CoRisk: Credit Risk Contagion with Correlation Network Models

Paolo Giudici,Laura Parisi

doi:10.3390/risks6030095

Abstract

We propose a novel credit risk measurement model for Corporate Default Swap (CDS) spreads that combines vector autoregressive regression with correlation networks. We focus on the sovereign CDS spreads of a collection of countries that can be regarded as idiosyncratic measures of credit risk. We model CDS spreads by means of a structural vector autoregressive model, composed by a time dependent country specific component, and by a contemporaneous component that describes contagion effects among countries. To disentangle the two components, we employ correlation networks, derived from the correlation matrix between the reduced form residuals. The proposed model is applied to ten countries that are representative of the recent financial crisis: top borrowing/lending countries, and peripheral European countries. The empirical findings show that the contagion variable derived in this study can be considered as a network centrality measure. From an applied viewpoint, the results indicate that, in the last 10 years, contagion has induced a “clustering effect” between core and peripheral countries, with the two groups further diverging through, and because of, contagion propagation, thus creating a sort of diabolic loop extremely difficult to be reversed. Finally, the outcomes of the analysis confirm that core countries are importers of risk, as contagion increases their CDS spread, whereas peripheral countries are exporters of risk. Greece is an unfortunate exception, as its spreads seem to increase for both idiosyncratic factors and contagion effects.

Highlights

The global financial crisis and, more recently, the European sovereign crisis, have led to an increasing research literature on systemic risk, with different definitions and measurement models
Peripheral countries, with high Corporate Default Swap (CDS) spreads, are impacted by contagion from other peripheral countries, and this effect is not mitigated by core countries as correlations with them are mostly negative
Our main methodological contribution consists in the introduction of partial correlations and correlation networks into VAR models, in order to disentangle the autoregressive component and the contemporaneous part that we have named CoRisk

Summary

Introduction

The global financial crisis and, more recently, the European sovereign crisis, have led to an increasing research literature on systemic risk, with different definitions and measurement models. The above approach is useful to establish policy thresholds aimed, in particular, at identifying the most systemic financial institutions, or the most systemic countries It is designed as a bivariate approach: on one hand, this allows for calculating the risk of an institution conditional on a reference market; on the other hand, such approach does not address the issue of how risks are transmitted between different institutions in a multivariate framework. A different stream of research considers systemic risks as exogenous and explains them by means of causal factors This approach has been proposed, in particular, by Ang and Longstaff (2013), Betz et al (2014), Duprey et al (2015), Schwaab et al (2016) and Ramsay and Sarlin (2016), who explain whether the default probability of a bank, of a country, or of a company depends on a set of exogenous risk sources, combining idiosyncratic and systematic factors. While powerful from an early warning perspective, causal models, to bivariate ones, concentrate on single institutions rather than on the financial system as a whole and, may underestimate systemic sources of risk arising from contagion effects within the system

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