The risk of betting on risk: Conditional variance and correlation of bank credit default swaps

Credit default swaps (CDS) have been used to speculate on the default risk of the reference entity. The risk of CDS can be measured by their second moments. We apply a Glosten, Jagannathan, and Runkle (GJR)-t model for the conditional variance and a Dynamic Conditional Correlation (DCC)-t model for the conditional correlation. Based on the CDS of six large US banks from 2002 to 2018, we find that CDS conditional variance is asymmetric and leptokurtic. A positive innovation actually increases CDS conditional variance more than a negative innovation does. CDS conditional correlations have stayed elevated since the financial crisis, in contrast to the decreasing stock conditional correlations.