Dynamic credit default swap curves in a network topology

Abstract

Systemically important banks are connected and their default probabilities have dynamic dependencies. An extraction of default factors from cross-sectional credit default swap (CDS) curves allows us to analyze the shape and the dynamics of default probabilities. In extending the Dynamic Nelson Siegel (DNS) model to an across firm multivariate setting, and employing the generalized variance decomposition of Diebold and Yilmaz [On the network topology of variance decompositions: Measuring the connectedness of financial firms. J. Econom., 2014, 182(1), 119–134], we are able to establish a DNS network topology. Its geometry yields a platform to analyze the interconnectedness of long-, middle- and short-term default factors in a dynamic fashion and to forecast the CDS curves. Our analysis concentrates on 10 financial institutions with CDS curves comprising of a wide range of time-to-maturities. The extracted level factor representing long-term default risk shows a higher level of total connectedness than those derived for short-term and middle-term default risk, respectively. US banks contributed more to the long-term default spillover before 2012, whereas European banks were major default transmitters during and after the European debt crisis, both in the long-term and short-term. The comparison of the network DNS model with alternatives proposed in the literature indicates that our approach yields superior forecast properties of CDS curves.

Keywords:

• CDS
• Network
• Default risk
• Variance decomposition
• Risk management

JEL classification:

• C32
• C51
• G17

Acknowledgments

The authors would like to thank the two anonymous referees and the editor of this journal for their valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Xiu Xu  http://orcid.org/0000-0002-7742-9601

Additional information

Funding

We gratefully acknowledge financial support through DFG via IRTG 1792 ‘High Dimensional Non Stationary Time Series’, Humboldt-Universität zu Berlin. Xiu Xu acknowledges the support of the Natural Science Foundation of China (Grant No. 71803140). The work was also partially supported by GACR EXPRO project 19-28231X.