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CoRisk: Credit Risk Contagion with Correlation Network Models

Paolo Giudici () and Laura Parisi ()

Risks, 2018, vol. 6, issue 3, 1-19

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.

Keywords: corporate default swap spreads; correlation networks; contagion; vector autoregressive regression (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 M2 M4 K2 (search for similar items in EconPapers)
Date: 2018
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Handle: RePEc:gam:jrisks:v:6:y:2018:i:3:p:95-:d:169274