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The objective of this paper is to analyze the concept and determinants of "sovereign risk" and the role of the credit risk rating agencies which serve internationally as the main reference instruments employed by economic agents to assess this risk. The paper also tries to identify macroeconomic variables which could be associated with sovereign ri...
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... fairly widespread use of the risk ratings to manage risk exposure is a sign that investors consider them to be appropriate indicators of the probability of default. Table 2 shows the accumulated Default Rates (DRs) of sovereign borrowers and 7 Canuto et al. (2011) built a model of "shadow sovereign ratings" for unrated countries. 8 With an exception for the DRs of the sovereign ratings Caa, Ca and C for the period of one year which is zero, when it would be expected to be over and above rating class B. The relatively small size of the sample of sovereigns can be one explanation for the emergence of this problem. ...
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Citations
... The SCRs of all three CRAs are sourced from Bloomberg and cross-referenced with Thomson Reuters. These categorical SCRs are first converted into an ordinal scale following the common convention adopted in similar studies (Afonso, Gomes, and Rother 2011, Bissondoyal-Bheenick 2005, Canuto, Santos, and Porto 2012, Hill, Brooks, and Faff 2010, Mellios and Paget-Blanc 2006, Reusens and Croux 2017, Cantor and Packer 1996. For this study, the broad ordinal scale is adopted and is presented in Table 3. ...
This paper examines the variability of emphasis on sovereign credit ratings (SCRs) issued by three leading credit rating agencies: Moody’s, S&P, and Fitch. Using the sample of 55 countries with observations from year 1998 to 2017, this study reveals a clear variation of emphasis on these determinants in assessing the investment-grade versus speculative-grade rated countries. For the speculative-grade rated countries, only determinants representing the Institutional and Susceptibility to External Event factors matter during the post-global financial crisis (GFC) period. Despite the observed variabilities, there is no evidence to support the argument that the CRAs changed their criteria in SCR determination after the global financial crisis. The seven-determinant model continues to have high predictive power for SCRs of all three leading issuers in a rage of 68 to 71% for investment-grade ratings and 81 to 87% for speculative-grade ratings.
... Many studies examine the sovereign credit ratings of developed countries, developing countries, or both. In addition, the related literature generally focus on the linear regression models, ordered response models or combination of different methodologies 5 (Cantor & Packer, 1996b;Haque et al 1996;Hu et al. 2002;Afonso, 2003;Rowland & Torres, 2004;Bissoondoyal-Bheenick, 2005;Mellios & Paget-Blanc, 2006;Mora, 2006;Afonso, 2007;Gültekin Karakaş et al, 2011;Canuto, 2012;Bozic & Magazzino, 2013;Erdem & Varlı, 2014;Chodnicka-Jaworska, 2014;Fourie & Both, 2015;Kabadayı & Çelik, 2015;Öztürk et al., 2016;Kırkıl, 2020;Stawasz-Grabowska, 2020;Proença et al, 2021). Here, some of these studies examining the sovereign credit ratings can be summarized as follows. ...
Sovereign credit ratings are monitored globally because they reflect the forward-looking estimate of the default probability. In addition, they are widely accepted as an indicator of sovereign risk. Many studies try to find the determinants of the credit ratings from economic, financial or political perspectives. While many different econometric methods (like ordinary least square, ordered response model or ordered probit models) are used in these studies, ignoring the interdependency between the countries can cause a major problem. This study aims to contribute to the related literature by applying spatial methods for credit ratings. In contrast to the conventional models, spatial models consider the spillover effects between the countries. For this reason, quarterly data for GIPSI (Greece, Ireland, Portugal, Spain and Italy) countries are used from 2003 to 2021. LM (Lagrange Multiplier) test and the LR (Likelihood Ratio) tests support that the spatial autoregressive model (SAR) is appropriate. According to estimations, the explanatory variables (GDP per capita, international reserves, GDP growth, primary balance, current account balance and government debt) are found to be statistically significant. In addition, the spatial autoregressive coefficient (ρ) is significant, which provides the existence of spatial interaction. Özet Ülke kredi notları, temerrüt olasılığının ileriye dönük tahminini yansıttıkları için küresel olarak izlenmektedir. Ayrıca, ülke riskinin bir belirleyicisi olarak da kabul edilmektedir. Birçok çalışma, kredi notlarının belirleyicilerini ekonomik, finansal veya politik açıdan bulmaya çalışmaktadır. Bu çalışmalarda birçok farklı ekonometrik yöntem (en küçük kareler, sıralı tepki modeli veya sıralı probit modelleri gibi) kullanılırken, ülkeler arasındaki karşılıklı bağımlılığın göz ardı edilmesi büyük bir soruna neden olabilmektedir. Bu çalışma, kredi notları için mekânsal yöntemler uygulayarak ilgili literatüre katkı sağlamayı amaçlamaktadır. Geleneksel modellerin aksine, mekânsal modeller, ülkeler arasındaki yayılma etkilerini dikkate almaktadır. Bu nedenle bu çalışmada GIPSI (Yunanistan, İrlanda, Portekiz, İspanya ve İtalya) ülkeleri için 2003-2021 dönemi için üçer aylık veriler kullanılmıştır. LM(Lagrance Multiplier) ve LR(Likelihood Ratio) testleri mekânsal otoregresif modelin uygun olduğunu desteklemektedir. Yapılan tahminlemelere göre, açıklayıcı değişkenler (kişi başına GSYİH, uluslararası rezervler, GSYİH büyümesi, faiz dışı denge, cari işlemler dengesi ve devlet borcu) istatistiksel olarak anlamlı bulunmuştur. Ayrıca mekansal etkileşimin varlığını kanıtlayan mekansal otoregresif katsayısı (ρ) anlamlı bulunmuştur.
... The company is exposed to actual loss or opportunity cost because of the default (or other failure to perform) by an entity with which the company does business. Canuto et al. (2012) analyzed the concept and determinants of "sovereign risk" and the role of the credit risk rating agencies which serve internationally as the main reference instruments employed by economic agents to assess this risk. The study concluded there is indeed a strong correlation between a country's ratings (be it the average ratings or one of the agency's ratings) and the level of the macroeconomic variables listed above, and between a country's ratings and the variation in levels of government debt and external. ...
The paper investigated the ascendancy of credit risks on water-sanitation infrastructure investments in Kenya. The study adopted a mixed methodology research design where qualitative and quantitative research approaches are used to test the research hypotheses. From a target population of 127, Total Population Sampling (TPS) was adopted whereby the whole population was studied. Both descriptive and inferential analysis methods were employed in the analysis. The study results show that that lack of access to funding infrastructure investments has seen poor cost recovery, weak governance and lack of institutional frameworks that adversely affect economic opportunities. From the empirical evidence and conclusion, an advancement to credit risks will need innovative financing models rolled out through co-financing and blended financing, risk pooling through tailor made infrastructure insurance products, private entity receiving a concession from the public sector to finance, design, construct, own, and operate a facility stated in the concession contract.
... Makoni (2015) shows that there is no investor who will be interested in investing in a country where there is a huge risk of locking his or her investment. Canuto et al. (2012), shows that sovereign risk is under the control of the government, given that the variables which are usually analysed when rating a country are under the control of the government. The authors also show that sovereign risk is different from country risk which is a broad term. ...
─ The current study analyses the relationship between sovereign credit ratings and capital flow represented by Foreign Direct Investment (FDI) and Foreign Portfolio Investment (FPI) in South Africa. South Africa has been receiving negative sovereign credit ratings lately. Time series quarterly data from 1994-2017 is collected and analysed using the Auto-Regressive Distributed Lag (ARDL) and the Error Correction Model (ECM). The ratings analysed are from Standard and Poor's rating agency. The results demonstrate that higher ratings are associated with higher financial flows. In other words, when a country is rated high, investors perceive such a country to be a safe heaven and believe that their investments will be safe. The results show that sovereign ratings influence foreign financial flows. These study outcomes imply that authorities need to consider all factors which are targeted by rating agencies and ensure that they perform as expected.
... SCRs play an important role in the credit rating industry [4]. ey can decrease the asymmetric information between investors and borrowers to increase the borrower's willingness to access funds and lessen the credit risk from the lender`s point of view [5]. e rating class of Fitch Agency, as one of the CRAs, has been shown in Table 1. is process is carried out by credit rating agencies (CRAs) to reduce the information gap between lenders and borrowers. ...
Sovereign debt ratings provided by rating agencies measure the solvency of a country, as gauged by a lender or an investor. It is an indication of the risk involved in investment and should be determined correctly and in a well-timed manner. The current system is lacking transparency of rating criteria and mechanism. The present study reconstructs sovereign debt ratings through logical analysis of data (LAD), which is based on the theory of Boolean functions. It organizes groups of countries according to 20 World Bank-defined variables for the period 2012–2015. The Fitch Rating Agency, one of the three big global rating agencies, is used as a case study. An approximate algorithm was crucial in exploring the rating method, in correcting the agency’s errors, and in determining the estimated rating of otherwise unrated countries. The outcome was a decision tree for each year. Each country was assigned a rating. On average, the algorithm reached almost 98% matched ratings in the training set and was verified by 84% in the test set.
... We will first review the variables that have been reported as statistically significant, and then we will focus on the studies that compare the determinants in different periods. Afonso (2003), Cantor and Packer (1996) and Canuto et al. (2012) conclude that the ratings are explained by a small set of variables, namely, gross domestic product (henceforth, GDP) per capita, GDP growth, inflation, external debt, economic development and default history. Alexe et al. (2003) find that, besides these variables, the ratio between the national credit and GDP, the public debt, political stability and government effectiveness are determinants of sovereign ratings and Altenkirch (2005) shows that the most significant variables are gross domestic savings, the current account balance, foreign reserves and political rights. ...
... As the scales of the ratings are qualitative information, we need to transform them into quantitative scales using linear transformations so that they are mathematically workable. Table A2, in Appendix, shows the assignment of a number for each rating that lies in the range 1 to 17; this is a linear transformation and was also used by Boumparis et al. (2019), Canuto et al. (2012) and Reusens and Croux (2017). ...
Purpose: This paper aims to study the determinants of sovereign debt ratings in thirty-two
European countries provided by the three main rating agencies. It intends to verify the countries
clusters existing for each of the agencies, considering regional bias, and then analyze whether
the determinants had changed before and after the global financial crisis. It is also intended to
understand how the determinants are taken into account for rich and developing countries, using
a sample for a period between 2001 to 2008 and 2009 to 2016.
Design/methodology/approach: To achieve this aim, we perform panel data estimation using an
Ordered Probit approach.
Findings: Under this method, the relevant explanatory variables for developing countries after
crisis are the unemployment rate and belonging to the eurozone. For rich countries, the inflation
rate is pivotal after the crisis period.
Originality/value: This paper is the first to use a clustering methodology to the sovereign debt
rating literature, which can detect a partition of the countries into cohesive clusters. Moreover,
we understand which countries, rating agencies look as strong or weak groups, and in these
groups, which are the sovereign rating determinants.
... While our analysis cannot quantify absolute risk, we have demonstrated that the factors used have a strong tie to credit rating, which drives international credit availability (Canuto, Pereira Dos Santos, & De Sa Porto, 2012;Iyer et al., 2015) Models like ours can, therefore, be used to help establish an index that gauges many of the risks inherent in the enterprise. They provide an integrated, comparative framework to assess relative readiness for nuclear development. ...
Many energy technologies that can provide reliable, low‐carbon electricity generation are confined to nations that have access to robust technical and economic capabilities, either on their own or through geopolitical alliances. Equally important, these nations maintain a degree of institutional capacity that could lower the risks associated with deploying emergent energy technologies such as advanced nuclear or carbon capture and storage. The complexity, expense, and scrutiny that come with building these facilities make them infeasible choices for most nations. This paradigm is slowly changing, as the pressing need for low‐carbon electricity generation and ongoing efforts to develop modular nuclear and carbon capture technologies have opened the door for potentially wider markets, including in nations without substantial institutional capacity. Here, using advanced nuclear technologies as our testbed, we develop new methods to evaluate national readiness for deploying complex energy infrastructure. Specifically, we use Data Envelopment Analysis—a method that eliminates the need for expert judgment—to benchmark performance across nations. We find that approximately 80% of new nuclear deployment occurs in nations that are in the top two quartiles of institutional and economic performance. However, 85% of potential low‐carbon electricity demand growth is in nations that are in the bottom two quartiles of performance. We offer iconic paradigms for deploying nuclear power in each of these clusters of nations if the goal is to mitigate risk. Our research helps redouble efforts by industry, regulators, and international development agencies to focus on areas where readiness is low and risk correspondingly higher.
... SCRs play an important role in the credit-rating industry (Hisarciklilar et al. 2011). They can decrease the asymmetric information between investors and borrowers to increase the borrower's willingness to access funds and lessen the credit risk from the lender`s point of view (Canuto, et al. 2012). This process is carried out by credit rating agencies (CRAs) to reduce the information gap between lenders and borrowers. ...
Sovereign debt ratings provided by rating agencies measure the solvency of a country, as gauged by a lender or an investor. It is an indication of the risk involved in investment, and should be determined correctly and in a well timed manner. The present study reconstructs sovereign debt ratings through logical analysis of data, which is based on the theory of Boolean functions. It organizes groups of countries according to twenty World Bank defined variables for the period 2012 till 2015. The Fitch Rating Agency, one of the three big global rating agencies, is used as a case study. An approximate algorithm was crucial in exploring the rating method, in correcting the agencys errors, and in determining the estimated rating of otherwise non rated countries. The outcome was a decision tree for each year. Each country was assigned a rating. On average, the algorithm reached almost ninety eight percentage matched ratings in the training set, and was verified by eighty four percentage in the test set. This was a considerable achievement.
... The authors used a discretionary approach based on quantitative methods. Canuto, Santos and Porto (2004) focused on examining factors of sovereign risk and the relation between these variables and ratings. Their study results with a list of small variables responsible for differences between risk ratings of countries. ...
The sovereign debt market has gathered a lot of attention post the global financial recession therefore it is very important to study how the countries of the eurozone countries can be shielded from all internal and external risks. This can be achieved by examining the macroeconomic determinants of the sovereign risk. Based on the results of the panel regression, it becomes evident which financial indicators are contributing to the sovereign risk. In terms of the stochastic properties, when homogeneity is assumed among the cross-sectional units, all the variables appeared to be level stationary except for the total government bond yield. However, when heterogeneity is assumed among the countries, variables such total government bond yield, GDS as a percentage of GDP, total credit to private sector, employment as a ratio to total GDP, and bank credit are level none stationary. Consequently, these findings will help identify the variables that can be used to approximate the movement of the government bond yield.
... One set of literature argues that credit ratings have significant impact on bond yields (Sy, 2002;Cantor and Packer, 2007;Kaminsky and Schmukler, 2002;Miricescu, 2015;Agarwal et al., 2016;Baum et al., 2016). In contrast, another set of literature argues that credit ratings have no significant impact on bond yields (Reisen and Von Maltzan, 1999;Canuto et al., 2012;Miricescu, 2015). Other studies (Gonzalez et al., 2004;Greenidge et al., 2010;Escrig-Olmedo et al., 2010) posit that the impact of credit ratings on bond yields depend on many secondary factors. ...
... They conclude that the spreads of government bond yields react before the changes of sovereign credit rating as the financial market anticipated the credit rating changes in advance. Similarly, Canuto et al. (2012) analyse 66 emerging economies using a panel data analysis for the period 1998-2002 against sovereign credit ratings issued by Moody's, S&P and Fitch. They find that sovereign credit rating changes in highly rated countries, which show a low sovereign credit risk, have no significant impact on sovereign bond yields. ...
... They find that sovereign credit rating changes in highly rated countries, which show a low sovereign credit risk, have no significant impact on sovereign bond yields. In support of Reisen and Von Maltzan (1999) and Canuto et al. (2012) and Miricescu (2015) also find that in emerging countries, which often have speculative ratings, sovereign credit ratings do not impact bond yields as the impact is already contained in sovereign bond interest rates, hence the bond interest rate change is small after the rating announcement. ...
This study applies an event study analysis on Standard & Poor’s
(S&P), Fitch and Moody’s sovereign credit ratings for South Africa over the
period 2007 to 2018 to investigate the impact of long-term foreign currency
sovereign credit ratings changes on 30-year sovereign bond yield. Results of
the analysis find a generally significant increase in bond yields before, during
and after credit rating downgrades and negative outlook events. However, there
was no statistically significant impact on bond yields associated with upgrades
and positive outlook events. It is thus concluded that investors in
South Africa’s long-term bonds are more sensitive to negative credit rating
events, which are mainly driven by structural problems in the economy. This
paper recommends monetary and fiscal authorities to address the concerns
raised by rating agencies in review reports leading to negative outlook and
downgrades before they become more apparent to trigger negative rating
actions.
