Systemically important banks are connected and have dynamic dependencies of their
default probabilities. An extraction of default factors from cross-sectional credit default
swaps (CDS) curves allows to analyze the shape and the dynamics of the default probabilities.
Extending the Dynamic Nelson Siegel (DNS) model, we propose a network DNS model
to analyze the interconnectedness of default factors in a dynamic fashion, and forecast the
CDS curves. The extracted level factors representing long-term default risk demonstrate
85.5% total connectedness, while the slope and the curvature factors document 79.72% and
62.94% total connectedness for the short-term and middle-term default risk, respectively.
The issues of default spillover and systemic risk should be weighted for the market participants
with longer credit exposures, and for regulators with a mission to stabilize financial
markets. The US banks contribute more to the long-run default spillover before 2012,
whereas the European banks are major default transmitters during and after the European
debt crisis either in the long-run or short-run. The outperformance of the network DNS
model indicates that the prediction on CDS curve requires network information.
CDS, network, default risk, variance decomposition, risk management