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Journal of Economics and Finance

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02-03-2018

Structural factors, global shocks and sovereign debt credit ratings

Authors: Carlos Uribe-Teran, Santiago Mosquera

Published in: Journal of Economics and Finance | Issue 1/2019

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Abstract

We analyse the role of fundamentals that reflect the sovereigns’ solvency (structural factors) and global shocks as determinants of sovereign debt credit ratings. By means of random effects ordered probit estimations, we show that structural features have short- and long-run effects that are robust to alternative specifications. The low variation of the structural variables and the world’s economic cycle captured by global shocks are key to obtain a higher proportion of correctly predicted downgrades and fewer mismatches between the estimated rating scale and the data. This also reduces the wrongly predicted upgrades to Investment Grade Status.

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We thank an anonymous referee for bringing to our attention this branch of the literature.

The limitations of the Ordinary Least Squares (OLS) estimator in finding the determinants of credit ratings are well known, and the empirical strategies used in current literature have turned towards other alternatives such as linear panel data models (Afonso et al. 2011; Dimitrakopoulos and Kolossiatis 2016; Erdem and Varli 2014), or pooled non-linear models (Bissoondoyal-Bheenick 2005; Afonso et al. 2011; Ozturk 2014). There is another group of literature that looks at the dynamic structure of the data generated by credit ratings. See for example, Hill et al. (2010) or Dimitrakopoulos and Kolossiatis (2016).

When we talk about structural regressors we do not refer to the result of a structural estimation, as would be the case in the macroeconomic literature. Instead, we use the definition of structural variables adopted in the Credit Rating business environment. We do this to establish a clear mapping between our empirical strategy and the credit ratings’ methodologies that are publicly available.

Few papers have as many observations as we have. To the knowledge of the authors, the papers that study sovereign credit ratings with larger sample sizes are: Hill et al. (2010) that includes 1155 observations, Ozturk (2014) with 1008 observations, Dimitrakopoulos and Kolossiatis (2016) with 744 observations and Jaramillo et al. (2010) that considers 691 observations.

The Development Status corresponds to the IMF classification.

We also use linear random effects estimations and perform the Sargan-Hansen test, which is a generalisation of the classic Hausman specification test that allows clustered standard errors for the estimated coefficients, to check that the Random Effects estimation is preferred. Additionally, we also estimate non-linear models using random effects probit specifications to show that our results are independent of the assumption we make about the distribution of the latent variable. Both of these tables are available upon request.

We are aware that the calibration of the years since last default index can potentially have significant effects over our estimations. To tackle this worry, we re-estimate the model for 501 possible calibrations of the index and focus on the β associated to the index. The point estimate varies between –4.31 and –0.75, and the results of a Wald test shows that these variation is not statistically significant.

We also computed kernel estimations of the density function for the distribution of probabilities that we observe in the first three most likely outcomes. These results are available upon request.

There are some papers that concentrate the efforts in analysing only this transition. See for example Jaramillo et al. (2010) or Jaramillo and Tejada (2011).

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Title: Structural factors, global shocks and sovereign debt credit ratings
Authors: Carlos Uribe-Teran
Santiago Mosquera
Publication date: 02-03-2018
Publisher: Springer US
Published in: Journal of Economics and Finance / Issue 1/2019
Print ISSN: 1055-0925
Electronic ISSN: 1938-9744
DOI: https://doi.org/10.1007/s12197-018-9435-0

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