Uncovering the mesoscale structure of the credit default swap market to improve portfolio risk modelling

TL;DR

This paper uncovers hidden intermediate-level structures in the CDS market using data-driven correlation analysis and Random Matrix Theory, revealing groups of issuers beyond traditional categories, and introduces a new risk model that outperforms existing ones.

Contribution

The paper presents a novel data-driven method based on Random Matrix Theory to identify mesoscopic structures in the CDS market, improving risk modeling accuracy.

Findings

Identified non-standard issuer groups not explained by traditional categories.

Developed a hierarchical decomposition method for correlation matrices.

Proposed a default risk model outperforming traditional models.

Abstract

One of the most challenging aspects in the analysis and modelling of financial markets, including Credit Default Swap (CDS) markets, is the presence of an emergent, intermediate level of structure standing in between the microscopic dynamics of individual financial entities and the macroscopic dynamics of the market as a whole. This elusive, mesoscopic level of organisation is often sought for via factor models that ultimately decompose the market according to geographic regions and economic industries. However, at a more general level the presence of mesoscopic structure might be revealed in an entirely data-driven approach, looking for a modular and possibly hierarchical organisation of the empirical correlation matrix between financial time series. The crucial ingredient in such an approach is the definition of an appropriate null model for the correlation matrix. Recent research showed that community detection techniques developed for networks become intrinsically biased when applied to correlation matrices. For this reason, a method based on Random Matrix Theory has been developed, which identifies the optimal hierarchical decomposition of the system into internally correlated and mutually anti-correlated communities. Building upon this technique, here we resolve the mesoscopic structure of the CDS market and identify groups of issuers that cannot be traced back to standard industry/region taxonomies, thereby being inaccessible to standard factor models. We use this decomposition to introduce a novel default risk model that is shown to outperform more traditional alternatives.