Network reconstruction with UK CDS trade repository data

William Abel and Laura Silvestri

Quantitative Finance, 2017, vol. 17, issue 12, 1923-1932

Abstract: Despite post-crisis reforms in over-the-counter derivatives markets, regulators are left with incomplete, but still improved, data-sets. This means that methods for reconstructing networks of bilateral exposures from incomplete data are still necessary to conduct a proper assessment of systemic risk. In this paper, we propose a modification of the network reconstruction method developed by Cont and Moussa that includes additional information which is now available to regulators through post-crisis reforms. By making use of a data-set containing all transactions on UK single name CDS contracts, we assess the suitability of the proposed methodology by examining the characteristics of reconstructed and real networks. We find that the proposed methodology allows us to reconstruct networks that both comply with the newly available information, and are as heterogeneous and sparse with fat tailed in- and out- degree distributions as the real ones.

Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://hdl.handle.net/10.1080/14697688.2017.1357975 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:17:y:2017:i:12:p:1923-1932

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RQUF20

DOI: 10.1080/14697688.2017.1357975

Access Statistics for this article

Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral

More articles in Quantitative Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().