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Credit Default Swaps Drawup Networks: Too Interconnected to Be Stable?

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  • Rahul Kaushik
  • Stefano Battiston

Abstract

We analyse time series of CDS spreads for a set of major US and European institutions in a period overlapping the recent financial crisis. We extend the existing methodology of -drawdowns to the one of joint -drawups, in order to estimate the conditional probabilities of spike-like co-movements among pairs of spreads. After correcting for randomness and finite size effects, we find that, depending on the period of time, 50% of the pairs or more exhibit high probabilities of joint drawups and the majority of spread series are trend-reinforced, i.e. drawups tend to be followed by drawups in the same series. We then carry out a network analysis by taking the probability of joint drawups as a proxy of financial dependencies among institutions. We introduce two novel centrality-like measures that offer insights on how both the systemic impact of each node as well as its vulnerability to other nodes' shocks evolve in time.

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  • Rahul Kaushik & Stefano Battiston, 2013. "Credit Default Swaps Drawup Networks: Too Interconnected to Be Stable?," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-8, July.
    • Handle: RePEc:plo:pone00:0061815
    DOI: 10.1371/journal.pone.0061815
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    5. Christoph Aymanns & J. Doyne Farmer & Alissa M. Keinniejenhuis & Thom Wetzer, 2017. "Models of Financial Stability and their Application in Stress Tests," Working Papers on Finance 1805, University of St. Gallen, School of Finance.
    6. Kyu-Min Lee & Kwang-Il Goh, 2016. "Strength of weak layers in cascading failures on multiplex networks: case of the international trade network," Papers 1603.05181, arXiv.org, revised May 2016.
    7. Vahan Nanumyan & Antonios Garas & Frank Schweitzer, 2015. "The Network of Counterparty Risk: Analysing Correlations in OTC Derivatives," Papers 1506.04663, arXiv.org, revised Sep 2015.
    8. Andre R. Neveu, 2018. "A survey of network-based analysis and systemic risk measurement," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(2), pages 241-281, July.
    9. Berardi, Simone & Tedeschi, Gabriele, 2017. "From banks' strategies to financial (in)stability," International Review of Economics & Finance, Elsevier, vol. 47(C), pages 255-272.
    10. Priya Malhotra & Pankaj Sinha, 2024. "Balanced Funds in India Amid COVID-19 Crisis: Spreader of Financial Contagion?," IIM Kozhikode Society & Management Review, , vol. 13(1), pages 7-24, January.
    11. Chen, Wang & Ho, Kung-Cheng & Yang, Lu, 2020. "Network structures and idiosyncratic contagion in the European sovereign credit default swap market," International Review of Financial Analysis, Elsevier, vol. 72(C).
    12. Choi, Sun-Yong, 2022. "Credit risk interdependence in global financial markets: Evidence from three regions using multiple and partial wavelet approaches," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    13. Ioannis Anagnostou & Sumit Sourabh & Drona Kandhai, 2018. "Incorporating Contagion in Portfolio Credit Risk Models Using Network Theory," Complexity, Hindawi, vol. 2018, pages 1-15, January.
    14. Vahan Nanumyan & Antonios Garas & Frank Schweitzer, 2015. "The Network of Counterparty Risk: Analysing Correlations in OTC Derivatives," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-23, September.
    15. Jamshid Ardalankia & Jafar Askari & Somaye Sheykhali & Emmanuel Haven & G. Reza Jafari, 2020. "Mapping Coupled Time-series Onto Complex Network," Papers 2004.13536, arXiv.org, revised Aug 2020.

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