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The Uses and Abuses of Sovereign Credit Ratings
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The sharp downgrades of structured credit products that followed in the wake of the subprime mortgage crisis and the more recent downgrades accompanying weakened sovereign balance sheets have focused attention on credit rating agencies (CRAs) and their rating methodolo-gies. In part this attention reflects the myriad ways in which ratings drive investment deci-sions and collateral eligibility standards, even those of central banks. Securities regulations and rules have played a big part in this rating reliance, as well as prudential regulations. This chapter focuses on how well CRAs do their job and whether they inadvertently contribute to financial instability. The chapter specifically focuses on sovereign ratings, given the most recent escalation in sovereign credit risk and the propensity for ratings to affect sovereign debt markets. Although CRAs have been under a cloud of suspicion following their role in structured credit markets, it should be acknowledged that ratings serve several useful purposes. They aggregate information about the credit quality of borrowers, including sovereign entities, corporations, financial institutions, and their related debt offerings. They thus allow such borrowers to access global and domestic markets and attract investment funds, thereby adding liquidity to markets that would otherwise be illiquid. The chapter examines the top three CRAs (Fitch Ratings, Moody's Investors Service, and Standard & Poor's) to see whether they serve their various roles effectively and, more specifically, whether they rate sover-eign debt accurately. It concludes that CRAs' ratings influence market prices, and that downgrades through the investment-grade barrier trigger market reactions. It shows that their market impact is associated not only with new information, but also with a "certification" role, though this is most evident through their use of "outlooks," "reviews," and "watches" (pre-rating change warnings) rather than actual rating changes. CRAs insist that they do not target their ratings to specific credit risk metrics, such as default probabilities or expected losses, but only to ordinal rankings of credit risk. Tested against this objective, the chapter finds that the CRAs' discriminatory power of sovereign default risk is validated to some extent. For example, all sovereigns that defaulted since 1975 had noninvestment-grade ratings one year ahead of their default. Despite the CRAs' goals of delivering only ordinal rankings, ratings are often used as though they map into specific credit-risk metrics, including in the Basel II standardized approach to determining bank capital requirements. Given this important use, and assuming Basel II's reliance on ratings remains, CRAs should provide default probabilities or expected losses. Also, they should be expected to meet the same rating calibration and validation standards as those required of banks that use the Basel II internal-ratings-based approach, since the CRAs are a substitute for this more sophisticated approach. In addition, to reduce the negative "cliff effects" in prices and spreads that rating changes imply, the chapter recommends that regulations that hardwire buy or sell decisions to ratings be eliminated. This recommendation is already being implemented to some degree in some countries, but could usefully be extended. As well, CRAs should continue to provide additional information on the accuracy of their ratings, the underlying data, and their efforts to mitigate the conflicts of interest that are associated with their "issuer pay" model of charging issuers for their ratings.
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... Recebendo bastante atenção nas últimas décadas (White, 2018), as principais ACR do mercado são a Standards & Poor's, Moody's e Fitch Service. Mesmo não sendo as únicas fontes desse tipo de informação (White, 2018), elas promovem muita influência sobre os preços de mercados, e seus impactos estão associados não somente às novas informações, mas também às certificações, como o uso de Outlooks, revisões e supervisões (Kiff et al., 2010). ...
Objetivo: analisar as assimetrias de informações no conteúdo dos relatórios sintéticos emitidos pelas Agências de Classificação de Risco (ACR) Standard & Poor’s, Moody’s e Fitch sobre o ratingsoberano brasileiro. Método: realizou-se uma análise de conteúdo nos relatórios sintéticos das ACR com a finalidade de criar variáveis e categorias. Por meio da Análise de Correspondência (AC), objetivou-se essasmesmas variáveis de categorias em um espaço multidimensional para análise comparativa. Originalidade/Relevância: o estudo mostra como a assimetria de informações entre credores e mutuários de títulos soberanos também pode ser precedida pelas próprias ACR quando disponibilizam para o mercado diferentes avaliações sobre um mesmo produto financeiro. O resultado mostra que assimetria de informações entre credores e mutuários não finda, apenas muda a fonte de informação. Resultados: os resultados mostraram que, embora o teor das avaliações tenha sido negativo nas ACR, há assimetrias em categorias específicas. A Standard & Poor’s, por exemplo, foi a Agência que mais se destacou nas análises. Ela se difere das demais ACR e se posiciona mais livremente entre as categorias em anos distintos. Contribuições Teóricas/Metodológicas: contribui com a integração de métodos qualitativos e quantitativos para ampliação de pesquisas sobre assimetria de informações das ACR. O estudo também analisa relatórios sintéticos, isto é, os mais acessíveis aos tomadores de decisão no mercado, mostrando que não é exclusiva a análise de relatórios complexos e dados robustos para analisar informações assimétricas. Além disso, explora resultados sobre assimetria de informações e soberano brasileiro, onde poucas abordagens e investigações teve enfoque no cenário acadêmico nacional.
In this study, we propose both a multinomial logistic model and a fuzzy multinomial logistic model that utilize the transformation of variable techniques to simplify the expression of the logistic model. For this purpose, least-squares estimation (LSE) and fuzzy least-squares estimation (FLSE) have been employed. Our primary objective is to enhance the predictive capabilities of multi-class classification models, specifically for forecasting sovereign credit ratings. This research is driven by the need for advanced classification methods using multinomial logistic models that offer flexible reference class prediction, addressing the limitations of traditional logistic techniques in achieving consistent accuracy across diverse classes. We introduce four innovative classification methods for the multinomial logistic model and five for the fuzzy multinomial logistic model, applying them to a comprehensive dataset of sovereign credit ratings from 61 countries, spanning 1995 to 2022, sourced from Moody’s. Our proposed methods outperform conventional logistic techniques and maintain consistent predictive accuracy across a variety of classes when compared to traditional logistic classification methods and machine learning techniques such as decision trees, random forests, and support vector machines. The significance of our findings lies in the broad applicability of these methods beyond financial data; they can be adapted for any context that requires reliable multi-class classification. This advancement holds potential for improving decision-making processes in fields such as economics, risk management, and policy analysis by providing a more consistent and accurate predictive framework.
The history of Credit Rating Agencies [CRAs], commonly called Rating Agencies, has a long and distinguished trajectory marked by influence, reputation and power. Due to the ability of this field to instigate significant changes in market regulations and actions of economic actors, this subject is extensively debated within the literature. In economic sociology, while some studies have focused on perceptions of performativity and market devices to understand how the calculability of its methods influences the economy, others, along relational lines of sociology, aim to understand them through a more complex constructive process. In this study, we attempt to fill a gap by exploring the complementarity between two theoretical approaches to sociology: theory of fields and performativity. Thus, we mobilize theory of fields to understand the inter-field relationship between the State and CRAs. Specifically, this paper sets out to i) analyze the co-constitution of the state and market fields with the field of CRAs; ii) describe the existing regulation and situate the CRAs and their classification devices in the broader history of the State and the market; iii) analyze how the legitimation of the CRAs by the State generates broader social contexts that assist in the performative work of the CRAs. Based on historical documentary research, we analyze some of the main milestones and regulatory events in the field of CRAs. This research reaffirms the assumption that even after credibility crises, the State is the most significant predictor of legitimacy, power, and influence that CRAs hold to date. The legitimacy of this power and influence has yet to be exhausted because the reputation of CRAs is rooted in the reputation of the State itself. This happens because the State provides essential actions (such as regulations) for the existence of the financial market. Thus, results suggest that the State, through regulatory measures, can engage several other fields to promote the legitimacy of performative work of the CRAs, whose purpose is to provide calculation tools to shape markets.
Pakistan has received a total of 23 loan packages from the International Monetary Fund (IMF) between 1958 and 2023, and recurrent indebtedness has hindered structural transformation. Recent crises, such as the COVID‐19 pandemic, surging commodity prices, Russia's invasion of Ukraine and diplomatic tensions between the United States and China, have exacerbated Pakistan's indebtedness. This debt has geopolitical importance given the rivalry between the US and China. Multilateral support for restructuring has been complicated by Pakistan's unique alliance with China through the China‒Pakistan Economic Corridor, pivotal to the Belt and Road Initiative. This analysis of Pakistan's debt crisis explores this complexity by considering the Pakistani government's attempts to resist the IMF, particularly between 2018 and 2022, when the potential of China as an alternative source of financial support looked increasingly viable. Unlike less critical political analyses on debt, which tend to be preoccupied with endogenous governance failures and fiscal profligacy, this article focuses on the external drivers of debt. In doing so, it highlights the role of a hostile international legal system, standard‐setting arrangements, rating agencies and arbitrary charges that impose huge economic burdens and undermine financial stability, as well as the constraints embedded in the global investment and financial architecture that persistently limit Pakistan's policy space.
In order to identify novel qualitative determinants of transitions in sovereign credit ratings, we construct six different textual sentiment and subjectivity measures using dictionary-based, and machine learning approaches on sovereign credit rating reports issued by Moody’s and Fitch in the period from 2002 to 2017. After controlling for macroeconomic and fiscal strength, soft information, as well as known sources of proximity biases, we find that, on average, these novel text-based measures improve the classification accuracy of downgrades and upgrades. The improvement is more notable for sentiment than subjectivity measures, and for downgrades compared to upgrades. Next, we find evidence that credit rating agencies seem to follow the through-the-cycle rating philosophy by taking a longer horizon into account. Finally, to the best of our knowledge, we offer the most comprehensive analysis of textual sentiment measures and their effect on sovereign credit ratings thus far.
In this study, we use multiple-unit symbolic dynamics and transfer entropy to develop a non-parametric Granger causality test procedure for longitudinal data. Monte Carlo simulations show that our test exhibits the correct size and a high power in situations where linear panel data causality tests fail, such as (1) when the linearity assumption does not hold, (2) when the data generating process is heterogeneous across the cross-section units or presents structural breaks, (3) when there are extreme observations in some of the cross-section units, (4) when the process exhibits causal dependence on the conditional variance, or (5) when the analysis involves qualitative data. We illustrate the usefulness of our proposed procedure by analyzing the dynamic causal relationships between public expenditure and GDP, between firm productivity and firm size in US manufacturing sectors, and among sovereign credit ratings, growth, and interest rates.
Using long-term sovereign ratings data for a panel of 130 countries over the last three decades, we investigate the duration and determinants of sovereign rating phases through the lens of discrete-time Weibull models. We find that the likelihood of the end of the 'speculative-grade’ phase increases as time goes by (i.e. there is positive duration dependence), but the ‘investment-grade’ phase is not duration dependent. Thus, for sovereigns rated as speculative, the build-up of reputation as good borrowers is a gradual process, whereas the reputation of investment-grade sovereigns solidifies and remains unchanged as time passes. However, the length of both phases significantly depends on the country's economic conditions. In particular, lower inflation, stronger growth and sounder fiscal policies shorten (prolong) the speculative- (investment-) grade phase. In addition, better governance quality helps to reduce the duration of speculative-grade phases.
The events linked to the subprime crisis undoubtedly damaged the reputation of credit rating agencies (CRAs), at least in the short and medium-term. In this paper, we intend to gauge the extent of that reputational damage by examining the reaction of the market to rating actions. Using a standard event study methodology, we measure the abnormal return of stock prices in the three-day window centred on the announcement day during the period November 2003 – November 2013. Our theory is that the market reaction to downgrades, upgrades and credit watches is lower – after the crisis – than it used to be due to a lack of trust in the neutrality and reliability of the rating agencies. We expect the phenomenon to be more pronounced for the three major CRAs – Moody’s, Standard & Poor’s and Fitch – who had more direct involvement in the scandal. For the minor players, the impact of the crisis is potentially twofold. On one side, they may have suffered from a general mistrust of the rating industry. On the other side, they may have benefited from a higher level of attention from investors in search for a cross-check of opinions.The evidence strongly supports the theory of a lower market reaction to rating announcements, especially for the three major agencies. The abnormal returns of equity prices in an event window of a rating action are significantly lower after the crisis than they were before, after considering various explicative factors relating to the features of the announcement and the market conditions in terms of volatility. In line with previous literature on the topic, we find that, due to the “certification” role that many regulations grant to rating agencies, the abnormal return is stronger when the valuation is near to the border between investment and speculative grade. As a consequence, where the certification role is prevalent, there is no difference in the market reaction to announcements before and after the crisis. Conversely, the cumulative abnormal return is significantly lower after the crisis when there is no “regulation-induced” trading and the market investors’ behaviour is predominantly guided by the faith put in the informative content of the rating.
In this paper we study the determinants of sovereign debt credit ratings using rating notations from the three main international rating agencies, for the period 1995-2005. We employ panel estimation and random effects ordered probit approaches to assess the explanatory power of several macroeconomic and public governance variables. Our results point to a good performance of the estimated models, across agencies and across the time dimension, as well as a good overall prediction power. Relevant explanatory variables for a country's credit rating are: GDP per capita, GDP growth, government debt, government effectiveness indicators, external debt, external reserves, and default history.
This paper analyses lead–lag relationships in sovereign ratings across five agencies, and finds evidence of interdependence in rating actions. Upgrade (downgrade) probabilities are much higher, and downgrade (upgrade) probabilities are much lower for a sovereign issuer with a recent upgrade (downgrade) by another agency. S&P tends to demonstrate the least dependence on other agencies, and Moody’s tends to be the first mover in upgrades. Rating actions by Japanese agencies tend to lag those of the larger agencies, although there is some evidence that they lead Moody’s downgrades.
A rating system's performance should be measured by its success or failure in meeting its objectives. This Special Comment discusses various metrics which may be used to judge rating systems and the application of these metrics to Moody's historical performance. Moody's corporate bond ratings are intended to be "accurate" and "stable" measures of relative credit risk, as determined by each issuer's relative fundamental creditworthiness and without reference to explicit time horizons. Moody's performance should therefore be measured by both rating accuracy (the correlation between ratings and defaults) and rating stability (the frequency and magnitude of ratings changes). To measure the accuracy of its corporate bond ratings Moody's tracks the cumulative accuracy profiles and accuracy ratios of its ratings, since relative rating accuracy is a primary objective of its rating system. Moody's also tracks investment-grade default rates and the average rating of defaulting issuers prior to their defaults. These metrics measure Moody's success at meeting a secondary cardinal or absolute rating system objective, namely that ratings be useful to investors who employ simple rating "cutoffs" in their investment eligibility guidelines. Moody's does not target specific default rates for the individual rating categories. For rating systems that do have this type of cardinal objective, performance can be measured by the stability of realized default rates by rating category around target default rates. To measure ratings stability Moody's tracks the frequency of rating changes, the frequency of large rating changes, and the frequency of rating reversals. Moody's ratings should change only when relative fundamental creditworthiness changes. Since relative fundamental credit risk generally changes quite slowly, ratings should be stable, especially when compared with other market based risk measures. Infrequent reversals, and stability more generally, facilitate the use of ratings in connection with investment eligibility guidelines and performance benchmarks. The accuracy and stability of Moody's ratings has fluctuated over time. Moody's rating accuracy ratios are currently within historical norms, which range from the lows of the early 1980's to a high in the mid-1990's. Current rating stability is low by historical norms. Changes in ratings performance may reflect either changes in the quality of the ratings process or changes in the environment that make defaults more or less difficult to predict, or make fundamental creditworthiness more volatile, thus making rating stability harder to maintain. To control for changes in the economic environment, we also compare the accuracy and stability of Moody's ratings to the accuracy and stability of other credit risk measures, such as bond market-implied ratings inferred from observed credit spreads. Over the past four years, compared to Moody's ratings, bond market-implied ratings have been more correlated with defaults at one-year horizons, but their accuracy ratios are comparable as the horizon lengthens to three years. Moody's ratings have been much more stable than market-based credit opinions. In any given twelve-month period, there is about one rating action for every four issuers and only one out of every 100 issuers experiences a rating reversal (a rating change followed by a rating change in the opposite direction). In contrast, based on market-implied ratings (inferred from monthly bond spreads), nine out of ten issuers experience rating changes each year and seven out of ten experience rating reversals. This study is limited to the assessment of the accuracy and stability of Moody's ratings; however, a complete analysis of Moody's rating system also requires an analysis of its outlooks and Watchlist designations, which will be a subject of Moody's future research.
This article reviews developments in financial markets, including the Bank’s official operations, between the 2012 Q4 Quarterly Bulletin and 22 February 2013. The article also summarises market intelligence on selected topical issues relating to market functioning.
This paper explores the role played by multiple credit rating agencies (CRAs) in the market for corporate bonds. Moody’s, S&P and Fitch operate in a competitive setting with market demand for both credit information and the certification value of a high rating. We empirically document the outcome of this competitive interaction over the period 2002 to 2007. Virtually all bonds in our sample are rated by both Moody’s and Standard and Poors (S&P), and between 40% and 60% of the bonds are also rated by Fitch. This apparent redundancy in information production has long been a puzzle. We consider three explanations for why issuers apply for a third rating: ‘information production,’ ‘adverse selection’ and ‘certification’ with respect to regulatory and rules-based constraints. Using ratings and credit spread regressions, we find evidence in favor of Certification only. Additional evidence shows that the reported certification effects are consistent with an equilibrium outcome in a market with information-sensitive and insensitive bonds. In such a setting, ratings help to prevent market breakdowns.
The role and performance of credit-rating agencies are currently under debate. Several surveys conducted in the United States reveal that most investors believe rating agencies are too slow in adjusting their ratings to changes in corporate creditworthiness. It is well known that agencies achieve rating stability by their through-the-cycle methodology. This study provides quantitative insight into this methodology from an investor's point-in-time perspective and quantifies the effects of the methodology on three, somewhat conflicting, objectives: rating stability, rating timeliness, and performance in predicting defaults. The results can guide the search for an optimal balance among these three objectives.
We demonstrate that credit rating agencies aggravated the East Asian crisis. In fact, having failed to predict the emergence of the crisis, rating agencies became excessively conservative. They downgraded East Asian crisis countries more than the worsening in these countries' economic fundamentals would justify. This unduly exacerbated, for these countries, the cost of borrowing abroad and caused the supply of international capital to them to evaporate. In turn, lower than deserved ratings contributed – at least for some time – to amplify the East Asian crisis. Although this goes beyond the scope of our paper, we also propose an endogenous rationale for rating agencies to become excessively conservative after having made blatant errors in predicting the East Asian crisis. Specifically, rating agencies would have an incentive to become more conservative, so as to recover from the damage these errors caused to them and to rebuild their own reputation.