preprints.org > computer science and mathematics > probability and statistics > doi: 10.20944/preprints201807.0353.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 18 July 2018 / Approved: 19 July 2018 / Online: 19 July 2018 (10:16:11 CEST)

We propose a novel credit risk measurement model for Corporate Default Swap 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 them by means of a vector autoregressive regression 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 proposed model is a good predictor of CDS spreads movements, and that the contemporaneous component decreases prediction errors with respect to a simpler autoregressive model. From an applied viewpoint, core countries appear to import risk, as contagion increases their CDS spread, whereas peripheral countries appear as exporters of risk. Greece is an unfortunate exception, as its spreads seem to increase for both idiosyncratic factors and contagion effects.

corporate default swap spreads, correlation networks, vector autoregressive regression.

Computer Science and Mathematics, Probability and Statistics

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