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Measuring connectedness of Euro area sovereign risk

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Listed:
  • Gätjen, Rebekka
  • Schienle, Melanie

Abstract

We introduce a methodology for measuring default risk connectedness that is based on an out-of-sample variance decomposition of model forecast errors. The out-of-sample nature of the procedure leads to "realized" measures which, in practice, respond more quickly to crisis occurrences than those based on in-sample methods. The resulting relative and absolute connectedness measures find distinct and complementary information from CDS and bond yield data on European area sovereign risk. The detection and use of these second moment di erences of CDS and bond data is new to the literature and allows to identify countries that impose risk on the system from those which sustain risk.

Suggested Citation

  • Gätjen, Rebekka & Schienle, Melanie, 2015. "Measuring connectedness of Euro area sovereign risk," SFB 649 Discussion Papers 2015-019, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    • Handle: RePEc:zbw:sfb649:sfb649dp2015-019
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    References listed on IDEAS

    as
    1. De Santis, Roberto A., 2012. "The Euro area sovereign debt crisis: safe haven, credit rating agencies and the spread of the fever from Greece, Ireland and Portugal," Working Paper Series 1419, European Central Bank.
    2. Francis A. Longstaff & Sanjay Mithal & Eric Neis, 2005. "Corporate Yield Spreads: Default Risk or Liquidity? New Evidence from the Credit Default Swap Market," Journal of Finance, American Finance Association, vol. 60(5), pages 2213-2253, October.
    3. Beirne, John & Fratzscher, Marcel, 2013. "The pricing of sovereign risk and contagion during the European sovereign debt crisis," Journal of International Money and Finance, Elsevier, vol. 34(C), pages 60-82.
    4. Peter DeMarzo & Darrell Duffie, 1999. "A Liquidity-Based Model of Security Design," Econometrica, Econometric Society, vol. 67(1), pages 65-100, January.
    5. Avino, Davide & Nneji, Ogonna, 2014. "Are CDS spreads predictable? An analysis of linear and non-linear forecasting models," International Review of Financial Analysis, Elsevier, vol. 34(C), pages 262-274.
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    Keywords

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    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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • F34 - International Economics - - International Finance - - - International Lending and Debt Problems
    • G01 - Financial Economics - - General - - - Financial Crises
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation

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