Santos, Carlos (2011): The euro sovereign debt crisis, determinants of default probabilities and implied ratings in the CDS market: an econometric analysis.

PDF
MPRA_paper_31341.pdf Download (168kB)  Preview 
Abstract
In this paper we take an innovative econometric look at the Euro Zone Sovereign Debt Crisis. We are particularly interested in understanding which determinants have led investors to ask for higher yields on sovereign debt from the Euro shatter belt. We dismiss the definition of speculation previously used in the literature, on the basis of the irrelevance of Granger Causality as an operational tool for this purpose. Instead, we suggest that speculative behavior would only exist if market assessment would be unrelated to economic fundamentals of such countries. Using a cross section of countries, we improve on the scarce literature on the Econometrics of Credit Default Swap Markets on sovereign debt. Firstly, we use an ordered probit model to determine whether economic fundamentals are driving the implied rating assessments. Secondly, we provide a pioneering application of quantile regression to this domain, to determine which variables matter at different conditional quantiles of the implied default probability distribution. Finally, Fisher’s Z statistic is used to relate bond markets to domestic saving rates. Overall, the different methodologies support the conclusion that the domestic savings rate is lenders’ main concern. Economies with worse saving habits are penalized both in the CDS market, and in the sovereign bonds markets. Notwithstanding, for countries on the top quantiles of the implied default probabilities, public debt and external debt also play a significant role, increasing the likelihood of higher insurance premium in the derivatives market. When looking at the Portuguese Case it seems clear that public policies that fail to take savings into proper account shall always be deemed to fail, as the country had the lowest net savings rate in the EU27 in 2008, followed closely by Greece.
Item Type:  MPRA Paper 

Original Title:  The euro sovereign debt crisis, determinants of default probabilities and implied ratings in the CDS market: an econometric analysis 
Language:  English 
Keywords:  sovereign debt; Euro Area; Credit Default Swaps; Quantile Regression; Ordered Probit; savings rate 
Subjects:  G  Financial Economics > G1  General Financial Markets > G12  Asset Pricing ; Trading Volume ; Bond Interest Rates H  Public Economics > H6  National Budget, Deficit, and Debt > H63  Debt ; Debt Management ; Sovereign Debt E  Macroeconomics and Monetary Economics > E2  Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy > E21  Consumption ; Saving ; Wealth C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C21  CrossSectional Models ; Spatial Models ; Treatment Effect Models ; Quantile Regressions C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C25  Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions ; Probabilities 
Item ID:  31341 
Depositing User:  Carlos Santos 
Date Deposited:  08. Jun 2011 12:48 
Last Modified:  31. Dec 2015 02:55 
References:  Aitchison, J. and Silvey, S. D. (1957), “The Generalization of Probit Analysis to the Case of Multiple Responses”, Biometrika, 44, pp. 131140. Ashford, J. R. (1959), “An Approach to the Analysis of Data for SemiQuantal Responses in Biology Response”, Biometrics, 15, pp. 573581. Cameron, A. C. and Trivedi, P. K. (2005), Microeconometrics Methods and Applications, Oxford: Cambridge University Press. Cantor, R. and Packer, F. (1996), “Determinants and impact of sovereign credit ratings”, Economic Policy Review, Federal Reserve Bank of New York, issue Oct, pp. 37 53. CMAVISION (2011), CMA Global Sovereign Debt Credit Risk Report, 4th quarter 2010, CMA. Cox, D. R. (1970), The Analysis of Binary Data, London: Methuen. 16 Damette, O. and Frouté, P. (2010) Is the crisis treatment exacerbating cautiousness or risktaking?, Applied Financial Economics, 20: 3, 213 — 218 Engle, R. F. and Manganelli, S. (2004), “CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles”, Journal of Business & Economic Statistics, American Statistical Association, 22, pp. 367381. Fisher, R. A. (1915), “Frequency distribution of the values of the correlation coefficient in samples of an indefinitely large population”, Biometrika (Biometrika Trust), 10 (4), pp. 507521. Fisher, R. A. (1921), “On the ‘probable error’ of a coefficient of correlation deduced from a small sample”, Metron, 1, pp. 332. Gayen, A. K. (1951), “The Frequency Distribution of the ProductMoment Correlation Coefficient in Random Samples of Any Size Drawn from NonNormal Universes”, Biometrika (Biometrika Trust), 38 (1/2), pp. 219247. Granger, C. W. (1969), Investigating causal relations by econometric models and crossspectral methods. Econometrica 37 (3): 424–438. Gonzalez, A. (2010) La década perdida de Italia e Portugal, El País, 24/10/2010 (http://www.elpais.com/articulo/primer/plano/decada/perdida/Italia/Portugal/elpepu econeg/20101024elpneglse_3/Tes) Gourieroux, C. and Jasiak, J. (2008), “Dynamic quantile models”, Journal of Econometrics, Elsevier, 147 (1), pp. 198205. Gros, D. (2010), Greek Burdens ensure some PIGS won’t fly, Financial Times (http://www.ft.com/cms/s/0/1a568f140c4011df8b81 00144feabdc0.html#axzz1FYUIE7WF) Gurland, J., Lee, I. and Dahm, P. A. (1960), “Polychotomous quantal response in biological assay”, Biometrics, 16, pp. 382397. Hausman, J. A., Lo, A. W. and MacKinlay, A. C. (1992), “An ordered probit analysis of transaction stock prices”, Journal of Financial Economics, Elsevier, 31 (3), pp. 319379. Hendry, D. F. and Santos (2010), Automatic Tests for Super Exogeneity, in Bollerslev, T., Russell, J. and Bollerslev, T. (eds) Volatility and Time Series Econometrics, Essays in Honour of Robert Engle, Advanced Texts in Econometrics, Oxford University Press Horowitz, J. L. (1992), “A Smooth Maximum Score Estimator for the Binary Response Model”, Econometrica, 60, pp. 505531. 17 Koenker, R. ad Bassett, G. (1978), “Regression Quantiles”, Econometrica, 46 (1), pp. 33 50. Koenker, R. and Bilias, Y. (2001), “Quantile Regression for Duration Data: a reappraisal of the Pennsylvania Reemployment Bonus Experiments”, Empirical Economics, 26 (1), pp. 199220. Koenker, R. and Geling, O. (2001), “Reappraising Medfly Longevity: A Quantile Regression Survival Analysis”, Journal of the American Statistical Association, 96, pp. 458468. Lee (1992), Lee, M.J. (1992), “Median Regression for Ordered Discrete Response”, Journal of Econometrics, 51, pp. 5977. Machado, J. A. S. and Silva, J. M. C. S. (2005), “Quantiles for Counts”, Journal of the American Statistical Association, 100, pp. 12261237. Manski, C. F. (1985), “Semiparametric Analysis of Discrete Response: Asymptotic Properties of the Maximum Score Estimator”, Journal of Econometrics, 27, pp. 313 333. McKelvey, R. D. and Zavoina, W. (1975) “A statistical model for the analysis of ordinal level dependent variables”, Journal of Mathematical Sociology, 4, pp. 103120. Powell, J. L. (1984), “Least Absolute Deviation Estimation for the Censored Regression Model”, Journal of Econometrics, 25, pp. 303325. Powell, J. L. (1986), “Censored Regression Quantiles”, Journal of Econometrics, 32, pp. 143155. Santos, C. (2008), Impulse Saturation Break Tests, Economics Letters, 98(2), pp.136 143 Santos, C., Hendry, D. F. and Johansen, S. (2008) Automatic Selection of Indicators in a fully Saturated Regression, Computational Statistics, 23(2), pp. 317335 Tsay, R. (2010), Analysis of Financial Time Series, 3rd Edition, Wiley, NY White, H., Kim, TH. and Manganelli, S. (2008), “Modeling autoregressive conditional skewness and kurtosis with Multiquantile CAViaR”, Working Paper Series 957, European Central Bank. Wooldridge, J. (2002), Econometric Analysis of Cross Section and Panel Data, MIT Press. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/31341 