Dynamic credit default swaps curves in a network topology

Abstract

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.