Subjectivity in sovereign credit ratings

Moor, Lieven De; Luitel, Prabesh; Sercu, Piet; Vanpée, Rosanne

doi:10.1016/j.jbankfin.2017.12.014

Cited by 49 publications

(35 citation statements)

References 45 publications

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“…The findings agree with the analysis done by Mellios and Paget-Blanc (2006) and Chee et al (2015) and concluded that exchange rates are one of the variables used by rating agencies to measure a country's creditworthiness. The results are consistent with findings by Bissoondoyal and Bheenick (2005); Mellios and Paget-Blanc (2006); Iyengar (2010); Afonso et al (2011); Arefjevs and Brasliņs (2013);Sánchez-Monedero et al (2014); Kabadayi and Celik (2015); Ivanovic et al (2015); Chee et al (2015);De Moor et al (2018); and Cantor and Packer (1996) that GDP growth is a crucial variable in determining sovereign credit ratings. For CPIH, there seems to be less difference between the means for less stable and those for more stable.…”

Section: Naïve Bayes Model For Moody'ssupporting

confidence: 90%

Determinants of Sovereign Credit Ratings: An Application of the Naïve Bayes Classifier

Takawira¹,

Mwamba²

2020

EJEF

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This is an analysis of South Africa’s (SA) sovereign credit rating (SCR) using Naïve Bayes, a Machine learning (ML) technique. Quarterly data from 1999 to 2018 of macroeconomic variables and categorical SCRs were analyzed and classified to predict and compare variables used in assigning SCRs. A sovereign credit rating (SCR) is a measurement of a sovereign government’s ability to meet its financial debt obligations. The differences by Credit Rating Agencies (CRA) on rating grades on similar firms and sovereigns have raised questions on which elements truly determine credit ratings. Sovereign ratings were split into two (2) categories that is less stable and more stable. Through data cross-validation for supervised learning, the study compared variables used in assessing sovereign rating by the major rating agencies namely Fitch, Moody’s and Standard and Poor’s. Cross-validation splits the dataset into train set and test set. The research applied cross-validation to reduce the effects of overfitting on the Naïve Bayes Classification model. Naïve Bayes Classification is a Machine-learning algorithm that utilizes the Bayes theorem in classification of objects by following a probabilistic approach. All variables in the data were split in the ratio of 80:20 for the train set and test set respectively. Naïve Bayes managed to classify the given variables using the two SCR categories that is more stable and less stable. Variables classified under more stable indicates that ratings are high or favorable and those for less stable show unfavorable or low ratings. The findings show that CRAs use different macroeconomic variables to assess and assign sovereign ratings. Household debt to disposable income, exchange rates and inflation were the most important variables for estimating and classifying ratings.

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Section: Naïve Bayes Model For Moody'ssupporting

confidence: 90%

Determinants of Sovereign Credit Ratings: An Application of the Naïve Bayes Classifier

Takawira¹,

Mwamba²

2020

EJEF

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“…Traditional approaches to modelling credit ratings rely on parametric estimations (such as ordered response models or OLS; see for example, Cantor & Packer 1996;Afonso et al, 2009;Baghai et al, 2016). However, due to the non-linearity of ratings and their distributional properties, and to address multicollinearity, researchers have considered non-parametric approaches to model sovereign ratings (Bennell et al, 2006;De Moor et al, 2018;Fioramanti, 2008;Markellos et al, 2016;Ozturk et al, 2016). The central benefits associated with these approaches are much better handling of non-linear outcomes in the data (Markellos et al, 2016) and the potential for superior fit (De Moor, 2018).…”

Section: Reconstructing Sovereign Credit Ratingsmentioning

confidence: 99%

“…However, due to the non-linearity of ratings and their distributional properties, and to address multicollinearity, researchers have considered non-parametric approaches to model sovereign ratings (Bennell et al, 2006;De Moor et al, 2018;Fioramanti, 2008;Markellos et al, 2016;Ozturk et al, 2016). The central benefits associated with these approaches are much better handling of non-linear outcomes in the data (Markellos et al, 2016) and the potential for superior fit (De Moor, 2018). Because sovereign ratings may be subject to thresholds in country-level predictors, such as GDP per capita (S&P 2017), methods capable of handling non-linearities are essential.…”

Section: Reconstructing Sovereign Credit Ratingsmentioning

confidence: 99%

Rising temperatures, falling ratings: The effect of climate change on sovereign creditworthiness

et al. 2021

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Enthusiasm for 'greening the financial system' is welcome, but a fundamental challenge remains: financial decision makers lack the necessary information. It is not enough to know that climate change is bad. Markets need credible, digestible information on how climate change translates into material risks. To bridge the gap between climate science and real-world financial indicators, we simulate the effect of climate change on sovereign credit ratings for 108 countries, creating the world's first climate-adjusted sovereign credit rating. Under various warming scenarios, we find evidence of climate-induced sovereign downgrades as early as 2030, increasing in intensity and across more countries over the century. We find strong evidence that stringent climate policy consistent with limiting warming to below 2°C, honouring the Paris Climate Agreement, and following RCP 2.6 could nearly eliminate the effect of climate change on ratings. In contrast, under higher emissions scenarios (i.e., RCP 8.5), 63 sovereigns experience climateinduced downgrades by 2030, with an average reduction of 1.02 notches, rising to 80 sovereigns facing an average downgrade of 2.48 notches by 2100. We calculate the effect of climate-induced sovereign downgrades on the cost of corporate and sovereign debt. Across the sample, climate change could increase the annual interest payments on sovereign debt by US$ 22-33 billion under RCP 2.

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“…Some authors state that a sovereign credit rating is a function of basic data and about information that is difficult to measure which should reflect the creditworthiness and the probability of a country to default. They propose an alternative characterisation for the subjective component of a sovereign credit rating -the part related to the rate's lobbying effort or its familiarity from a United States point of view -and apply it to S&P and Moody's and Fitch ratings, using both traditional ordered logit panel models and machine learning techniques (De Moor et al 2018). In the paper of Daud and Podivinsky (2011), it is found that the positive effect of the accumulation of reserves aiming to improve sovereign ratings is crowded out by the negative effect of the accumulation of external debt, resulting in a net negative effect.…”

Section: Literature Reviewmentioning

confidence: 99%

Does the International Tourism Industry Relax Sovereign Credit Ratings: The Case of Countries Most Reliant on Tourism

Šergo

Gržinić

2018

South East European Journal of Economics and Business

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Sustainable tourism plays a dominant role in the economic well-being of some of the world’s countries, especially small ones. Tourism earnings account for a significant proportion of their GDP, and they have an overwhelming reliance on tourism as a source of service exports. The general trends in tourism earnings and volatilities in country risk ratings often go hand in hand, especially for small touristic countries in that region. The research presented in this paper provides a comparative assessment of the international country risk ratings and highlights the importance of their tourism earnings and tourism export. This study employs the ordered response and Poisson count panel data model for a sample of twenty-two countries most reliant on tourism, including Mediterranean countries. The aim of this study is to investigate whether the tourism determinants of sovereign credit ratings for those countries vary between different rating agencies (Standard & Poor’s, Moody’s and Fitch’s). The key finding is that an increase in tourism earnings as a proportion of GDP and as the main export share in the total country export impairs the sovereign risk rating and turns out to be robust across the different methodologies.

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Subjectivity in sovereign credit ratings

Cited by 49 publications

References 45 publications

Determinants of Sovereign Credit Ratings: An Application of the Naïve Bayes Classifier

Determinants of Sovereign Credit Ratings: An Application of the Naïve Bayes Classifier

Rising temperatures, falling ratings: The effect of climate change on sovereign creditworthiness

Does the International Tourism Industry Relax Sovereign Credit Ratings: The Case of Countries Most Reliant on Tourism

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