SOVEREIGN RATINGS AND THEIR ASYMMETRIC RESPONSE TO FUNDAMENTALS. Carmen Broto and Luis Molina. Documentos de Trabajo N.º PDF

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SOVEREIGN RATINGS AND THEIR ASYMMETRIC RESPONSE TO FUNDAMENTALS 2014 Carmen Broto and Luis Molina Documentos de Trabajo N.º 1428 SOVEREIGN RATINGS AND THEIR ASYMMETRIC RESPONSE TO FUNDAMENTALS SOVEREIGN

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SOVEREIGN RATINGS AND THEIR ASYMMETRIC RESPONSE TO FUNDAMENTALS 2014 Carmen Broto and Luis Molina Documentos de Trabajo N.º 1428 SOVEREIGN RATINGS AND THEIR ASYMMETRIC RESPONSE TO FUNDAMENTALS SOVEREIGN RATINGS AND THEIR ASYMMETRIC RESPONSE TO FUNDAMENTALS (*) Carmen Broto and Luis Molina BANCO DE ESPAÑA (*) Contact authors: We thank Enrique Alberola, Ignacio Hernando, Iikka Korhonen, Luis Orgaz and seminar participants at XI Emerging Market Workshop and the Banco de España for their helpful comments. The opinions expressed in this document are solely the responsibility of the authors and do not represent the views of the Banco de España. Documentos de Trabajo. N.º The Working Paper Series seeks to disseminate original research in economics and fi nance. All papers have been anonymously refereed. By publishing these papers, the Banco de España aims to contribute to economic analysis and, in particular, to knowledge of the Spanish economy and its international environment. The opinions and analyses in the Working Paper Series are the responsibility of the authors and, therefore, do not necessarily coincide with those of the Banco de España or the Eurosystem. The Banco de España disseminates its main reports and most of its publications via the Internet at the following website: Reproduction for educational and non-commercial purposes is permitted provided that the source is acknowledged. BANCO DE ESPAÑA, Madrid, 2014 ISSN: (on line) Abstract Changes in sovereign ratings are strongly asymmetric, as downgrades tend to be deeper and faster than upgrades. In other words, once a country loses its initial status it takes a long time to recover it. Using S&P data, we characterise rating cycles in terms of their duration and amplitude. We then study whether the agency reaction to new economic and fi nancial domestic information also differs during upgrade and downgrade phases. Our results indicate that favourable fundamentals could be helpful for smoothing and slowing down the path of downgrades, whereas favourable fundamentals do not seem to accelerate the rating recovery. Keywords: sovereign credit ratings, rating cycle, emerging countries, panel data model. JEL classification: G24, C33. Resumen La evolución de los ratings soberanos es profundamente asimétrica, ya que las fases de bajadas de la califi cación crediticia suelen ser más bruscas y de mayor intensidad que las de subidas. Dicho de otro modo, una vez que un país pierde su califi cación inicial, transcurre un período prolongado hasta que logra recuperarla, si es que lo consigue. En este artículo se utilizan las califi caciones soberanas de Standard and Poor s para caracterizar la duración y la amplitud de los «ciclos de rating». Posteriormente, se analiza si la forma en que esta agencia incorpora la nueva información económica y fi nanciera es distinta en los períodos de rebajas del rating y respecto de los de incrementos. Los resultados muestran que, mientras que la mejoría de los fundamentos económicos puede contribuir a suavizar y reducir la velocidad de las bajadas de califi cación, dicha recuperación no parece acelerar la senda de subidas. Palabras clave: ratings soberanos, ciclos de rating, economías con mercados emergentes, modelos de datos de panel. Códigos JEL: G24, C33. 1 Introduction Rating agencies have played a prominent role during the ongoing financial crisis. Agencies assign a credit rating to sovereign and private sector borrowers that indicates the probability of not fulfilling their obligations in their debt issues. Upgrade moves result from favorable signals in the credit outlook, whereas downgrades stem from unfavorable indicators. This permanent updating of the credit ratings is precisely one of the reasons why financial markets rely on agencies (Cantor and Packer, 1994). In this paper we focus on sovereign credit ratings. Understanding their dynamics is relevant given their implications for capital flows and their strong link with private ratings, either from banks and non financial corporations, in the sense that sovereign ratings represent a ceiling for corporate ratings (Alsakka and ap Gwilym, 2009; BIS, 2011). Besides, sovereign ratings are a main driver of sovereign bond spreads (see, for instance, Cantor and Packer, 1996), which in turn determine the financing costs of the public sector. Despite their importance, the agencies do not provide enough detail neither on the ratings determinants nor on their rating procedure (Mora, 2006), although some recent regulatory initiatives are trying to enhance the agencies transparency. 1 In this article we focus on Standard & Poors (S&P onwards) rating decisions and analyze how this agency updates sovereign ratings throughout time. In other words, we study sovereign rating cycles. Probably, in our setting the term cycle can be a misnomer as it suggests certain periodicity, but in the case of credit ratings such periodicity has not to be necessarily linked to the business cycle, as shown later on. Indeed, the term rating cycle has hardly been used in the literature. 2 In our setting, a complete credit cycle comprises a downgrade phase, when the rating goes from peak to trough, and an upgrade phase, when the rating improves, but not necessarily to reach its initial status. 1 In this sense, the EU Commission launched a regulatory reform of rating agencies on January While the EU regulatory framework for credit ratings already contains measures on disclosure and transparency, further measures such as the access to more comprehensive information on the data and the reasons underlying rating variations are needed. Although from 2013 on rating agencies are providing more methodological information (for instance, S&P, 2013), the final decision on rating variations is not exclusively linked to those models. The own rating agencies admit this fact (S&P, 2013): These criteria represent the specific application of fundamental principles that define credit risk and ratings opinions. Their use is determined by issuer or issue-specific attributes as well as S&P s Ratings Services assessment of the credit and, if applicable, structural risks for a given issuer or issue rating. 2 As far as we know, Sy (2002) and Koopman et al. (2009) are among the few works that specifically use the term rating cycle. BANCO DE ESPAÑA 7 DOCUMENTO DE TRABAJO N.º 1428 The number of countries that have already completed a rating cycle is rather small, and they are basically emerging countries (EMEs onwards). 3 Rating cycles are characterized by their strong asymmetries, as their length and depth (duration and amplitude) have a very different behavior in the upgrade and in the downgrade phases. In this sense, a remarkable stylized fact is that downgrade periods tend to be shorter than those of upgrade, as rating increases tend to be slower than decreases, which are more abrupt. In other words, once a country loses their rating level it takes a long period to recover it. For instance, Koopman et al. (2008) find out asymmetric effects across rating grades by means of a duration model with multiple states. These strong asymmetric dynamics are not only typical of ratings, but also of most financial variables that can be described by the so-called financial cycle (see, for instance, Aizenman et al, 2013). Given the above mentioned asymmetries in the ratings evolution, one possible interpretation could be that those signals that the agencies use to update ratings also exhibit asymmetries in the recession and recovery periods. But, how do the rating agencies really adjust to changes in the countries fundamentals and financial market conditions? 4 There is some empirical evidence that, broadly speaking, has concluded two different results. On the one hand, the less extended view supports the adequacy of ratings to their models based on the countries fundamentals. This is the case of Hu et al. (2002), who propose an ordered probit model to obtain estimates of the transition matrices. This brand of the literature would be implicitly supporting the use of a point-in-time strategy by rating agencies, so that they adapt to the borrower countries current conditions in an updated manner. On the other hand, most papers state that rating agencies do not adjust in an accurate way to the domestic indicators. For instance, some authors conclude that the agencies respond with certain lag to the domestic indicators. Along this line, Ferri et al (1999) analyze the East Asian crisis and deduce that rating agencies, which previously failed to predict the arrival of the 3 Our country classification between developed and emerging countries is in line with that of MSCI. We classify Korea, Latvia and the Czech Republic as EMEs, although the IMF does not consider these countries as EMEs. In our analysis, high income countries like Bermuda, Oman or Qatar are also EMEs. Our classification does not change throughout the sample period. 4 On the contrary, there is also a broad literature that analyzed the impact of rating changes on the financial and economic variables. See, for instance, Ferri et al. (1999) for an application for the East Asian crisis, or Alsakkasa and Gwilym (2013) for the European debt crisis. Larrain et al. (1997) and Reisen and von Maltzan (1998) also study this causal relation for emerging countries. In all these papers the authors demonstrate that the credit ratings amplified the boom-bust cycles. BANCO DE ESPAÑA 8 DOCUMENTO DE TRABAJO N.º 1428 crisis, had reputational incentives to downgrade these countries more than fundamentals would justify in subsequent periods, which, in turn, contributed to amplify the crisis. In other words, during downgrade phases, rating agencies would be excessively sensitive to fundamentals, so that sovereign ratings would have a procyclical nature. Monfort and Mulder (2000) also conclude the procyclycal nature of rating movements. On the contrary, Mora (2006), who also analyzes the Asian crisis, states that ratings are sticky rather than procyclical, so that ratings are adjusted only when there is a sufficiently large divergence of predicted ratings from assigned ratings. A widely accepted explanation for this sometimes inadequate timeliness of rating variations is the through-the-cycle methodology that agencies are supposed to apply in their rating assignments that leads to more stable ratings but less accurate (see, for instance, Löffler, 2004; Altman and Rijken, 2005; Kiff et al., 2013). This evolution of ratings comes as a result of the dilemma between accuracy and stability faced by the agencies (Cantor and Mann, 2006). 5 Thus, despite the initial ratings stability, ratings would be more prone to sudden reversals in downgrade phases that may result in market disruption and forced selling. Besides, the through-the-cycle strategy can be an explanation of the sudden drop of ratings during downgrade periods (Ferri et al., 1999; Kiff et al., 2013) and the low power of ratings to predict future defaults (Löffler, 2004; Kiff et al., 2013). Most of the empirical literature on the adjustment of credit ratings to fundamentals focuses on financial crisis periods, and less attention has been paid to their characterization during upgrade phases. Although there are several empirical papers analyzing the procyclical nature of corporate ratings and testing the hypothesis of rating through the cycle (see for instance Amato and Furfine, 2004), those empirical papers that have tried to characterize the dynamics of sovereign ratings and their link with the complete business cycle are scarce. In particular, those authors that analyze rating through-the-cycle conclude that in the recovery phase ratings are typically smoothed and, as in downgrade periods, are adjusted with a certain lag (Kiff et al., 2013). The main objective of this paper is twofold. First, we describe the S&P ratings evolution for a broad sample of countries to confirm the presence of asymmetries in the cycle, that is, if downgrade phases are faster and shorter than recovery periods. Second, once we confirm this evidence empirically, we try to disentangle the determinants of this different behavior of 5 The through-the-cycle methodology entails a focus on the permanent credit risk component that makes the agencies disregard short-term fluctuations and a prudent policy regarding rating changes (Altman and Rijken, 2005). BANCO DE ESPAÑA 9 DOCUMENTO DE TRABAJO N.º 1428 S&P ratings in both upgrade and downgrade periods by means of a sample of 67 countries, where 43 of them are EMEs. As far as we know, this is the first empirical paper that tries to characterize the link between domestic variables and the ratings evolution distinguishing upgrade and downgrade periods. Our results indicate that improving domestic fundamentals could be helpful to smooth the path of downgrades, whereas this stylized fact does not hold during upgrade phases. That is, once the initial rating of a country is lost, it takes a long time to recover it, and even with a favorable economic and financial performance the country would not accelerate the upgrade path. 6 Our findings are relevant to enhance the understanding of the performance of rating agencies and the interpretation of their signals to the markets. This kind of analysis could also be useful to infer some lessons about how future ratings recovery in the European peripheral countries would be once the sovereign debt crisis will be overcome. The remainder of this paper is organized as follows. Section 2 introduces our data on rating cycles and Section 3 describes our set of explanatory variables. In turn, Section 4 presents the methodological approach used in this paper. Finally, Section 5 summarizes the main results of our empirical analysis and Section 6 concludes. 2 How do rating cycles look like? Next, we analyze the characteristics of the credit cycle for the complete sample of countries for which S&P assigns a sovereign debt rating. Throughout the paper, we are going to use exclusively the ratings of this agency so as to not mix the data sources that could lead to measurement errors. This is a non-trivial issue as, despite the interdependence of rating actions of the three major agencies, their credit rating models are different (Hill et al., 2010). 7 In particular, S&P tends to be less dependent on other agencies and it provides the lowest and more volatile ratings among the three major ones (Alsakka and ap Gwilym, 2010). The choice of S&P is also based on the data availability for a higher number of countries and a larger period, which in our data description runs from January 1975 to May From 1975 the number of rated countries has gradually increased from two countries, namely the US and Canada, 6 Our results are in line with the theoretical papers by Bar-Isaac and Shapiro (2013) and Opp et al. (2013), who state that agencies tighten their ratings standards and accuracy during economic downturns. 7 Besides, Cantor and Packer (1996) conclude that sovereign ratings exhibit more discrepancies between agencies than corporate ratings. 8 See S&P (2013) for a detailed description of the methodology used by this agency. BANCO DE ESPAÑA 10 DOCUMENTO DE TRABAJO N.º 1428 to 127 economies in 2013, 100 EMEs and 27 developed ones (see Figure 1, right-hand plot). Throughout this section we describe S&P sovereign ratings on a daily basis from 1975 and for the whole set of rated countries. The complete country sample is enumerated in the Appendix A. From 1975 to 1988 the sample was dominated by AAA rated developed countries. 9 From that year onwards EMEs were gradually evaluated, which explains the higher range of ratings since that date (see Figure 1, left-hand plot). Thus, whereas in 1990 rating categories from AA- to AAA accounted for 67.7% of the total sample, in 2013 this percentage diminished to 24.4% as a result of the rating evaluation of EMEs and the downgrade of several developed countries. On the contrary, during this period the percentage of EMEs rated above AA- also diminished (from 22% in 1990 to 12% in 2013). Finally, also confirming the higher spectrum of rating categories towards lower ratings, the countries rated above BBB-, the category who marks the investment grade status, decreased from 97% in 1990 to 54% in In the same line, Kernel estimations for the complete rating range (Figure 2) also pointed to a change in the probability density functions throughout time, as in 2013 ratings were more concentrated in intermediate categories (from BB- to BBB+) than in 1995 or 2000, 10 due to an increase in rated EMEs, 11 and to a increase of density mass below AA- in developed economies. That is, former safest assets scaled back, as illustrated by the fact that from 2005 to 2013 the median rating fell from BBB+ to BBB-, as developed and EMEs sovereign assets became more risky (from AAA to AA+ and from BB+ to BB respectively). As a first evidence of the presence of asymmetries in the upward and downward rating paths, Table 1 presents the rating variations from 1975 to Given the evolution of developed countries, where downgrades represent 74% of total variations, the total country sample exhibits a higher number of downgrades than that of upgrades (53.1%). In EMEs, upgrades predominated by a narrow margin (52.3% of total changes). Most rating variations of developed countries are clustered between AAA and AA, whereas in EMEs most changes take place around B to BB. Finally, rating changes of more than three notches in a unique announcement are practically 9 In the 70s the rating scale did not include rating modifiers. 10 BBB- is the rating that signals the investment grade status. Most investment funds and pension funds are not allowed to invest in asset rated below this category, so that falling below investment grade could trigger huge movements in its the price and interest rate. 11 Note that the most frequent initial assigned rating, for developed economies is AAA (68.3%), where the range of first ratings is relatively narrow (from BBB (Greece) to AAA), whereas for EMEs it is B+ (19.2%) with a range that comprises all rating categories (from SD (Ecuador in July 2000) to AAA (Venezuela in October 1977). BANCO DE ESPAÑA 11 DOCUMENTO DE TRABAJO N.º 1428 non-existent. These severe rating variations usually correspond to countries that fall to the category of default from already low ratings, and are massively upgraded once the default is solved. Next, we characterize the main features of what we denominate rating cycle. As for most economic variables, rating cycles can also be described in terms of their duration and amplitude. For illustrative purposes, Figure 3 represents both measures for a hypothetical country X. In this framework, the duration is the number of days from peak to trough and from trough to peak, that is, the downgrade and the upgrade phase, respectively, whereas the amplitude is the number of notches in both periods. We consider that both measures run from the day of the first increase (or decrease) of the rating to the day of the last increase (or decrease). The evolution of the rating of country X represents our a priori assumptions on asymmetries in line with the previous empirical literature. Thus, regarding duration, downgrade periods would be shorter than upgrade periods, which indicates how long does it take to recover the rating. With respect to amplitude asymmetries, at the end of the cycle the rating does not necessarily reach its initial level. To check if S&P ratings fulfill country X rating pattern, Table 2 reports some summary statistics of the rating cycles for a selected country sample, namely the G-20, which represents around 75% of world GDP, as well as a sample of additional developed countries and EMEs. Several conclusions can be raised from this table. First, the countries with at least one complete cycle that is, from trough to peak and from peak to tro
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