Cagliero, Riccardo Crupi

Abstract

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.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents an innovative application of Variational Autoencoders (VAEs) to generate correlation matrices for Credit Value-at-Risk sensitivity analysis. By leveraging a structured VAE latent space, the authors offer an interpretable alternative to traditional GAN approaches in risk management. The methodology combines sound neural network architecture with quantitative risk sensitivity analysis, though reproducibility is slightly limited by missing code links.

Research Flowchart

  flowchart TD
    A[Goal: Quantify Credit Portfolio VaR Sensitivity to Correlation Changes] --> B[Data Input: Historical Asset Return Data & Empirical Correlation Matrices]
    B --> C[Method: Train Variational Autoencoder vs GANs]
    C --> D[Generate Synthetic Correlation Matrices via VAE Latent Space]
    D --> E[Simulate Credit Portfolio Value-at-Risk]
    E --> F[Outcome: Interpretable Latent Space Captures Diversification & VaR Sensitivity]