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Extracting the sovereigns´ CDS market hierarchy: a correlation-filtering approach


  • Carlos Eduardo Léon Rincón


  • Karen Juliet Leiton


  • Jhonatan Pérez Villalobos



Since correlation may be interpreted as a measure of the influence across time-series, it may be conveniently mapped into a distance and into a weighted adjacency matrix. Based on such matrix, network theory has attempted to filter out the noise in correlation matrices by extracting the dominant hierarchy (i.e. the strongest linear-dependence signals) within time-series. The aim of this brief paper is to find the current hierarchy in the sovereigns´ CDS market after the structural shift caused by the failure of Lehman Brothers. Thus, based on two different correlation-into-distance mapping techniques and a minimal spanning tree-based correlation-filtering methodology on 36 sovereign CDS spread time-series, the target is to identify which sovereigns are providing the strongest -less noisy- and most informative signals. The resulting sovereigns´ CDS market hierarchy agrees with prior findings of Gilmore et al. (2010) regarding sovereigns´ bonds market, such as the importance of geographical clustering and the idiosyncratic nature of Japan and United States. Additionally, results (i) confirm that a small set of common factors affect the entire system; (ii) identify the relevance of credit rating clustering; (iii) identify Russia, Turkey and Brazil as regional benchmarks; (iv) suggest that lower-medium grade rated sovereigns are the most influential, but also the most prone to contagion; and (v) suggest the existence of a Latin American common factor". "

Suggested Citation

  • Carlos Eduardo Léon Rincón & Karen Juliet Leiton & Jhonatan Pérez Villalobos, 2013. "Extracting the sovereigns´ CDS market hierarchy: a correlation-filtering approach," BORRADORES DE ECONOMIA 010749, BANCO DE LA REPÚBLICA.
  • Handle: RePEc:col:000094:010749

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    References listed on IDEAS

    1. G. Bonanno & G. Caldarelli & F. Lillo & S. Micciché & N. Vandewalle & R. Mantegna, 2004. "Networks of equities in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 38(2), pages 363-371, March.
    2. Taleb, Nassim Nicholas, 2007. "Black Swans and the Domains of Statistics," The American Statistician, American Statistical Association, vol. 61, pages 198-200, August.
    3. Fratzscher, Marcel, 2012. "Capital flows, push versus pull factors and the global financial crisis," Journal of International Economics, Elsevier, vol. 88(2), pages 341-356.
    4. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    5. Gilmore, Claire G. & Lucey, Brian M. & Boscia, Marian W., 2010. "Comovements in government bond markets: A minimum spanning tree analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4875-4886.
    6. Carlos León & Karen Leiton & Alejandro Reveiz, 2012. "Investment horizon dependent CAPM: Adjusting beta for long-term dependence," Borradores de Economia 730, Banco de la Republica de Colombia.
    7. Eryiğit, Mehmet & Eryiğit, Resul, 2009. "Network structure of cross-correlations among the world market indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3551-3562.
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    9. Coudert, V. & Gex, M., 2010. "Credit default swap and bond markets: which leads the other?," Financial Stability Review, Banque de France, issue 14, pages 161-167, July.
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    11. Junnosuke Shino & Kouji Takahashi, 2010. "Sovereign Credit Default Swaps: Market Developments and Factors behind Price Changes," Bank of Japan Review Series 10-E-2, Bank of Japan.
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    Cited by:

    1. Carlos León & Geun-Young Kim & Constanza Martínez & Daeyup Lee, 2017. "Equity markets’ clustering and the global financial crisis," Quantitative Finance, Taylor & Francis Journals, vol. 17(12), pages 1905-1922, December.

    More about this item


    correlation; minimal spanning tree; correlation-filtering; sovereign; credit default swap;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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