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The Euro Sovereign Debt Crisis, Determinants Of Default Probabilities And Implied Ratings In The Cds Market: An Econometric Analysis

  • Carlos Santos

    ()

    (Portuguese Catholic University and CEGE, Portugal)

In this paper, we investigate what has been leading investors to ask for higher yields on sovereign debt from certain Euro countries. We dismiss Granger Causality as a basis to define speculation. Instead, we assume that speculative behavior would only exist if market assessments were unrelated to economic fundamentals. Using a cross section of countries, we improve on the literature on Credit Default Swap Markets on sovereign debt. Firstly, we use an ordered probit to determine whether fundamentals are driving ratings. Then, quantile regression determines which variables matter at different conditional quantiles of the default probability. Finally, Fisher's Z statistic is used to relate bond yields to domestic savings. The different methods support the conclusion that the domestic savings rate is lenders' main concern. Economies with worse saving habits are penalized both in the CDS and in the bonds market. Notwithstanding, for countries on the top quantiles of the default probabilities, public and external debt also increase the insurance premium in the derivatives market. Looking at the Portuguese case, it is clear that policies that don't take savings into account shall fail, as the country had the lowest net savings rate in the EU27 in 2008, followed closely by Greece.

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Article provided by ASERS Publishing in its journal Journal of Advanced Studies in Finance.

Volume (Year): II (2011)
Issue (Month): 1 (June)
Pages: 53-61

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Handle: RePEc:srs:jasf12:5:v:2:y:2011:i:1:p:53-61
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  1. David Hendry & Søren Johansen & Carlos Santos, 2008. "Automatic selection of indicators in a fully saturated regression," Computational Statistics, Springer, vol. 23(2), pages 337-339, April.
  2. Hausman, Jerry A. & Lo, Andrew W. & MacKinlay, A. Craig, 1992. "An ordered probit analysis of transaction stock prices," Journal of Financial Economics, Elsevier, vol. 31(3), pages 319-379, June.
  3. Santos, Carlos, 2008. "Impulse saturation break tests," Economics Letters, Elsevier, vol. 98(2), pages 136-143, February.
  4. Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
  5. Yannis Bilias & Roger Koenker, 2001. "Quantile regression for duration data: A reappraisal of the Pennsylvania Reemployment Bonus Experiments," Empirical Economics, Springer, vol. 26(1), pages 199-220.
  6. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  7. Richard Cantor & Frank Packer, 1996. "Determinants and impacts of sovereign credit ratings," Research Paper 9608, Federal Reserve Bank of New York.
  8. Jose A. F. Machado & J. M. C. Santos Silva, 2002. "Quantiles for counts," CeMMAP working papers CWP22/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  9. Koenker R. & Geling O., 2001. "Reappraising Medfly Longevity: A Quantile Regression Survival Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 458-468, June.
  10. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
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  12. Manski, Charles F., 1985. "Semiparametric analysis of discrete response : Asymptotic properties of the maximum score estimator," Journal of Econometrics, Elsevier, vol. 27(3), pages 313-333, March.
  13. Lee, Myoung-jae, 1992. "Median regression for ordered discrete response," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 59-77.
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