Default count-based network models for credit contagion

Abstract

Interconnectedness between economic institution and sectors, already recognised as a trigger of the great financial crisis in 2008–2009, is assuming growing importance in financial systems. In this article, we study contagion effects between corporate sectors using financial network models, in which the significant links are identified through conditional independence testing. While the existing financial network literature is mostly focused on Gaussian processes, our approach is based on discrete data. We indeed test dependence in the conditional mean (and volatility) of default counts in different economic sector estimated from Poisson autoregressive models, and in their shocks. Our empirical application to Italian corporate defaults in the 1996–2018 period reveals evidence of a high inter-sector vulnerability, especially at the onset of the global financial crisis in 2008 and in the following years. Many contagion effects between corporate sectors are indeed found in the shock component of the default count dynamics.

Keywords:

• Financial networks
• inter-sector contagion
• Poisson autoregressive models
• vector autoregressive models
• conditional Granger causality
• PC-algorithm

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 http://www.bancaditalia.it/statistiche.

2 Real Estate includes the construction and real estate categories; Primary contains both agriculture and mining activities, as in its typical definition; Services includes several categories of the service industry, such as travel, food services and business support.

3 http://www.stlouisfed.org.

Additional information

Funding

This study was funded by European Union’s Horizon 2020 research and innovationprogramme under grant agreement No 825215.