Are Sovereign Credit Ratings Overrated? (original) (raw)

Abstract

In this paper we examine the relevance of changes in sovereign credit rating for the borrowing cost of EU countries. Our results indicate that discretionary credit rating announcements are only of limited economic importance for the borrowing cost of these countries. It seems that rating agencies do not reveal important new information to financial markets, in addition to that already contained in the underlying fundamentals. Hence, given the sentiment in financial markets, the borrowing cost of a country can only be reduced by improving macroeconomic and fiscal fundamentals.

Figures (17)

Note: Dependent variables are daily changes in CDS spreads. The symbols *k* ** and * denote statistical significance at the 1%, 5% and 10% level, based on the White robust standard error estimator. Table 1: The impact of rating downgrades/upgrades on CDS spreads

Note: Dependent variables are daily changes in CDS spreads. The symbols *k* ** and * denote statistical significance at the 1%, 5% and 10% level, based on the White robust standard error estimator. Table 1: The impact of rating downgrades/upgrades on CDS spreads

Table 2: The impact of rating downgrades on CDS spreads for different rating categories

Table 2: The impact of rating downgrades on CDS spreads for different rating categories

Table 4: Hausman test for credit rating determinants equations Results The results of the estimated specifications with fixed effects are shown in Ta- ble 5, where each column represents the estimated parameters for the corresponding rating agency and for the average rating. The first four columns refer to the baseline specification. while the last four columns are related to the non-linear specification which includes the interaction dummy variable described earlier. When interpreting our results one should bea in mind that the main objective of this analysis was not to estimate and interpret individua. model elasticities. Therefore the estimated specification contains a relatively large number of correlated regressors which raises the issue of multicollinearity. However, the primary ob- jective of our analysis is the aggregate influence of fundamentals on rating levels which was the reason we chose such a wider set of regressors.

Table 4: Hausman test for credit rating determinants equations Results The results of the estimated specifications with fixed effects are shown in Ta- ble 5, where each column represents the estimated parameters for the corresponding rating agency and for the average rating. The first four columns refer to the baseline specification. while the last four columns are related to the non-linear specification which includes the interaction dummy variable described earlier. When interpreting our results one should bea in mind that the main objective of this analysis was not to estimate and interpret individua. model elasticities. Therefore the estimated specification contains a relatively large number of correlated regressors which raises the issue of multicollinearity. However, the primary ob- jective of our analysis is the aggregate influence of fundamentals on rating levels which was the reason we chose such a wider set of regressors.

Table 5: Determinants of sovereign credit ratings

Table 5: Determinants of sovereign credit ratings

Table 6: Precision of the estimated credit rating models models for Fitch and for the average rating predicted the ratings with a high degree of accu- racy within one notch (91%), while the model for Moody’s is slightly less precise (85% for the linear and 89% for the non-linear specification). Models for all agencies are able to predict ratings within two notches with a very high degree of accuracy, exceeding 95%. These results suggest that by using the standard set of macroeconomic and fiscal fundamentals one may very precisely replicate the ratings of European countries. THT hee * ye Se | r 1 a | a _: a ca: se 1 2 shy r W 1 1

Table 6: Precision of the estimated credit rating models models for Fitch and for the average rating predicted the ratings with a high degree of accu- racy within one notch (91%), while the model for Moody’s is slightly less precise (85% for the linear and 89% for the non-linear specification). Models for all agencies are able to predict ratings within two notches with a very high degree of accuracy, exceeding 95%. These results suggest that by using the standard set of macroeconomic and fiscal fundamentals one may very precisely replicate the ratings of European countries. THT hee * ye Se | r 1 a | a _: a ca: se 1 2 shy r W 1 1

Table 7: The effect of the rating overestimation indicator, rating implied by fundamentals and risk aversion index on CDS spreads ating agencies for CDS spreads in comparison to the relative importance of economic funda- nentals and other factors. The relative importance of individual variables in the model may ye analyzed in the context of the importance of the variable for: 1) describing the variability fs preads and 2) the importance of the variable for describing the level of spreads. For the yurpose of examining the relative importance of a regressor for describing the variability of he of t pp y the method d dependent variable in a regression, it is necessary to analyze the marginal effect of each he used regressors on the R? statistic. In order to carry out the said decomposition, we escribed in Lindeman, Merenda and Gold (1980), which was previously ised to decompose European spreads in Kunovac (2013). For each possible variable ranking nd each variable in the model this method calculates the marginal influence its inclusion 1as on the R? statistic. The final estimate of the contribution of the variance of a given variable is calculated as the average of these marginal contributions over all possible orders yf the variables in the model. Table 8 shows the results of the mentioned variance decomposition of CDS spreads to

Table 7: The effect of the rating overestimation indicator, rating implied by fundamentals and risk aversion index on CDS spreads ating agencies for CDS spreads in comparison to the relative importance of economic funda- nentals and other factors. The relative importance of individual variables in the model may ye analyzed in the context of the importance of the variable for: 1) describing the variability fs preads and 2) the importance of the variable for describing the level of spreads. For the yurpose of examining the relative importance of a regressor for describing the variability of he of t pp y the method d dependent variable in a regression, it is necessary to analyze the marginal effect of each he used regressors on the R? statistic. In order to carry out the said decomposition, we escribed in Lindeman, Merenda and Gold (1980), which was previously ised to decompose European spreads in Kunovac (2013). For each possible variable ranking nd each variable in the model this method calculates the marginal influence its inclusion 1as on the R? statistic. The final estimate of the contribution of the variance of a given variable is calculated as the average of these marginal contributions over all possible orders yf the variables in the model. Table 8 shows the results of the mentioned variance decomposition of CDS spreads to

Table 8: CDS spreads variance decomposition

Table 8: CDS spreads variance decomposition

Note: Dependent variables are the credit rating levles. The symbols ***, ** and * denote statistical significance at the 1%, 5% and 10% level, based on the White robust standard error estimator. Table 9: Determinants of sovereign credit ratings for different alternative specifications

Note: Dependent variables are the credit rating levles. The symbols ***, ** and * denote statistical significance at the 1%, 5% and 10% level, based on the White robust standard error estimator. Table 9: Determinants of sovereign credit ratings for different alternative specifications

Figure 1: Rating implied by fundamentals and observed rating for S&P

Figure 1: Rating implied by fundamentals and observed rating for S&P

Figure 2: Rating implied by fundamentals and observed rating for Moody’s

Figure 2: Rating implied by fundamentals and observed rating for Moody’s

Figure 3: Rating implied by fundamentals and observed rating for Fitch

Figure 3: Rating implied by fundamentals and observed rating for Fitch

Figure 4: Rating implied by fundamentals and observed average rating

Figure 4: Rating implied by fundamentals and observed average rating

Figure 5: Contribution of the rating overestimation indicator (S&P) to the level of CDS spreads

Figure 5: Contribution of the rating overestimation indicator (S&P) to the level of CDS spreads

Figure 6: Contribution of the rating overestimation indicator (Moody’s) to the level of CDS spreads

Figure 6: Contribution of the rating overestimation indicator (Moody’s) to the level of CDS spreads

Figure 7: Contribution of the rating overestimation indicator (Fitch) to the level of CDS spreads

Figure 7: Contribution of the rating overestimation indicator (Fitch) to the level of CDS spreads

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References (33)

  1. Afonso, A. 2003: Understanding the determinants of sovereign debt ratings: Evidence for the two leading agencies. Journal of Economics and Finance, Springer, vol. 27(1), pages 56-74, March.
  2. Afonso, A., Gomes, P. and Rother, P. 2009: Ordered response models for sovereign debt ratings. Applied Economics Letters, Taylor & Francis Journals, vol. 16(8), pages 769-773.
  3. Afonso, A., Furceri, D. and Gomes, P. 2012: Sovereign credit ratings and financial markets linkages: Application to European data. Journal of International Money and Finance, Elsevier, vol. 31(3), pages 606-638.
  4. Afonso, A., Arghyrou, M. G. and Kontonikas, A. 2015: The determinants of sovereign bond yield spreads in the EMU. Working Paper Series 1781, European Central Bank.
  5. Aizenman, J., Binic, M. and Hutchison, M. 2013: Credit Ratings and the Pricing of Sovereign Debt during the Euro Crisis. NBER Working Papers 19125, National Bureau of Economic Research, Inc.
  6. Berger, L., Goldberg, L. and Cheyette, O. 2002: Market Implied Ratings. The Barra Credit Series Research Insights.
  7. Bissoondoyal-Bheenick, E. 2004: An analysis of the determinants of sovereign ratings. Global Finance Journal, Elsevier, vol. 15, pages 251 -280.
  8. Butler, A., W., Fauver, L. 2006: Institutional Enviroment and Sovereign Credit Ratings. Financial Management Association, vol. 35(3), Autumn. 15, pages 53 -79.
  9. Cantor, R. and Packer, F. 1996: Determinants and impact of sovereign credit ratings. Economic Policy Review, Federal Reserve Bank of New York, issue Oct, pages 37-53.
  10. CNB 2012: The impact of changes in countries credit ratings on their borrowing costs. Croatian National Bank Bulletin 182, Box 5, June 2012.
  11. Dell'Ariccia, G., Schnabel, I., and Zettelmeyer, J. 2006: How Do Official Bailouts Affect the Risk of Investing in Emerging Markets? Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(7), pages 1689-1714, October.
  12. De Wit, J. 2006: Exploring the CDS-Bond Basis. Working Paper Research 104, National Bank of Belgium
  13. Cavallo, E., Powell, A., and Rigobon, R. 2008: Do Credit Rating Agencies Add Value? Evidence from the Sovereign Rating Business. Research Department Publications 4601, Inter-American Development Bank, Research Department.
  14. Eichengreen, B and Mody, A 1998: What Explains Changing Spreads on Emerging- Market Debt: Fundamentals or Market Sentiment?. NBER Working Papers 6408, Na- tional Bureau of Economic Research, Inc.
  15. Ferri, G., Liu, L.-G., Stiglitz, J. E. 1999: The Procyclical Role of Rating Agencies: Evidence from the East Asian Crisis. Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 28(3), pages 335-355, November.
  16. Gärtner, M., Griesbach, B. and Jung, F. 2011: PIGS or Lambs? The European Sovereign Debt Crisis and the Role of Rating Agencies. International Advances in Economic Re- search, International Atlantic Economic Society, vol. 17(3), pages 288-299, August.
  17. IMF 2010: Sovereigns, Funding and Systemic Liquidity. Global Financial Stability Re- port, International Monetary Fund, Washington DC, October, 2010.
  18. Jaramillo, L., Tejada, C., M. 2011: Sovereign Credit Ratings and Spreads in Emerging Markets: Does Investment Grade Matter? IMF Working Paper Series 11/44. Interna- tional Monetary Fund
  19. Kaminsky G, Schmukler S. 2002: Emerging Market Instability: Do Sovereign Ratings Affect Country Risk and Stock Returns? The World Bank Economic Review 16(2): 171-195.
  20. Kou, J. and Varotto, S. 2005: Predicting Agency Rating Migrations with Spread Implied Ratings. ICMA Centre Discussion Papers in Finance icma-dp 2005-06, Henley Business School, Reading University.
  21. Kunovac, D. 2012: Sovereign credit ratings of European countries -main determinants and their impact on borrowing costs. Croatian National Bank, mimeo.
  22. Kunovac, D. 2013: The Borrowing Costs of Selected Countries of the European Union - the Role of the Spillover of External Shocks. CNB Working papers, W -38, May 2013.
  23. Lindeman R. H., Merenda, P. F. and Gold, R. Z. 1980: Introduction to Bivariate and Multivariate Analysis. Scott, Foresman, Glenview, Il.
  24. Murphy, K., M. and Topel, R., H, 1985: Estimation and Inference in Two-Step Econo- metric Models. Journal of Business & Economic Statistics, American Statistical Associ- ation, vol. 3(4), pages 370-79, October.
  25. Özmen, E., and Yaşar, Ö. D. 2015: Emerging Markets Sovereign Bond Spreads, Credit Ratings and Global Financial Crisis. ERC Working Papers 1510, ERC -Economic Re- search Center, Middle East Technical University, revised Nov 2015.
  26. Pagan, A. 1984: Econometric Issues in the Analysis of Regressions with Generated Regressors. International Economic Review, vol. 25(1), pages 221-47, February.
  27. Reinhart, C., M. 2002: Default, Currency Crises and Sovereign Credit Ratings. NBER Working Paper 8738
  28. Rowland, P. and Torres, J. L. 2013: Determinants of Spread and Creditworthiness for Emerging Market Sovereign Debt: A Panel Data Study. Borradores de Economia 295, Banco de la Republica de Colombia.
  29. Standard & Poor's 2014: Sovereign Rating Methodology. S & P Rating services, Decem- ber 2014. [online] at https://www.standardandpoors.com/en AP/web/guest/article/- /view/type/HTML/ id/1557417
  30. Table 9: Determinants of sovereign credit ratings for different alternative specifications I II III IV V VI VII Intercept -6.31 10.71
  31. Note: Dependent variables are the credit rating levles. The symbols ***, ** and * denote statistical significance at the 1%, 5% and 10% level, based on the White robust standard error estimator. -100
  32. 100 200 300 2008 2010 2012 2014 SK -40 0 40 80 120 2008 2010 2012 2014 FI -40 0 40 80 120 2008 2010 2012 2014 SE -100 -50 0 50 100 150 2008 2010 2012 2014 7: Contribution of the rating overestimation indicator (Fitch) to the level of CDS spreads -100
  33. 100 200 300 2008 2010 2012 2014 SK -20 0 20 40 60 80 100 2008 2010 2012 2014 FI -40 0 40 80 120 2008 2010 2012 2014 SE -100 -50 0 50 100 150 2008 2010 2012 2014 8: Contribution of the rating overestimation indicator (average rating) to the level of CDS spreads -100