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Related Experiment Videos

Credit Default Swaps networks and systemic risk.

Michelangelo Puliga1, Guido Caldarelli2, Stefano Battiston3

  • 1IMT Alti Studi Lucca, Piazza San Francesco 19, Lucca, Italy.

Scientific Reports
|November 5, 2014
PubMed
Summary

Credit Default Swaps (CDS) network correlations reveal structural changes at the 2008 crisis onset, not before. Incorporating US housing price data with Group DebtRank stress tests improved early detection of systemic risk in financial institutions.

Related Experiment Videos

Area of Science:

  • Financial economics
  • Network science
  • Systemic risk analysis

Background:

  • Credit Default Swaps (CDS) spreads are expected to reflect corporate debt default risk.
  • Empirical evidence shows CDS spreads often lag, rather than lead, increasing default risk.
  • Network analysis of CDS spread correlations may offer early warnings of systemic risk.

Purpose of the Study:

  • To investigate if network structures of CDS spread time series exhibit changes signaling systemic risk.
  • To assess the utility of these networks as a proxy for interdependencies among financial institutions.
  • To evaluate the effectiveness of stress testing incorporating macroeconomic factors for early risk detection.

Main Methods:

  • Analysis of 176 Credit Default Swaps (CDS) time series for financial institutions (2002-2011).
  • Construction of correlation-based networks from CDS spread data.
  • Application of Group DebtRank stress tests, integrating a US housing price macroeconomic indicator.

Main Results:

  • Network structural changes were observed coinciding with the 2008 credit crisis, but not prior.
  • Systemic risk, as measured by Group DebtRank, increased pre-2008 only when US housing price data was included.
  • The study demonstrates the potential of network analysis combined with macroeconomic indicators for risk assessment.

Conclusions:

  • Interdependencies among financial institutions, proxied by CDS spread networks, show structural shifts during crises.
  • Macroeconomic indicators, specifically related to housing prices, are crucial for early systemic risk detection.
  • The proposed approach offers a promising method for identifying and potentially mitigating systemic financial instabilities.