Understanding the sovereign credit ratings of emerging markets

Erdem, Orhan; Varlı, Yusuf

doi:10.1016/j.ememar.2014.05.004

Cited by 68 publications

(76 citation statements)

References 11 publications

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“…Last but not the least; the governance indicator enters into the model with positive and significant sign. Better governance is associated with countries' willingness to repay and expected to have positive sign (Erdem and Varli 2014;Moody's 2010). Table 3 presents a comparison of the performance of different AI methods used in our analyses.…”

Section: Resultsmentioning

confidence: 96%

“…It is also well suited for developing new machine learning schemes. As a first step we follow the recent literature and estimate an ordered response model to identify the determinants of sovereign credit ratings (Erdem and Varli 2014;Gültekin-Karakaş et al 2011 etc.). Table 2 reports the estimation results of the standard ordered probit model using all variables with clustered robust standard errors.…”

Section: Resultsmentioning

confidence: 99%

“…Among the earlier studies, Cosset and Roy (1991) and Cantor and Packer (1996) investigate the determinants of sovereign credit ratings by employing ordinary least squares (OLS). Frank and Cline (1971), Grinols (1976), Sergen (1977), and Saini and Bates (1978) employ discriminant analysis, whereas, Alexe et al (2003), Afonso (2003), Bissoondoya-Bheenick (2005), Bennell et al (2006), Afonso et al (2009), Gültekin-Karakaş et al (2011, and Erdem and Varli (2014) are the new generation studies that use ordered response models. Even though the results and findings of these studies vary, the predictive power of statistical models is very similar and relatively low.…”

Section: Sovereign Credit Ratingsmentioning

confidence: 96%

See 2 more Smart Citations

Reducing Overreliance on Sovereign Credit Ratings: Which Model Serves Better?

Öztürk

Namlı

Erdal³

2015

Comput Econ

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Sovereign credit ratings have been a controversial issue since the outbreak of the 2008 financial crisis. Among the debates the inaccuracies stay at the centre. By employing classification and regression trees, multilayer perceptron, support vector machines (SVM), Bayes net, and naïve Bayes; we compare the ability of various learning techniques with the conventional statistical method in predicting sovereign credit ratings. Experimental results suggest that all the techniques excluding SVM have over 90 % accurate prediction. According to within one and two notch accurate prediction measure, the prediction performance of SVM also increases above 90 %. These findings indicate a clear outperformance of AI methods over the conventional statistical method. The results have many implications for the practitioners in credit scoring industry. Amidst the regulatory measures that encourage individual credit scoring for international financial institutions, these findings suggest that up-to-date AI methods serve quite reliable technical tools to predict sovereign credit ratings.

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

Section: Sovereign Credit Ratingsmentioning

confidence: 96%

See 1 more Smart Citation

Reducing Overreliance on Sovereign Credit Ratings: Which Model Serves Better?

Öztürk

Namlı

Erdal³

2015

Comput Econ

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“…The relationship between ratings and their determinants has been widely examined following the innovative studies of Cantor and Packer (1995) and Cantor and Packer (1996). Among many others, Afonso and Gomes (2011), Erdem and Varli (2014), Gültekin-Karaka¸s et al (2011) studied the determinants of ratings and found that the impact of fiscal balance on rating assessments is significant. All these authors conclude that CRAs attach considerable importance to fiscal balance meaning that the deterioration in fiscal balance is likely to trigger rating downgrades.…”

Section: Political Opportunism and Rating Changesmentioning

confidence: 99%

The role of sovereign credit ratings in fiscal discipline

Duygun

Öztürk

Shaban

2016

Emerging Markets Review

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This paper investigates several aspects of the relationship between sovereign credit ratings and fiscal discipline. The analysis of over one thousand country-year observations for 93 countries during the 1999-2010 period reveals that a country's debt level is likely to increase with higher ratings, confirming the existence of pro-cyclicality and path dependence of ratings. In addition, the study finds no evidence to support the theory of Political Business Cycle, which implies that political ambitions may lead to fiscal worsening following a rating upgrade. The study findings further demonstrate that institutional quality is an important factor in the ratings-fiscal discipline nexus.

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“…Previous work on sovereign ratings had identified per capita income levels and the level of economic development as among the most important ratings determinants, which arguably reflected the positive OECD-country bias built into the Basel I framework for risk weighting of the regulatory bank capital. A study based on recent data (following the Basel II regime that had abandoned the OECD bias) is by Erdem and Varli [2014]. They find that the most relevant factors for sovereign ratings are budget balance/GDP, GDP per capita growth, governance indicators and reserves/GDP for Standard and Poor's (S&P).…”

Section: The Financing Gap For Multilateral Soft Loansmentioning

confidence: 99%