| By: |
Mario Cerrato;
John Crosby;
Minjoo Kim;
Yang Zhao |
| Abstract: |
We document asymmetric and time-varying features of dependence between the
credit risks of global systemically important banks (G-SIBs) in the UK banking
industry using a CDS dataset. We model the dependence of CDS spreads using a
dynamic asymmetric cop- ula. Comparing our model with traditional copula
models, we find that they usually under- estimate the probability of joint (or
conditional) default in the UK G-SIBs. Furthermore, we show that dynamics and
asymmetries between CDS spreads are closely associated with the probabilities
of joint (or conditional) default through the extensive regression analysis.
Especially, our regression analysis provides a policy implication that copula
correlation or tail dependence coefficients are able to be leading indicators
for the systemic credit event. |
| Keywords: |
Calibrated marginal default probability, probability of joint default, probability of conditional default, GAS-based GHST copula. |
| JEL: |
C32 G32 |
| Date: |
2015–10 |
| URL: |
http://d.repec.org/n?u=RePEc:gla:glaewp:2015_24&r=ifn |