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The risk of betting on risk: Conditional variance and correlation of bank credit default swaps
Author(s) -
Huang Xin
Publication year - 2020
Publication title -
journal of futures markets
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.88
H-Index - 55
eISSN - 1096-9934
pISSN - 0270-7314
DOI - 10.1002/fut.22068
Subject(s) - conditional variance , credit default swap , econometrics , kurtosis , conditional dependence , variance (accounting) , conditional expectation , correlation , stock (firearms) , credit risk , economics , mathematics , statistics , actuarial science , volatility (finance) , autoregressive conditional heteroskedasticity , accounting , engineering , mechanical engineering , geometry
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.