Split sovereign ratings and rating migrations in emerging economies

Emerging Markets Review 11 (2010) 79–97

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Emerging Markets Review j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e m r

Split sovereign ratings and rating migrations in emerging economies Rasha Al-Sakka a,⁎, Owain ap Gwilym b a b

School of Management and Business, Aberystwyth University, UK Bangor Business School, Bangor University, UK

a r t i c l e

i n f o

Article history: Received 30 July 2009 Received in revised form 25 November 2009 Accepted 26 November 2009 Available online 3 December 2009 JEL classification: G15 G24 Keywords: Split ratings Emerging sovereign ratings Rating migrations Marginal effects

a b s t r a c t This paper presents evidence on sovereign rating heterogeneity in emerging economies. Split rated sovereigns are prone to be upgraded (downgraded) by the agency from whom a lower (higher) rating exists. The harsher the split ratings between two agencies, the greater the effect on probabilities of future rating changes. Split ratings among Moody's, S&P and Fitch are influential on their rating migrations. The rating dynamics of Capital Intelligence, Japan Credit Rating Agency and Japan Rating & Investment Information are affected by their rating disagreements with the larger agencies. Only Moody's upgrade decisions are influenced by rating differentials with the smaller agencies. © 2009 Elsevier B.V. All rights reserved.

1. Introduction During 2007–2009, international credit rating agencies were partly blamed for the credit crunch and the subsequent effects on the global economy. Rating agencies were criticised by politicians and regulators for failing to properly assess the risks in the securities backed by sub-prime mortgages. The high-level group chaired for the European Commission by Jacques de Larosiere argued that when rating agencies evaluated the credit risk associated with collateralised debt obligations (CDOs), there were “flaws in their rating methodology” (Smith, 2009). Rating agencies have also been criticised recently on the basis of inherent conflicts of interest within their business model, lack of transparency and poor communication (Duff and Einig, 2009). Formal regulation of the credit rating industry was introduced by the European Union in April 2009.1 Despite the recent criticism, rating agencies have long played an essential role in ⁎ Corresponding author. School of Management and Business, Aberystwyth University, SY23 3DD, UK. Tel.: +44 1970 62 2260. E-mail addresses: [email protected] (R. Al-Sakka), [email protected] (O. ap Gwilym). 1 The new rules will require credit rating agencies operating in Europe to register with authorities, who could regulate them more closely. The rules also require rating agencies to publish their methodologies in order to enhance transparency. 1566-0141/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.ememar.2009.11.005

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global financial markets through the production of credit information and its distribution to market participants (see Section 2). However, rating agencies do not always agree on the credit risk of a particular security/issuer, leading to split ratings. The role of rating agencies is more challenging and problematic in emerging economies than in developed countries. Emerging countries often demonstrate a significant lack of economic and political stability, lack of market regulation and transparency, and a high degree of volatility and uncertainty. Therefore, disagreements across rating agencies on emerging sovereign ratings are very common. For example, 63.5% of the monthly ratings for emerging sovereign issuers rated by both Moody's and S&P from January 2000 to May 2006 differ by up to six rating notches (Al-Sakka and ap Gwilym, 2009). Despite such evidence, the sovereign ratings literature has been silent on the effect this may have on future rating migrations. Spilt ratings convey valuable information to the market and have an impact on bond yields and prices (e.g., Liu and Moore, 1987; Cantor et al., 1997; Jewell and Livingston, 1998). The aim of this paper is to investigate the relationship between split long-term (LT) foreign-currency (FC) ratings and future rating migrations for emerging market sovereigns. Jewell and Livingston (1998), Morgan (2002) and Livingston et al. (2007) suggest that split ratings convey additional valuable information, and therefore one can expect that split ratings may also have an effect on future rating migration probabilities. Issuers with larger information asymmetry problems are expected to be subject to more news and, thus, it is likely that rating agencies will adjust their ratings to mirror any newly available information. Consequently, issuers with split ratings are more likely to experience rating migrations. Livingston et al. (2008) is the only prior study to relate split ratings with future rating changes. Livingston et al. (2008) use a sample of corporate bond issuers rated by S&P and Moody's over the period 1983 to 2001. Their results reveal that corporate bonds with initial split ratings are prone to experience future rating migrations. Prior studies on split ratings have been focused on corporate ratings and mainly investigate the causes of split corporate ratings. Ederington (1986) argues that split ratings are triggered by random errors across rating agencies, while Moon and Stotsky (1993), Cantor and Packer (1994), Pottier and Sommer (1999) and Dandapani and Lawrence (2007) attribute corporate split ratings to different rating methodologies and differing weighting factors used by agencies in judging the creditworthiness of corporate borrowers. In addition, Morgan (2002), Livingston et al. (2007, 2008) and Hyytinen and Pajarinen (2008) show that differences in corporate ratings are a symptom of firm opaqueness. Corporate issuers with more opaque assets arising from poor information quality have a higher frequency of split ratings. Split ratings may also be due to “home bias”. Beattie and Searle (1992) and Shin and Moore (2003) find that agencies appear to judge issuers from their own home country more leniently. Duff and Einig (2009) highlight that issuers are more likely to hire an agency if it has a strong reputation in a specific market. In the only prior study to address split sovereign ratings, Cantor and Packer (1995) argue that the split sovereign ratings between Moody's and S&P are due to the agencies' lack of experience in rating sovereign credits. Recently, the number of sovereign issuers has significantly grown largely based on expansion in emerging country ratings. For example, the number of sovereign issuers rated by Moody's has increased from 48 countries in 1995 (of which only 16 were emerging countries) to 107 in December 2007 (of which 71 were emerging countries) (see Cantor et al., 2008). S&P rated 118 sovereigns in April 2008 (of which 78 were emerging countries) compared to 7 countries in 1975 (all developed countries) (see Beers, 2008). The main focus of related studies on sovereigns has been identification of the determinants of LT FC sovereign ratings (e.g., Cantor and Packer, 1996; Hu et al., 2002; Bennell et al., 2006). Sovereign rating migrations have received little attention. Rating migrations, which capture changes in credit quality over a given time period, are key inputs to many applications in modern risk management, such as portfolio risk assessments, bond pricing models, pricing of credit derivatives and modelling credit risk premia (Frydman and Schuermann, 2007). Fuertes and Kalotychou (2007) examine rating momentum and duration effects on Moody's sovereign rating changes, and find negative duration dependence and a significant rating momentum effect for downgrades. Al-Sakka and ap Gwilym (2009) also show that rating momentum, Watchlist status, rating duration, existing rating and issuer's domicile region are useful determinants in modelling the migration process of sovereign ratings in emerging economies. Al-Sakka and ap Gwilym (2009) point out that differences across agencies on their emerging sovereign ratings are attributable to the use of different rating processes, and variation in the weighting of different factors that enter rating upgrade/downgrade decisions.

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Using LT FC ratings from six international rating agencies, this paper analyses the relationship between split rated emerging sovereign issuers and future ratings migrations. This paper makes a significant contribution by identifying “split rating” as a valuable factor affecting the probabilities of emerging sovereign rating changes. In comparison with Livingston et al. (2008), the current paper focuses on LT FC ratings of emerging market sovereign issuers in international bond markets rather than corporate ratings, uses ratings from six international rating agencies rather than two, considers rating changes by notches (one-notch, more-than-one-notch), and covers the most recent data. The findings show that split rated emerging sovereign issuers are more likely to be upgraded by the agency from whom a lower rating exists, and more likely to be downgraded by the agency from whom a higher rating exists, within the subsequent year. The marginal effects analysis also reveals that the harsher the split between two agencies, the greater the effect on the probability of rating change. These findings support the opaqueness hypothesis of split ratings since opaque issuers are more likely to receive split ratings, and thus they will have more rating migrations in the future. Split ratings among the larger three agencies (Moody's, S&P and Fitch) are influential on each others' future rating migrations. S&P/Fitch rating behaviour tends not to be affected by their rating differentials with smaller rating agencies (CI, R&I or JCR). In contrast, Moody's upgrade decisions are influenced to some extent by rating differentials between Moody's and the smaller agencies. The rating dynamics of the smaller agencies are always affected by their rating disagreements with the larger agencies. The remainder of the paper is organised as follows. The next section provides contextual information on credit ratings. Section 3 explains the sovereign ratings data and presents the ordered probit model. The empirical results are discussed in Section 4, and Section 5 concludes the paper. 2. Rating agencies, regulation and rating practice The unique task of the rating agencies is to distil a multitude of credit information to a single letter on a rating scale (Duff and Einig, 2009). External credit ratings are applied to a wide range of debt issuers, including financial institutions and banks, corporates, sovereigns, supranational and municipal borrowers, and also to several types of issues, such as commercial paper, preferred stock, bonds, bank loans, and structured financing. An issue credit rating is the opinion of a rating agency about the creditworthiness of an obligor regarding a particular (class of) financial obligation or programme. An issuer credit rating is the opinion of a rating agency about the overall ability and willingness of an obligor to repay its financial obligations on time. Both long-term and short-term debt issue/issuer ratings are assigned by rating agencies. Both sovereigns and non-sovereigns may receive local and/or foreign-currency ratings depending on the currency of debt issuance. The focus of the paper is only on LT FC ratings of sovereign issuers (supranational, sub-sovereign, and municipal issuers are excluded). The importance of credit rating agencies is rapidly growing due to the increasing numbers of issuers and debt products and the effects of globalization. Access to capital markets is the main reason for issuers to seek ratings from the agencies. Investors' appetite for a debt security without an (appropriate) rating will be limited. For example, rule 2a–7 of the U.S. Investment Company Act of 1940 restricts the investments of money market funds to triple-A assets rated by NRSROs.2 U.S. pension funds and municipalities are also restricted to invest in investment-grade assets, with ratings assigned by NRSROs. Langohr (2006) states that rating agencies offer a gate-keeping function to access international capital markets. Therefore, about 80% of all capital flows are affected by credit ratings (von Schweinitz, 2007). Additionally, credit ratings are required to reduce the information gap between investors and borrowers, decreasing the risk premium of a debt issue, and thus reducing the overall cost of capital (Duff and Einig, 2009). Investors also consider ratings for capital calculations. The U.S. Securities Exchange Act of 1934 requires broker-dealers to maintain a minimum amount of net capital at all times. Rule 15c3-1 allows broker-dealers to take a lower capital charge, termed a ‘haircut’, for certain types of securities, such as commercial paper, nonconvertible debt securities, and nonconvertible preferred stock, that are rated investment grade by at least two 2 Nationally Recognized Statistical Rating Organizations approved by the U.S. Securities and Exchange Commission (SEC). As of November 2009, ten agencies qualify as NRSROs: A.M.Best Company, DBRS, Egan-Jones Rating Company, Fitch Ratings, Japan Credit Rating Agency, Japan Rating and Investment Information, LACE Financial Corp, Moody's Investors Service, Realpoint LLC, and Standard & Poor's Rating Service.

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NRSROs. Another source of demand for ratings comes from the Basel II Accord that allows banks and financial institutions to use external credit ratings to assess credit risks and to determine capital adequacy requirements. For example, the risk weights for claims on rated sovereigns are: AAA-AA: 0%; A: 20%; BBB: 50%; BB-B: 100%; CCC-C: 150%; and D: deducted from capital (von Schweinitz, 2007). The U.S. Security Exchange Act of 1934 empowers the SEC over all aspects of the securities industry in order to protect investors. The Act requires issuers to register with the SEC and to file periodic reports. Domestic publicly traded companies (with more than $10 million in total assets whose securities are held by more than 500 owners worldwide) are required to regularly file company information with the SEC, e.g. the annual 10-K filing within 60 to 90 days, and the quarterly 10-Q filing within 45 days. On the other hand, foreign private issuers (other than foreign governments) are required to file with the SEC an annual report on Form 20-F within 4 months after the end of the previous fiscal year, and they are also required to file form 6-K to reflect all material information that arises between annual financial reports. Foreign government issuers are required to file with the SEC an annual report on Form 18-F within 9 months after the end of each fiscal year. The form includes a statement giving the title, date of issue, date of maturity, interest rate and amount outstanding, together with the currency(ies) in which payable of each issue of funded debt as of the close of the last fiscal year of the registrant. However, foreign private placements are not required to register their securities with SEC, and do not have to file reports with the SEC (if the issuance of the securities conforms to an exemption according to Rules 504, 505, or 506 in Regulation D or sections 4(6) of the Securities Act of 1933). They must only file what is known as a “Form D” after they first issue the securities. Form D is a brief notice that includes the names and addresses of the company's owners and stock promoters, but contains little other information about the company. The U.S. SEC requires domestic issuers to include financial statements prepared in accordance with U.S. GAAP in registration statements and in annual and quarterly reports. Unless the financial statements are already prepared in accordance with U.S. GAAP or International Financial Reporting Standards (IFRS) as issued by the International Accounting Standards Board (IASB), foreign private issuers are required (under Regulation G) to provide reconciliation to U.S. GAAP (that identifies and quantifies the material differences between the GAAP and the used set of accounting principles) (see SEC, 2007). This paper focuses on sovereign ratings for several reasons. Investors are increasingly focused on international diversification, and hence an understanding of sovereign credit risk is very important (Cantor and Packer, 1996). In addition, sovereign rating news affects both own-country and international stock and bond markets, and also impacts the cost of capital (Sy, 2002, Brooks et al., 2004). Sovereign ratings improve the capability of countries to access international markets and to attract foreign investments, especially in emerging countries (Bissoondoyal-Bheenick, 2004; Andritzky et al., 2007; Kim and Wu, 2008). Sovereign ratings also represent a ceiling for the ratings assigned to financial institutions, corporates and provincial governments within the country. Moody's, S&P and Fitch have recently eliminated their sovereign ceiling rule. Though the ceiling effect is no longer absolute, there remains a “sovereign ceiling lite” (Borensztein et al., 2007). There are some exceptions where the LT FC ratings of non-sovereign issuers can be higher than sovereign LT FC ratings, if the non-sovereign issuer has stronger credit features than the sovereign. An example would be a company that has a strong credit profile, substantial foreign exchange earnings, an exemption from constraints of transfer risk, or is owned by a financially strong foreign entity (Kastholm and Scher, 2005). For instance, in 2005, Fitch assigned a ‘BB’ grade to the LT FC debt of Companhia Vale do Rio Doce (CVRD), while Brazil's LT FC was ‘BB−’.3 In November 2005, S&P assigned a ‘BBB’ LT FC rating for Tata Steel, while India's LT FC rating was ‘BB+’.4 In September 2001, Moody's assigned a ‘Ba1’ LT FC bond rating to Petrobras, (the largest oil, petrochemical and energy conglomerate in Brazil) which was three notches above Brazil's sovereign rating. This reflected Moody's positive perspective of the company's financial and operational strengths and the likelihood that Petrobras would be able to access foreign currency during a currency crisis (Petrobras, 2001). As of January 2009, the City of Buenos Aires was rated

3 CVRD is a mining multinational corporation and is the world's largest exporter of iron. It generates substantial amounts of hard currency, maintains considerable cash balances abroad, and has been excused from past government restructurings and transfer risk constraints. 4 Tata is the world's sixth largest steel company. S&P's action reflected the corporate's strong balance sheet and financial position, and its competitive cost position.

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Fig. 1. Distribution of emerging sovereign annual ratings by rating score, All agencies, January 2000–January 2008. The credit ratings scale is transformed into a 20-point numerical scale (Aaa/AAA = 1, Aa1/AA+ = 2…Caa3/CCC− = 19, Ca/CC, C/SD − D = 20).

‘B1’ by Moody's and the Province of Mendoza was ‘B2’, both ratings being higher than Moody's LT FC rating of Argentina at ‘B3’ (Liu and Tan, 2009). 3. Data sample and ordered probit model The sample includes annual observations (as at 31st of January) of long-term foreign-currency (LT FC) sovereign ratings assigned by at least two of six international credit rating agencies to 49 emerging sovereigns during the period from 2000 to 2008.5 The rating agencies are: Moody's Investors Service (Moody's), Standard and Poor's (S&P), Fitch Ratings (Fitch), Capital Intelligence (CI), Japan Rating and Investment Information (R&I) and Japan Credit Rating Agency (JCR).6 Data is obtained from the Financial Times (FT) Credit Ratings International database. To identify “emerging” countries, the World Bank's country classification, according to countries' GNI per capita, is adopted. All low-income or middle income countries are defined as “emerging”. The ratings scale is transformed into a 20-point numerical scale (Aaa/ AAA = 1, Aa1/AA+ = 2 … Caa3/CCC− = 19, Ca/CC to C/SD − D = 20) (see e.g., Bennell et al., 2006; Kim and Wu, 2008). Fig. 1 presents the distribution of the sovereign ratings by rating score for the data sample. None of the sovereign issuers are rated between Aaa/AAA and Aa3/AA−, while very few issuers are rated in the A1/A+ categories, reflecting the focus of the data sample on emerging markets. Also, 5.9% of observations are at the Caa1/CCC+ category and below due to the small number of defaulted sovereigns (issuers are usually rated Caa1/CCC+ or below just before/after they defaulted or at default). With the exception of the small proportion of ratings in categories at the top and the bottom of the rating scale, there is a reasonable spread of observations across the other rating scores. Table 1 presents the distribution of annual rating changes for agency pairs (i.e. the sovereign rating of an emerging country at 31st January of each year is compared with its rating at the 31st January of the previous year).7 In all sub-samples, the percentages of upgrades are far higher than downgrades. The upgrade trend of emerging sovereign ratings derives from the strong economy during the sample period (see below). Additionally, since annual data is used some downgrades that occurred within a year but were later upgraded are not captured. A variety of causes have fuelled the economic growth in many emerging countries during the sample period. Russia gained from higher oil and natural gas prices, leading to enhanced foreign exchange reserves coverage and a strengthening government balance sheet. Eastern Europe benefited from the strong world economy and employment opportunities via the European Union, given their lower labour costs and more 5 The sample includes only sovereign ratings of emerging countries since rating behaviour for developed countries is influenced by different factors than those affecting emerging countries (Monfort and Mulder, 2000; Mulder and Perrelli, 2001). In addition, it is well known that there is much less disagreement on developed countries' sovereign ratings. 6 CI is the only agency that is not approved by the U.S. SEC as a NRSRO. 7 For the Japanese agencies, there are either no or very few observations at more-than-one-notch upgrade (downgrade) categories, which were therefore later merged with the 1-notch upgrade (downgrade) categories (see Table 1).

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Table 1 Annual emerging sovereign rating changes, January 2000–January 2008. UP

DW

Changes

1n-up

N1n-up

1n-dw

N1n-dw

56 89 14.0 22.2

23 14 5.7 3.5

4 16 1.0 4.0

7 10 1.7 2.5

78 80 22.1 22.7

10 13 2.8 3.7

7 13 2.0 3.7

6 7 1.7 2.0

49 78 13.7 21.9

23 12 6.5 3.4

6 7 1.7 2.0

5 7 1.4 1.9

26 28 15.4 16.6

9 3 5.3 1.7

4 2 2.4 1.2

2 2 1.2 1.2

40 28 24.1 16.9

2 3 1.2 1.8

4 2 2.4 1.2

2 2 1.2 1.2

37 28 23.3 17.6

4 3 2.5 1.9

3 4 1.9 2.5

2 0 1.2 0.0

UP

DW

Changes

CI and Moody's (total no. of obs. 221) Moody's no 46 3 49 CI no 46 4 50 Moody's% of obs. 20.8 1.4 22.2 CI% of obs. 20.8 1.8 22.6 CI and S&P (total no. of obs. 214) S&P no 52 7 59 CI no 45 5 50 S&P% of obs. 24.3 3.3 27.6 CI% of obs. 21.0 2.3 23.3 CI and Fitch (total no. of obs. 186) Fitch no 46 6 52 CI no 38 4 42 Fitch% of obs. 24.8 3.2 28.0 CI% of obs. 20.4 2.2 22.6 JCR and Moody's (total no. of obs. 112) Moody's no 27 3 30 JCR no 26 3 29 Moody's% of obs. 24.1 2.7 26.8 JCR% of obs. 23.2 2.7 25.9 JCR and S&P (total no. of obs. 112) S&P no 35 4 39 JCR no 26 3 29 S&P% of obs. 31.3 3.6 34.9 JCR% of obs. 23.2 2.7 25.9 JCR and Fitch (total no. of obs. 112) Fitch no 33 4 37 JCR no 26 3 29 Fitch% of obs. 29.5 3.6 33.1 JCR% of obs. 23.2 2.7 25.9

1n-up

N 1n-up

1n-dw

N 1n-dw

32 37 14.5 16.7

14 9 6.3 4.1

3 4 1.4 1.8

0 0 0.0 0.0

47 36 22.0 16.8

5 9 2.3 4.2

5 5 2.4 2.3

2 0 0.9 0

39 30 21.0 16.1

7 8 3.8 4.3

6 4 3.2 2.2

0 0 0 0

19 26 17.0 23.2

8 0 7.1 0

3 3 2.7 2.7

0 0 0 0

30 26 26.8 23.2

5 0 4.5 0

4 3 3.6 2.7

0 0 0 0

28 26 25.0 23.2

5 0 4.5 0.0

4 3 3.6 2.7

0 0 0 0

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S&P and Moody's (total no. of obs. 401) Moody's no 79 11 90 S&P no 103 26 129 Moody's% of obs. 19.7 2.7 22.4 S&P% of obs. 25.7 6.5 32.2 S&P and Fitch (total no. of obs. 353) Fitch no 88 13 101 S&P no 93 20 113 Fitch% of obs. 24.9 3.7 28.6 S&P% of obs. 26.4 5.7 32.1 Fitch and Moody's (total no. of obs. 356) Moody's no 72 11 83 Fitch no 90 14 104 Moody's% of obs. 20.2 3.1 23.3 Fitch% of obs. 25.3 3.9 29.2 R&I and Moody's (total no. of obs. 169) Moody's no 35 6 41 R&I no 31 4 35 Moody's% of obs. 20.7 3.6 24.3 R&I% of obs. 18.3 2.4 20.7 R&I and S&P (total no. of obs. 166) S&P no 42 6 48 R&I no 31 4 35 S&P% of obs. 25.3 3.6 28.9 R&I% of obs. 18.7 2.4 21.1 R&I and Fitch (total no. of obs. 159) Fitch no 41 5 46 R&I no 31 4 35 Fitch% of obs. 25.8 3.1 28.9 R&I% of obs. 19.5 2.5 22.0

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flexible working rules (e.g., Hungary, Poland and Romania). Czech Republic and Ukraine were upgraded during the study period based on their good economic growth, supported by strong exports and foreign direct investment. The sovereign rating of the Slovak Republic was upgraded many times, supported by rapid progress in public sector reform that improved the fiscal position, and also by strong foreign direct investment. Middle East countries, such as Saudi Arabia, gained from high oil prices, which create huge trade surpluses, but the region's political instability (e.g., Lebanon) created little growth outside of the energy sector. The sovereign rating of Morocco was upgraded on the basis of the government's ongoing economic reform efforts and an improved external position, bolstered by increasing export demand from the Eurozone and large tourism receipts. China's move to a mixed economy generated strong growth, even with government control of major heavy industries. Adoption of Western technologies, allowing productivity to improve, and low labour costs, supported by holding down the exchange rate, made China very competitive in export markets. India's economic growth began to accelerate, helped by strong capital account inflows and increasing foreign exchange reserves. Indonesia improved its external position, supported by improved commodity prices, lower labour costs, weaker imports and strong portfolio capital inflows, leading to an increase in its foreign reserves, which enabled Indonesia to repay half of its outstanding $7.5 billion IMF loan in June 2006. During the sample period, some countries in Latin America, such as Brazil, Chile, Mexico and Peru, showed solid growth. Brazil pushed ethanol production to reduce reliance on imported oil. Brazil had developed a consistent macroeconomic structure comprising a floating exchange rate regime and fiscal consolidation and inflation targeting strategies. The economic growth in Chile reflected modernization of public institutions and strengthening financial profile, supported by monetary stability and successful inflation targeting. A low inflation rate, a flexible exchange rate, skilful debt management and growing integration with the U.S. economy contributed to greater economic stability in Mexico, which in turn led to enhancement in external liquidity and deepening domestic financial markets. Peru demonstrated deepening macroeconomic stability, a strong commitment to fiscal responsibilities, and an improving external liquidity position, boosted by robust export growth.8 Table 2 documents the frequencies of agreement and disagreement across rating agencies for each agency pair. The split ratings across agencies represent more than half of all observations; with the exception of the case of S&P and Fitch (see Column 5). S&P and Fitch have by far the lowest frequency of split ratings (34.6%) between agencies. Moody's and S&P have different sovereign ratings in 59.4% of cases, while 57.6% of emerging sovereigns rated jointly by Moody's and Fitch have different ratings. CI tends to be the harshest agency in this sector, with mostly lower CI ratings compared with the sovereign ratings of the larger three agencies. Disagreements between R&I/JCR and the larger three agencies are very frequent. JCR seems to be the most generous agency with mostly higher JCR ratings compared with the sovereign ratings of the larger three agencies. To better understand the relation between sovereign split ratings and rating changes, we track the rating history of the emerging sovereign issuers with split ratings in our sample. We identify that issuers are often upgraded (downgraded) by the agency assigning a lower (higher) rating one year earlier. We also identify many cases whereby rating agencies change their ratings toward the other's rating, resulting in rating convergence. Table 3 provides some examples. The impact of split ratings on rating changes is analysed by employing the ordered probit modelling approach. This approach considers the discrete, ordinal nature of rating changes. It has been widely used in a variety of contexts in credit rating research, and has been demonstrated to have superior properties to OLS for this type of application (Hu et al., 2002; Bennell et al., 2006). Subsequent downgrade and upgrade models for each pair of agencies are estimated separately due to expected dissimilar behaviour (see Fuertes and Kalotychou, 2007; Livingston et al., 2008). Rating changes are identified by notches (one-notch and more-than-one-notch) using the 20-point rating scale and on the basis of 1-year intervals. The test variable is split rating. Both patterns of split rating (rating from a given agency is higher or lower than from the other agency) and the level of split rating (split-with-one-notch and split-with-more-than-one-notch) are considered since these characteristics may have different effects for rating actions (Livingston et al., 2008).

8 For more details about the reasons behind the boom in emerging economies, see Banerjee (2006), Chambers (2006), Wyss (2006), Al-Sakka and ap Gwilym, 2009.

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Table 2 (Dis)Agreements across rating agencies on sovereign ratings in emerging countries, January 2000 to January 2008. Agencies

Whole Non-split Split Split % No. of 1-n higher More-than-1-n 1-n lower More-than-1-n countries sample of whole from first higher from from first lower from sample agency first agency agency first agency

S&P and Moody's S&P and Fitch Fitch and Moody's CI and Moody's CI and S&P CI and Fitch R&I and Moody's R&I and S&P R&I and Fitch JCR and Moody's JCR and S&P JCR and Fitch Column Number

46 44 42 25 26 22 19 19 18 14 14 14 1

401 353 356 221 214 186 169 166 159 112 112 112 2

163 231 151 92 102 85 49 64 56 18 33 33 3

238 122 205 129 112 101 120 102 103 94 79 79 4

59.4 34.6 57.6 58.4 52.3 54.3 71.0 61.5 64.8 83.9 70.5 70.5 5

99 39 86 27 24 11 58 67 58 36 44 42 6

20 4 23 5 0 0 29 26 27 33 33 30 7

77 66 68 54 67 64 11 9 18 25 2 7 8

42 13 28 43 21 26 22 0 0 0 0 0 9

Additionally, two sets of control variables are included as follows. First, four region dummy variables (r) indicating the geographical region of the emerging country of interest (see Al-Sakka and ap Gwilym, 2009). According to the World Bank regional classification, four regions are considered: East Asia & Pacific and South Asia (E S Asia), Europe and Central Asia (EU-CA), Latin America (LA), and Middle East, North Africa and Sub-Saharan Africa (ME & Af), where EU-CA is used as the reference region. Second, five current rating dummy variables (Rating_A): A, BBB, BB, B and CCC, where BBB rated issuers are the base case (Livingston et al., 2008). The ordered probit specification is defined as follows: yit = β1 1N H Ait−1 + β2 2N H Ait−1 + β3 1N L Ait−1 + β4 2N L Ait−1 + ζri + γRating Ait−1 + εit ; εit ∼Nð0; 1Þ

ð1Þ i = 1,…C (number of countries), t = 2,…9 years. yit is an ordinal variable equal to either UPit or DWit. UPit(DWit) = 1 or 2 if an emerging sovereign i was upgraded (downgraded) by agency A by one or more-than-one-notch, respectively, in year t, 0 otherwise. 1N_H_Ait − 1 is a dummy variable taking the value of 1 if an emerging sovereign issuer has one-notch higher rating from given agency A than from agency B at year t − 1, zero otherwise. 2N_H_Ait − 1 is a dummy variable taking the value of 1 if an emerging sovereign issuer has more-thanone-notch higher rating from given agency A than from agency B at year t − 1, zero otherwise. 1N_L_Ait − 1 is a dummy variable taking the value of 1 if an emerging sovereign issuer has one-notch lower rating from given agency A than from agency B at year t − 1, zero otherwise. 2N_L_Ait − 1 is a dummy variable taking the value of 1 if an emerging sovereign issuer has more-thanone-notch lower rating from given agency A than from agency B at year t − 1, zero otherwise. ri is the region indicator dummy variables. Rating_Ait− 1 is the current rating dummy variable.

The economic impact of the four split rating dummies, the region indicator dummy variables and the current rating dummy variable on the probabilities of annual rating upgrades and downgrades are calculated (the marginal effects). The marginal effects are the partial derivative of the predicted probability of the dependent variable that results when the independent dummy variable takes the value of 1 while the other variables are held at their mean. 4. Empirical results The results discussion initially focuses on the split rating effect. Region and rating level effects are discussed at the end of this section. Table 4 presents the results for issuers jointly rated by Moody's and

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Table 3 Some examples of split rated emerging sovereigns that experienced rating changes during the following year. A

B

C

D

E

F

G

H

I

J

Date

Sovereign

S&P

Moody's

Rating action next year

2005 2005 2006 2006 2006 2007 2002

Brazil Turkey Chile China India Mexico Egypt

BB− BB− A A− BB+ BBB BBB−

B1 B1 Baa1 A2 Baa3 Baa1 Ba1

Upgrade by Moody's to Ba3 Upgrade by Moody's to Ba3 Upgrade by Moody's to A2 Upgrade by S&P to A Upgrade by S&P to BBB− Upgrade by S&P to BBB+ Downgrade by S&P to BB+

Date

Sovereign

S&P

Fitch

Rating action next year

2001 2003 2005 2005 2001 2005 2006

Tunisia Brazil Chile Mexico Colombia India China

BBB B+ A BBB BB BB A−

BBB− B A− BBB− BB+ BB+ A

Upgrade by Fitch to BBB Upgrade by Fitch to B+ Upgrade by Fitch to A Upgrade by Fitch to BBB Downgrade by Fitch to BB Upgrade by S&P to BB+ Upgrade by S&P to A

Date

Sovereign

Fitch

Moody's

Rating action next year

2005 2005 2006 2005 2006 2007 2002

Brazil Turkey Chile China India Mexico Egypt

BB− BB− A A− BB+ BBB BBB−

B1 B1 Baa1 A2 Baa3 Baa1 Ba1

Upgrade by Moody's to Ba3 Upgrade by Moody's to Ba3 Upgrade by Moody's to A2 Upgrade by Fitch to A Upgrade by Fitch to BBB− Upgrade by Fitch to BBB+ Downgrade by Fitch to BB+

Date

Sovereign

CI

Moody's

Rating action next year

2006 2006 2007

Indonesia South Africa India

B+ BBB− BB+

B2 Baa1 Baa3

Upgrade by Moody's to B1 Upgrade by CI to BBB+ Upgrade by CI to BBB−

Date

Sovereign

CI

S&P

Rating action next year

2007 2007

China India

BBB+ BB+

A BBB−

Upgrade by CI to A Upgrade by CI to BBB−

Date

Sovereign

CI

Fitch

Rating action next year

2006 2007

Malaysia India

BBB+ BB+

A− BBB−

Upgrade by CI to A− Upgrade by CI to BBB−

Date

Sovereign

R&I

Moody's

Rating action next year

2007 2002

Brazil Ukraine

BB+ CCC+

Ba2 B2

Upgrade by Moody's to Ba1 Upgrade by R&I to B

Date

Sovereign

R&I

S&P

Rating action next year

2002 2005

Ukraine Mexico

CCC+ BBB−

B BBB

Upgrade by R&I to B Upgrade by R&I to BBB

Date

Sovereign

R&I

Fitch

Rating action next year

2005 2006

Indonesia Uruguay

B B

BB− B+

Upgrade by R&I to BB− Upgrade by R&I to B+

Date

Sovereign

JCR

Moody's

Rating action next year

2001 2002

Turkey Mexico

BB+ BBB

B1 Baa3

Downgrade by JCR to B+ Upgrade by Moody's to Baa3

Date

Sovereign

JCR

S&P/Fitch

Rating action next year

2005 2006

Turkey Slovak

B+ A−

BB− A

Upgrade by JCR to BB− Upgrade by JCR to A

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Table 4 Rating migration and split ratings between Moody's and S&P — Eq. (1). Indep

Coef

t-val

ME%

Coef 2

3.57 3.26 −0.81 −1.65 −1.81 − 3.75 − 2.40 − 0.45 −0.07 0.25 0.83

12.3 18.0 − 3.5 −8.8 − 6.6 −13.2 − 9.7 − 2.2 −0.3 1.0 5.6

8.0 21.8 − 1.6 − 3.5 − 2.9 − 6.2 − 3.9 − 1.0 − 0.1 0.5 3.4

− 0.43 − 1.08 Merged with 1N-H-S&P 0.26 0.69 0.01 0.02 0.42 0.78 0.95 ⁎ 1.81 Merged with E S Asia 0.64 0.95 0.42 0.88 0.90 ⁎ 1.85 0.50 0.73 11.47%

0.10 − 0.20 2.74 1.85 − 2.18 − 3.87 − 2.59 − 2.49 −1.25 2.30 3.88

0.5 − 2.5 16.0 10.2 −11.7 −19.9 −16.5 −15.1 −6.9 14.4 29.9

0.1 − 0.4 3.7 2.2 − 1.6 − 3.0 − 2.0 − 1.9 − 1.1 3.2 18.0

− 0.11 − 0.06 − 0.39 − 0.08 −0.10 0.51 ⁎ − 0.32 0.21 0.33 0.52 0.23 7.38%

Moody's upgrades 1N-H-S&P 2N-H-S&P 1N-L-S&P 2N-L-S&P E S Asia LA MidEast & Afr A BB B CCC Pseudo R2

0.65 ⁎⁎⁎ 1.11 ⁎⁎⁎ − 0.20 − 0.55 ⁎ − 0.38 ⁎ −0.78 ⁎⁎⁎ −0.61 ⁎⁎ −0.12 − 0.02 0.05 0.29 10.08%

0.02 −0.09 0.55 ⁎⁎⁎ 0.35 ⁎ −0.45 ⁎⁎ −0.75 ⁎⁎⁎ −0.66 ⁎⁎⁎ −0.59 ⁎⁎⁎ −0.25 0.49 ⁎⁎ 1.28 ⁎⁎⁎ 11.95%

ME% 1

2

− 0.6

− 0.9

Moody's downgrades

S&P upgrades 1N-H-S&P 2N-H-S&P 1N-L-S&P 2N-L-S&P E S Asia LA MidEast & Afr A BB B CCC Pseudo R2

t-val

1

0.5 0.02 0.8 2.0

0.8 0.0 1.3 3.8

1.5 0.8 2.2 1.2

2.8 1.4 4.4 2.1

− 0.7 − 0.4 − 2.2 − 0.5 − 0.7 3.9 − 1.9 − 1.6 2.4 4.3 1.8

− 0.5 − 0.3 − 1.4 − 0.4 − 0.4 2.9 − 1.2 1.1 1.7 3.4 1.3

S&P downgrades − 0.43 − 0.12 − 1.19 − 0.19 − 0.28 1.74 0.68 0.53 0.99 1.51 0.44

Rating upgrade/downgrades are identified by notches (one and more-than-one-notch) using a 20-point rating scale and on the basis of 1-year intervals during the period of January 2000–January 2008. ⁎⁎⁎ Significant at 1% level. ⁎⁎ Significant at 5% level. ⁎ Significant at 10% level.

S&P. The coefficients on one-notch and more-than-one-notch higher (lower) S&P dummy variables are positive and significant for Moody's (S&P) rating upgrades, implying that split rated emerging sovereigns with higher (lower) S&P rating are more likely to be upgraded by Moody's (S&P) in the following year than non-split rated issuers. The coefficient for the more-than-one-notch lower S&P dummy variable is also negative and significant for Moody's rating upgrades, indicating that split rated issuers with more-thanone-notch lower S&P ratings are less likely to be upgraded by Moody's than non-split rated sovereigns within a one-year interval. Panel A of Table 3 provides some examples supporting the results. Marginal effects analysis shows that issuers with one-notch/more-than-one-notch higher (lower) S&P ratings increase the probabilities of Moody's (S&P) upgrades by one-notch and more-than-one-notch by 12.3%/ 18.0% (16.0%/10.2%) and 8.0%/21.8% (3.7%/2.2%). Issuers at more-than-one-notch lower S&P than Moody's category have decreased probabilities of upgrades of one-notch and more-than-one-notch by Moody's in the next year by 8.8% and 3.5%. These results imply that the harsher the split rating between Moody's and S&P, the greater is the effect on Moody's rating upgrades. These rating change probabilities are highly significant bearing in mind that on average only 19.7% (25.7%) of Moody's (S&P) emerging sovereign issuers experience rating upgrades within a 1-year interval during this sample period (see Table 1). The results in Table 4 also reveal that Moody's and S&P rating downgrades are not influenced by split ratings between these agencies. This may be driven by relatively low annual downgrade percentages during this sample period (2.7% by Moody's and 6.5% by S&P) (see Table 1). Table 5 demonstrates the results for emerging sovereign issuers jointly rated by Fitch and S&P. The coefficients for all split dummy variables in the S&P upgrade equation, the coefficients for one-notch higher/

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Table 5 Rating migration and split ratings between Fitch and S&P — Eq. (1). Indep

Coef

t-val

ME%

Coef

1

2

24.2

5.2

− 17.6 8.7 − 9.4 − 19.7 − 14.0 − 9.1 0.5 32.3 15.7

− 1.5 1.2 − 0.9 − 1.9 − 1.1 − 0.9 0.1 9.8 2.8

− 12.7

−1.7

13.6 16.7 − 16.4 − 19.3 − 14.8 − 13.2 −5.7 14.1 27.8

3.0 4.7 − 2.2 − 2.9 − 1.8 −1.8 −0.9 3.3 13.7

Fitch upgrades 1N-H-S&P 2N-H-S&P 1N-L-S&P 2N-L-S&P E S Asia LA MidEast & Afr A BB B CCC Pseudo R2

0.80 ⁎⁎⁎ 3.64 Merged with 1-N-H-S&P −0.71 ⁎⁎⁎ − 2.82 0.29 0.71 −0.34 − 1.53 − 0.75 ⁎⁎⁎ − 3.49 − 0.56 ⁎⁎ − 1.96 − 0.33 − 1.35 0.02 0.09 1.14 ⁎⁎⁎ 4.19 0.51 1.20 15.68%

−0.48 ⁎ −1.77 Merged with 1-N-H-S&P 0.46 ⁎⁎ 2.24 0.57 ⁎ 1.84 −0.63 ⁎⁎⁎ − 2.84 −0.72 ⁎⁎⁎ − 3.46 −0.57 ⁎⁎ − 2.03 −0.49 ⁎⁎ −2.05 −0.20 − 0.95 0.48 ⁎⁎ 1.92 ⁎⁎⁎ 1.10 3.06 13.01%

ME% 1

2

Fitch downgrades

S&P upgrades 1N-H-S&P 2N-H-S&P 1N-L-S&P 2N-L-S&P E S Asia LA MidEast & Afr A BB B CCC Pseudo R2

t-val

Merged with the reference category (no split) Merged with the reference category (no split) 0.83 ⁎⁎⁎ 2.62 4.5 4.7 0.74 1.26 4.3 4.7 − 0.26 −0.59 − 0.9 − 0.7 0.48 1.32 2.0 1.8 0.22 0.41 0.9 0.8 0.11 0.25 0.4 0.4 0.20 0.50 0.8 0.6 −0.52 − 0.90 − 1.5 − 1.1 Merged with B 10.65% S&P downgrades − 0.42 − 0.89 Merged with 1-N-H-S&P −0.002 −0.01 − 0.004 − 0.01 0.003 0.01 0.41 1.33 −0.15 − 0.30 0.20 0.51 0.15 0.41 0.45 1.15 0.32 0.60 4.63%

− 2.4

− 1.3

− 0.01 − 0.03 0.02 3.1 − 1.0 1.5 1.00 3.8 2.6

− 0.01 −0.02 0.01 2.1 − 0.6 1.0 0.7 2.8 1.9

Rating upgrade/downgrades are identified by notches (one and more-than-one-notch) using a 20-point rating scale and on the basis of 1-year intervals during the period of January 2000–January 2008. ⁎⁎⁎ Significant at 1% level. ⁎⁎ Significant at 5% level. ⁎ Significant at 10% level.

lower S&P dummy variables in the Fitch upgrade equation, and the coefficient for one-notch lower S&P dummy variable in the Fitch downgrade equation are significant, all with the anticipated signs. Panel B of Table 3 provides some examples supporting the results. However, split ratings between Fitch and S&P have no significant effect on subsequent S&P downgrades. Marginal effects analysis illustrates that issuers having one-notch higher (lower) S&P ratings increase (decrease) the probabilities of one-notch and more-thanone-notch annual rating upgrades by Fitch by 24.2% (17.6%) and 5.2% (1.5%), while decreasing (increasing) the probabilities of one-notch and more-than-one-notch rating upgrades by S&P by 12.7% (13.6%) and 1.7% (3.0%). In addition, issuers having more-than-one-notch lower S&P ratings have slightly greater likelihoods of being one-notch and more-than-one-notch upgraded the following year by S&P than those with onenotch lower S&P ratings (16.7% and 4.7% compared with 13.6% and 3.0%). Further, issuers with one-notch lower S&P rating have elevated downgrade probabilities of one-notch and more-than-one-notch by Fitch by 4.5% and 4.7% during the following year. These rating change probabilities are highly significant bearing in mind that on average only 24.9% (26.4%)/3.7% (5.7%) of Fitch (S&P) emerging sovereign issuers experience rating upgrades/downgrades within a 1-year interval during this sample period (see Table 1). Table 6 reports the results for emerging sovereign issuers jointly rated by Moody's and Fitch. In the Moody's upgrade equation, the coefficients on the four split rating dummy variables are significant with the appropriate sign, suggesting that higher (lower) Fitch rated sovereign issuers are more (less) likely to be upgraded by Moody's the following year. The results in the Fitch upgrade equation show positive and significant lower Fitch dummy variables, implying that those issuers are more likely to be upgraded by Fitch in the following year. Panel C of Table 3 provides some examples supporting the results. The marginal

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Table 6 Rating migration and split ratings between Moody's and Fitch — Eq. (1). Indep

Coef

t-val

ME%

Coef 2

1.72 3.29 −1.91 − 1.89 − 2.14 − 3.20 −1.75 − 1.07 − 0.73 − 1.04 NA

6.0 15.6 − 7.8 − 11.1 −7.8 − 10.8 − 7.7 − 4.6 −2.7 −4.0 NA

4.2 20.6 − 4.2 − 5.1 − 4.2 − 6.1 − 3.9 − 2.6 − 1.6 − 2.3 NA

− 0.25 − 0.63 0.40 0.82 − 0.35 − 0.70 0.79 1.48 − 0.13 −0.31 0.11 0.31 Merged with LA − 0.62 − 0.98 0.06 0.14 0.39 1.08 NA NA 8.35%

0.15 − 0.74 1.59 1.65 − 1.14 −2.66 − 0.48 −2.12 −0.58 2.84 0.98

0.8 − 7.5 8.9 12.8 − 6.6 − 13.6 − 3.4 12.7 − 3.2 18.4 10.2

0.2 − 1.4 2.3 4.1 − 1.3 − 2.7 − 0.7 − 2.3 − 0.7 6.7 3.0

0.59 ⁎⁎ 0.56 − 0.33 0.23 − 0.26 0.19 − 0.26 0.16 0.41 0.05 Merged with 8.63%

Moody's upgrades 1N-H-Fitch 2N-H-Fitch 1N-L-Fitch 2N-L-Fitch E S Asia LA MidEast & Afr A BB B CCC Pseudo R2

0.33 ⁎ 1.01 ⁎⁎⁎ − 0.48 ⁎⁎ − 0.77 ⁎⁎ − 0.48 ⁎⁎ − 0.66 ⁎⁎⁎ −0.48 ⁎ − 0.27 −0.15 −0.23 NA 8.00%

0.03 −0.29 0.32 ⁎ 0.47 ⁎ −0.25 −0.53 ⁎⁎⁎ −0.13 − 0.51 ⁎⁎ −0.12 0.68 ⁎⁎⁎ 0.37 7.89%

ME% 1

2

− 0.7 1.7 − 0.9 4.1 − 0.4 0.4

−0.6 1.6 − 0.7 4.5 − 0.3 0.3

− 1.4 0.2 1.5 NA

− 1.1 0.2 1.4 NA

2.7 2.8 − 1.0 1.0 − 0.8 0.7 − 0.8 0.5 1.6 0.2

3.1 3.5 − 1.0 1.0 − 0.8 0.7 − 0.8 0.5 1.7 0.2

Moody's downgrades

Fitch upgrades 1N-H-Fitch 2N-H-Fitch 1N-L-Fitch 2N-L-Fitch E S Asia LA MidEast & Afr A BB B CCC Pseudo R2

t-val

1

Fitch downgrades 1.85 1.20 −0.67 0.41 − 0.61 0.57 − 0.47 0.30 1.02 0.10 B

Rating upgrade/downgrades are identified by notches (one and more-than-one-notch) using a 20-point rating scale and on the basis of 1-year intervals during the period of January 2000–January 2008. ⁎⁎⁎ Significant at 1% level. ⁎⁎ Significant at 5% level. ⁎ Significant at 10% level.

effects calculations show that issuers with more-than-one-notch higher Fitch ratings have more than 3times greater probabilities of being upgraded by Moody's than those with one-notch higher Fitch ratings (15.6% and 20.6% compared with 6.0% and 4.2%). More-than-one-notch lower Fitch rated issuers have decreased probabilities of one-notch and more-than-one-notch annual Moody's upgrades by 11.1% and 5.1%, which are slightly greater likelihoods than for those one-notch lower rated issuers by Fitch (7.8% and 4.2%). Also, issuers with more-than-one-notch lower Fitch ratings have elevated probabilities of upgrades of one-notch and more-than-one-notch by Fitch the next year by 12.8% and 4.1%, which are higher probabilities than for those issuers with one-notch lower Fitch ratings (8.9% and 2.3%). Further, a onenotch higher current rating by Fitch increases the probabilities of one-notch and more-than-one-notch annual downgrades by Fitch by 2.7% and 3.1%. These rating change probabilities are highly significant bearing in mind that on average only 20.2% (25.3%)/3.1% (3.9%) of Moody's (Fitch) emerging sovereign issuers experience rating upgrades/downgrades within a 1-year interval in this sample (see Table 1). However, split rated issuers have no significant effects on Moody's downgrades, which is perhaps unsurprising given the small percentage of annual Moody's downgrades (3.1%) in this sample (see Table 1). Table 7 reports the results of the effects of split ratings between CI and the larger three agencies on their emerging sovereign rating actions.9 For Moody's and CI (Panel A), issuers with more-than-one-notch lower CI ratings have decreased probabilities of Moody's annual upgrades by one-notch and more-than-one9 Due to the small numbers of annual downgrades in the samples of emerging sovereigns jointly rated by CI and any one of the larger agencies, the downgrade regressions are not estimated for these samples (see Table 1).

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Table 7 Rating migration and split ratings between CI and larger three agencies — Eq. (1). Indep

Coef

t-val

ME% 1

Coef

Moody's upgrades Panel A — CI and Moody's 1N-H-CI 0.32 1.12 2N-H-CI Merged with 1N-H-Cl 1N-L-CI − 0.56 ⁎⁎ − 1.98 2N-L-CI − 1.01 ⁎⁎⁎ −2.65 E S Asia − 0.46 ⁎ − 1.83 LA NA NA MidEast & Afr − 0.70 ⁎⁎⁎ − 2.38 A − 0.26 −0.82 BB − 0.75 ⁎⁎⁎ −2.64 B − 0.40 − 1.26 CCC 0.25 0.32 7.64% Pseudo R2

6.0

4.2

−9.5 −15.2 −8.1 NA − 11.7 − 4.7 −12.3 −6.8 4.7

−4.9 −7.1 −4.5 NA − 6.1 − 2.5 − 6.2 − 3.5 3.3

2

− 0.77 ⁎ − 1.83 Merged with 1N-H-Cl 0.05 0.20 0.59 ⁎ 1.84 0.07 0.27 NA NA 0.27 0.89 − 0.03 −0.06 −0.06 − 0.22 0.72 ⁎⁎ 2.25 0.37 0.70 6.26%

− 13.9

− 3.7

1.1 13.2 1.6 NA 5.9 − 0.6 − 1.3 16.0 8.3

0.4 6.2 0.6 NA 2.3 − 0.2 − 0.4 9.0 3.7

− 4.6 NA 27.5 30.8 1.4 NA 7.3 3.4 3.0 14.5 − 0.2

− 0.9 NA 9.7 31.9 0.3 NA 1.7 0.8 0.7 4.5 − 0.01

CI upgrades 1.00 NA 1.49 − 0.67 −1.73 NA − 1.66 −1.96 − 1.57 1.23 2.64

10.1 NA 10.9 − 7.5 − 11.6 NA − 13.1 −14.7 − 11.9 11.6 30.5

1.3 NA 1.3 −0.7 − 1.1 NA −1.2 − 1.3 −1.1 1.6 33.5

Fitch upgrades Panel C — Fitch and CI 1N-H-CI 0.70 2N-H-CI NA 1N-L-CI 0.38 2N-L-CI 0.44 E S Asia − 0.29 LA NA MidEast & Afr − 0.13 A − 0.72 ⁎⁎⁎ BB − 0.42 B 0.55 CCC NA 8.58% Pseudo R2

ME% 1

CI upgrades

S&P upgrades Panel B — S&P and CI 1N-H-CI 0.33 2N-H-CI NA 1N-L-CI 0.36 2N-L-CI − 0.24 E S Asia − 0.4 LA NA MidEast & Afr − 0.48 ⁎ A − 0.55 ⁎⁎ BB − 0.41 B 0.38 CCC 1.91 ⁎⁎⁎ 11.40% Pseudo R2

t-val

2

− 0.22 NA 1.20 ⁎⁎⁎ 1.80 ⁎⁎⁎ 0.06 NA 0.31 0.15 0.13 0.59 ⁎ − 0.01 17.38%

− 0.42 NA 4.51 4.78 0.23 NA 1.06 0.34 0.46 1.78 −0.01

CI upgrades 1.56 NA 1.58 1.22 − 1.16 NA − 0.46 −2.53 − 1.44 1.46 NA

18.0 NA 9.6 11.3 −7.4 NA − 3.4 − 17.2 −10.4 14.6 NA

7.9 NA 3.4 3.6 − 1.7 NA −0.8 − 3.6 − 2.3 5.5 NA

Merged with the reference category NA NA NA 0.88 ⁎⁎⁎ 3.15 20.2 1.72 ⁎⁎⁎ 5.04 31.6 −0.31 −1.18 − 6.8 NA NA NA 0.11 0.34 2.5 − 0.14 −0.30 − 3.1 0.19 0.60 4.5 0.40 1.22 9.6 Merged with B 15.57%

NA 6.4 28.1 1.5 NA 0.6 − 0.7 1.1 2.9

Rating upgrade/downgrades are identified by notches (one and more-than-one-notch) using a 20-point rating scale and on the basis of 1-year intervals during the period of January 2000–January 2008. NA: no data available (see Table 2). ⁎⁎⁎ Significant at 1% level. ⁎⁎ Significant at 5% level. ⁎ Significant at 10% level.

notch by 15.2% and 7.1%, which are greater than the effect for those issuers with one-notch lower CI ratings (9.5% and 4.9%). Issuers with higher (more-than-one-notch lower) CI ratings have decreased (increased) CI annual upgrade probabilities of one-notch and more-than-one-notch by 13.9% (13.2%) and 3.7% (6.2%). These rating change probabilities are highly significant bearing in mind that on average only 20.8% (20.8%) of Moody's (CI) emerging sovereign issuers experience rating upgrades within a 1-year interval in this sample (see Table 1).

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Panel B of Table 7 shows that S&P and CI split rated issuers have no significant influence on S&P rating dynamics. In contrast, issuers with more-than-one-notch lower CI ratings have greater likelihoods to be upgraded by one-notch and more-than-one-notch by CI (30.8% and 31.9%) than those with one-notch lower CI ratings (27.5% and 9.7%). Two examples supporting the results appear in Panel E of Table 3. These rating change probabilities are highly significant bearing in mind that on average only 21.0% of CI issuers experience rating upgrades within a 1-year interval in this sample (see Table 1). Panel C of Table 7 documents the results for issuers jointly rated by Fitch and CI. Split ratings between Fitch and CI have no significant effect on Fitch rating actions. On the other hand, issuers currently assigned more-than-onenotch lower ratings by CI have far greater likelihood to be upgraded by CI by one-notch and more-thanone-notch (31.6% and 28.1%) than those with currently one-notch lower CI ratings (20.2% and 6.4%). Two examples supporting the results appear in Panel F of Table 3. These rating change probabilities are highly significant bearing in mind that on average only 20.4% of CI issuers experience rating upgrades within a 1year interval in this sample (Table 1). Results on the impact of split ratings between R&I and the larger three agencies on their rating actions are reported in Table 8.10 For Moody's and R&I in Panel A, the results show that more-than-one-notch lower (higher) R&I rated issuers have decreased probabilities of being upgraded by Moody's (R&I) by onenotch and more-than-one-notch by 14.6% (17.2%) and 5.5%. These rating change probabilities are highly significant bearing in mind that on average 20.7% (18.3%) of Moody's (R&I) emerging sovereign issuers experience rating upgrades within a 1-year interval in this sample (see Table 1). Panel B of Table 8 shows that split ratings between S&P and R&I have no significant effect on rating change decisions made by S&P. But issuers with higher (lower) R&I ratings have decreased (elevated) probabilities of upgrades by R&I the following year by 38.5% (57.9%). These rating change probabilities are highly significant bearing in mind that on average 18.7% of R&I issuers experience rating upgrades within a 1-year interval in this sample (see Table 1). Two examples supporting the results appear in Panel H of Table 3. Panel C of Table 8 reveals that split ratings between Fitch and R&I have no significant impact on rating changes' decisions made by Fitch. But issuers with higher (lower) R&I ratings have decreased (elevated) probabilities of upgrades by R&I the following year by 22.2% (39.8%). Two examples supporting the results appear in Panel I of Table 3. These rating change probabilities are highly significant bearing in mind that on average 19.5% of R&I emerging sovereign issuers experience rating upgrades within a 1-year interval in this sample (see Table 1). Results on the effects of split rating between JCR and the larger three agencies on sovereign rating dynamics are documented in Table 9.11 Disagreements with JCR have no significant effect on annual upgrades by the larger three agencies. For issuers jointly rated by Moody's and JCR, Panel A shows that an issuer with more-than-one-notch higher JCR rating has decreased probability of being upgraded by JCR by 35.0% the following year. In addition, a one-notch lower JCR rated issuer has increased probability of JCR annual upgrade by 40.6%. For issuers jointly rated by S&P and JCR (Panel B of Table 9), it is found that onenotch and more-than-one-notch higher JCR rated issuers have decreased probabilities of annual JCR upgrades by 29.9% and 44.2%. For issuers jointly rated by Fitch and JCR, the results in Panel C of Table 9 reveal that issuers with more-than-one-notch higher JCR ratings have decreased probability of being upgraded by JCR the following year by 38.4%, which is much stronger than the effect of those with onenotch higher JCR ratings (21.7%). These rating change probabilities are highly significant bearing in mind that on average 23.2% JCR emerging sovereign issuers experience rating upgrades within a 1-year interval in these samples (see Table 1). A further two interesting results also emerge from this analysis. First, across all models (in Tables 4–9), emerging sovereigns outside ‘Europe & Central Asia’ tend to be significantly less likely to be upgraded by Moody's, S&P and Fitch. This is mainly driven by the economic growth in emerging Europe.12 In comparison

10 Only 4 annual downgrades by R&I, 6 by Moody's, 6 by S&P, and 5 by Fitch are observed in the samples of emerging sovereigns jointly rated by R&I and Moody's/S&P/Fitch (see Table 1). Therefore, downgrade regressions are not estimated for these samples. 11 Only 3 annual downgrades by JCR, 3 by Moody's, 4 by S&P, and 4 by Fitch are observed in the samples of emerging sovereigns jointly rated by JCR and Moody's/S&P/Fitch (see Table 1). Therefore, downgrade regressions are not estimated for these samples. 12 Section 3 explains the variety of causes that have fuelled the economic growth in many emerging countries in Europe (and other regions) during the sample period, leading to the upward ratings trend.

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Table 8 Rating migration and split ratings between R&I and larger three agencies — Eq. (1). Indep

Coef

t-val

ME% 1

Coef

Moody's upgrades Panel A — Moody's and R&I 1N-H-R&I −0.02 2N-H-R&I 0.34 1N-L-R&I −0.78 2N-L-R&I −0.94 ⁎ E S Asia −0.44 LA 0.08 MidEast & Afr −0.88 ⁎⁎ A −0.10 BB −0.08 B −0.12 CCC NA 2 5.63% Pseudo R

− 0.06 0.98 − 1.17 − 1.74 − 1.38 0.22 − 2.06 − 0.25 − 0.21 −0.35 NA

− 0.4 6.8 − 12.1 − 14.6 − 8.3 1.6 − 14.1 − 1.8 − 1.5 −2.3 NA

1

2

− 0.2 4.1 − 4.3 − 5.5 − 4.0 0.9 − 5.4 − 0.9 − 0.7 − 1.1 NA

− 0.29 − 0.91 ⁎ − 0.51 0.45 0.36 − 0.18 − 0.20 − 0.39 0.58 0.88 ⁎⁎ 0.39 10.57%

− 0.93 − 1.88 − 0.67 0.98 0.96 − 0.41 − 0.43 − 1.14 1.46 2.09 0.45

− 7.4 −17.2 −10.5 13.5 9.7 −4.5 −4.9 − 9.2 17.9 29.1 11.8

− 1.52 ⁎⁎⁎

− 3.96

− 38.5

1.67 ⁎⁎ NA 0.74 ⁎⁎

2.30 NA 1.96 −1.27 1.36 −1.59 1.68 2.08 NA

57.9 NA 18.9 − 11.5 20.8 − 12.2 20.0 29.5 NA

− 0.85 ⁎⁎⁎

− 2.49

− 22.2

1.17 ⁎⁎⁎ NA 0.23 − 0.34 0.59 − 0.62 0.74 ⁎

2.76 NA 0.70 −0.80 1.20 −1.54 1.65 1.09 NA

39.8 NA 6.1 − 7.8 18.2 − 13.4 23.5 14.3 NA

R&I upgrades 1.30 1.33 0.04 NA − 0.08 0.74 − 0.58 − 0.81 − 0.66 2.05

13.3 20.00 0.8 NA −0.8 9.5 −7.7 −9.4 −6.4 27.8

− 0.60 0.71 − 0.61 0.68 ⁎ 0.94 ⁎⁎ NA 26.97%

Fitch upgrades Panel C — Fitch and R&I 1N-H-R&I 0.18 2N-H-R&I 0.23 1N-L-R&I 0.08 2N-L-R&I NA E S Asia − 0.06 LA 0.36 MidEast & Afr 0.01 A − 0.12 BB 0.02 B 1.32 ⁎⁎⁎ CCC NA 11.22% Pseudo R2

ME%

R&I upgrades

S&P upgrades Panel B — S&P and R&I 1N-H-R&I 0.39 2N-H-R&I 0.55 1N-L-R&I 0.02 2N-L-R&I NA E S Asia − 0.03 LA 0.27 MidEast & Afr − 0.24 A − 0.30 BB − 0.20 B 0.75 ⁎⁎ CCC Merged with B 7.78% Pseudo R2

t-val

2

R&I upgrades 0.57 0.56 0.18 NA − 0.20 0.97 0.02 − 0.31 0.06 2.99 NA

0.6 0.6 0.9 NA 0.9 0.3 1.0 0.8 1.0 0.1 NA

0.48 NA 22.65%

Rating upgrade/downgrades are identified by notches (one and more-than-one-notch) using a 20-point rating scale and on the basis of 1-year intervals during the period of January 2000–January 2008. For S&P (Fitch), there are 2 (4) observations at more-than-onenotch upgrade categories, which were thus merged with 1-notch upgrade (downgrade) category in these samples. NA: no data available (see Table 2). ⁎⁎⁎ Significant at 1% level. ⁎⁎ Significant at 5% level. ⁎ Significant at 10% level.

with emerging sovereign issuers in ‘Europe and Central Asia’, Latin America's issuers are more likely to be downgraded by Moody's and S&P. The underlying cause of this is that most of the sovereign crises during the sample period happened in this region, including the 2001–2002 Argentina credit crisis, Uruguay sovereign crisis in 2002–2003, Paraguay sovereign default in 2003, and the sovereign crises of Dominican

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Table 9 Rating migration and split ratings between JCR and larger three agencies — Eq. (1). Indep

Coef

t-val

ME% 1

Coef 2

Moody's upgrades Panel A — Moody's and JCR 1N-H-JCR 0.68 1.56 13.2 2N-H-JCR 0.70 1.53 13.5 1N-L-JCR − 0.79 −1.26 − 14.2 2N-L-JCR NA NA NA E S Asia − 0.76 ⁎⁎ −2.33 − 14.5 LA Merged with the reference category MidEast & Afr NA NA NA A 0.03 0.06 0.6 BB 0.001 0.01 0.02 B − 0.06 − 0.14 −1.1 CCC NA NA NA 2 8.55% Pseudo R

10.0 10.3 − 6.8 NA −8.7 NA 0.3 0.01 − 0.6 NA

1

2

0.08 0.17 2.2 − 1.65 ⁎⁎⁎ − 2.89 − 35.0 1.20 ⁎⁎ 1.95 40.6 NA NA NA 0.50 1.10 14.5 Merged with the reference category NA NA NA −1.43 ⁎⁎⁎ −3.03 −33.5 1.48 ⁎⁎⁎ 2.67 ⁎⁎⁎ 52.4 − 0.11 − 0.20 − 3.1 NA NA NA 25.61% JCR upgrades

2.0 2.4 NA − 2.7 NA −4.0 − 1.5 8.3 NA

Fitch upgrades Panel C — Fitch and JCR 1N-H-JCR −0.20 −0.56 − 5.6 2N-H-JCR −0.31 −0.73 − 8.6 1N-L-JCR Merged with the reference category 2N-L-JCR NA NA NA E S Asia −1.03 − 1.39 − 23.3 LA Merged with the reference category MidEast & Afr NA NA NA A −0.92 ⁎⁎ − 2.15 −9.1 BB −0.10 −0.30 − 23.2 B 0.86 ⁎⁎ 1.89 21.7 CCC NA NA NA 9.96% Pseudo R2

ME%

JCR upgrades

S&P upgrades Panel B — S&P and JCR 1N-H-JCR 0.31 0.89 9.3 2N-H-JCR 0.43 0.90 10.3 1N-L-JCR Merged with the reference category 2N-L-JCR NA NA NA E S Asia − 0.45 ⁎ −1.65 −13.5 LA Merged with the reference category MidEast & Afr NA NA NA A −0.94 ⁎⁎⁎ − 2.37 − 25.5 BB −0.27 −0.70 −8.0 B 0.78 ⁎ 1.73 21.8 CCC NA NA NA 12.92% Pseudo R2

t-val

− 1.34 ⁎⁎⁎ − 3.22 − 29.9 − 2.49 ⁎⁎⁎ − 4.67 −44.2 Merged with the reference category NA NA NA 0.05 0.13 − 1.3 Merged with the reference category NA NA NA − 1.18 ⁎⁎⁎ −2.71 − 26.2 0.76 1.47 24.2 − 0.52 − 0.78 − 10.8 NA NA NA 36.22% JCR upgrades

−1.3 − 1.9 NA − 3.5 NA − 2.2 − 4.3 11.5 NA

−2.38 − 21.7 − 0.86 ⁎⁎⁎ −2.02 ⁎⁎⁎ − 3.95 − 38.4 Merged with the reference category NA NA NA − 0.18 − 0.51 −5.0 Merged with the reference category NA NA NA − 1.07 − 2.68 − 26.1 0.62 1.29 20.4 −0.41 − 0.74 −9.9 NA NA NA 25.86%

Rating upgrade/downgrades are identified by notches (one and more-than-one-notch) using a 20-point rating scale and on the basis of 1-year intervals during the period of January 2000–January 2008. NA: no data available (see Table 2). ⁎⁎⁎ Significant at 1% level. ⁎⁎ Significant at 5% level. ⁎ Significant at 10% level.

Republic, Grenada and Venezuela in 2005. Second, highly rated emerging sovereign issuers (A) are less likely to be upgraded than BBB rated issuers by S&P, Fitch and JCR. There is no obvious pattern for speculative-grade issuers, except that B rated emerging sovereigns tend to be more likely to be upgraded than BBB rated issuers by S&P, Fitch, CI and R&I, given the upward trend in the data sample. In summary, the results emphasize that split ratings across agencies significantly affect sovereign rating dynamics in emerging countries. Split rated issuers are more likely to be upgraded by the rating agency

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from which a lower rating applies, and more likely to be downgraded by the rating agency from which a higher rating applies, within a 1-year subsequent interval. Additionally, the impact of split ratings is greater on rating upgrade decisions than on downgrades, but this issue is driven by the smaller percentage of annual downgrades compared with annual upgrades within the study's sample period. Also, the marginal effects analysis provides evidence suggesting that the harsher the split between two agencies, the greater the effect on the probabilities of future rating changes. These findings are in line with the opaqueness hypothesis of split ratings suggested by Morgan (2002), Livingston et al. (2007, 2008) and Hyytinen and Pajarinen (2008). Opaque issuers are more likely to receive split ratings, and also to have surprising information and unexpected news. Consequently, the opaqueness hypothesis also implies that split rated issuers will experience more rating migrations in the future. The results show that split ratings among the larger three agencies (Moody's, S&P and Fitch) are influential on each others' future rating migrations. S&P/Fitch rating dynamics appear not to be influenced by rating differentials between S&P/Fitch and the smaller agencies (CI, R&I and JCR). Moody's upgrade decisions do tend to be somewhat related to disagreements between its ratings and CI/R&I ratings. Models of rating behaviour of the smaller agencies always indicate a relation to their split ratings with the larger three agencies. 5. Conclusion The paper examines the relationship between split ratings and subsequent rating changes. Annual sovereign ratings of 49 emerging countries rated by at least two of six international credit rating agencies at 31st of January during the period from 2000 to 2008 are utilized. Split ratings and rating changes are defined by one-notch and more-than-one-notch using a 20-point numerical rating scale. The descriptive analysis of the data shows that emerging sovereign ratings changes are biased towards upgrade, reflecting the economic growth in emerging economies during the sample period. The disagreements across agencies represent more than half of all observations, except in the case of the S&P/Fitch pair. JCR is the most generous agency in assigning sovereign ratings, while CI seems to be the harshest agency. The high frequency of disagreements across agencies can be explained by rating agencies using varying methodologies, different quantitative/qualitative factors and different weights on these factors in assigning sovereign ratings. Rating agencies may also disagree to a greater extent about speculativegrade rated issuers (more opaque issuers with a high degree of instability and poor information quality), which represent 46.4% of the total number of observations. In addition, agencies may have better knowledge about countries in their “home region”, and thus assign favourable ratings for issuers located there. The Japanese agencies may have a tendency to assign higher ratings for issuers in Asia, while CI may tend to assign higher ratings for issuers in the Middle East and Africa. The impact of split ratings on rating changes is analysed by employing an ordered probit modelling approach, with subsequent one-notch and more-than-one-notch upgrades/downgrades as the dependent variable. Four dummy variables describing split ratings are included as independent variables (rating by a given agency is one-notch and more-than-one-notch higher or lower than the rating by the other agency). The paper identifies that “split rating” is a factor affecting the probability of future emerging sovereign rating migrations. Split rated emerging sovereigns are prone to be upgraded by the rating agency from whom a lower rating exists, and more likely to be downgraded by the rating agency from whom a higher rating exists, within a 1-year subsequent interval. The results draw attention to the greater influence of rating disagreement across agencies on rating upgrade decisions than on downgrade decisions, but this issue is driven by the smaller percentage of downgrades compared with upgrades within the study sample period. In addition, the marginal effects analysis reveals that the harsher the split between two agencies, the greater the effect on the probability of rating change. These results appear to support the opaqueness hypothesis of split ratings. Opaque issuers are more likely to receive split ratings, and thus split rated issuers will experience more rating changes in the future. Split ratings among the larger three agencies (Moody's, S&P and Fitch) are influential on their future rating migrations. The rating dynamics of the smaller agencies (CI, R&I and JCR) are always affected by their rating disagreements with other agencies. On the other hand, the rating differentials between S&P/Fitch and the smaller agencies tend not to impact upon the future behaviour of S&P/Fitch sovereign ratings. In contrast, Moody's upgrade decisions are influenced to some extent by rating splits between Moody's and

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both CI and R&I ratings. These results shed some light on the potential lead-lag relationships for emerging sovereigns across these rating agencies, and merits future investigation. It is expected that Moody's may both follow and lead rating changes made by the smaller agencies, while smaller agencies are affected by the rating adjustments of S&P and Fitch, but not vice versa. The prior literature on rating migration mostly uses data from one rating agency (Moody's or S&P) to estimate the probability of future rating migrations. This paper provides evidence suggesting that such estimation of sovereign rating migrations can be considerably improved by taking into account the effect of split ratings with other rating agencies. This has considerable relevance to current modelling of sovereign rating migration and to risk management practice. References Al-Sakka, R., ap Gwilym, O., 2009. Heterogeneity of sovereign rating migrations in emerging countries. 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