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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". "

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Paper provided by BANCO DE LA REPÚBLICA in its series BORRADORES DE ECONOMIA with number 010749.

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Length: 26
Date of creation: 22 May 2013
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Handle: RePEc:col:000094:010749
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  1. Fratzscher, Marcel, 2011. "Capital Flows, Push versus Pull Factors and the Global Financial Crisis," CEPR Discussion Papers 8496, C.E.P.R. Discussion Papers.
  2. 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.
  3. Mizuno, Takayuki & Takayasu, Hideki & Takayasu, Misako, 2006. "Correlation networks among currencies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 364(C), pages 336-342.
  4. 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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  8. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B - Condensed Matter and Complex Systems, Springer, vol. 11(1), pages 193-197, September.
  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.
  10. 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.
  11. Brooks,Chris, 2008. "RATS Handbook to Accompany Introductory Econometrics for Finance," Cambridge Books, Cambridge University Press, number 9780521896955.
  12. L. Kullmann & J. Kertesz & K. Kaski, 2002. "Time dependent cross correlations between different stock returns: A directed network of influence," Papers cond-mat/0203256, arXiv.org, revised May 2002.
  13. 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.
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