Publication Type

Journal Article

Version

publishedVersion

Publication Date

3-2019

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

Discipline

Finance | Finance and Financial Management

Publication

Quantitative Finance

Volume

19

Issue

10

First Page

1705

Last Page

1726

ISSN

1469-7688

Identifier

10.1080/14697688.2019.1585560

Publisher

Taylor and Francis Group

Additional URL

https://doi.org/10.1080/14697688.2019.1585560

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