LA IMPORTANCIA O CENTRALIDAD DEL OCIO JUVENIL

Overall, without taking upgrades and downgrades into account, it can be seen that Standard and Poor’s is a rating leader in the sovereign credit rating market of African countries (Standard and Poor’s lead Fitch, Moody’s and NKC respectively). In their study, Alsakka and apGwilym (2010a) concluded that Standard and Poor’s were the

most independent of the rating agencies. There is evidence in this study that confirms this aspect for the sovereign credit rating market in Africa.

In the second set of equations, it was seen that Fitch leads the other three rating agencies in terms of upgrades specifically. In the other upgrade causality results; it was shown that Moody’s leads Standard and Poor’s, Standard and Poor’s lead NKC and NKC leads Moody’s (but only in the first two lags of the analysis). There are no clear leaders and followers between the agencies in terms of downgrades. Standard and Poor’s leads Fitch (but only in the first three lags), Moody’s leads NKC (but only in the first lag) and NKC leads Moody’s (but only after the sixth lag). The study by Alsakka and apGwilym (2010a) showed that Standard and Poor’s lead Moody’s in downgrades, but they found that Standard and Poor’s did not only lead Fitch in downgrades but in upgrades as well.

The lead-lag analysis between Fitch and Moody’s shows that Fitch leads Moody’s in upgrades, but not in downgrades. NKC leads Moody’s in upgrades (in the first two lags) and downgrades - after lag six; however, Moody’s leads NKC in the first lag. To an extent, this relates to the findings of Alsakka and apGwilym (2010a). Their results proposed that the smaller (Japenese) agencies included in their research have an insignificant influence on Standard and Poor’s and Fitch’s rating actions, but that the smaller agencies increased Moody’s change probabilities by 14.0 and 18.4 percent, respectively; in the 16 to 90 days and the 91 to 180 days after the rating change (Alsakka and apGwilym, 2010a). In their study, Alsakka and apGwilym (2010a) concluded from their results that Moody’s tends to follow smaller agencies during downgrades. It seems that for African sovereigns, this is the case for upgrades.

5.5 CONCLUSION

The purpose of this chapter was to investigate the lead-lag dynamics between the three major credit rating agencies - Standard and Poor’s, Fitch and Moody’s - and a small research entity, NKC African Economics, among sovereign issuers on the African continent. The monthly rating changes (rating actions) for the four agencies were used for 27 African countries from 2007 to 2015. An ordered probit panel model, similar to the Granger-causality test, was utilised. The first set of models used pairs of

rating agencies, considering the rating actions of each as well as 12 lags in each model. The second set of models divided the rating actions into upgrades and downgrades in order to see if further light could be shed on the dynamics between the rating agencies.

The evidence supports research by Alsakka and apGwilym (2010a) that Standard and Poor’s is the most independent in their rating actions. The results shows that Standard and Poor’s is a rating leader - Moody’s, Fitch and NKC are all followers of Standard and Poor’s - in the sovereign credit rating market of African countries. This only holds when rating changes are taken into account without investigating upgrades and downgrades specifically.

This seems plausible for NKC and Fitch as it was shown that the lowest disagreement ratio between agencies took place between Standard and Poor’s and NKC (with a disagreement rate of 27.4 percent); followed by Standard and Poor’s and Fitch (with a disagreement rate of 33.5 percent). It is quite surprising that the highest disagreement rate was shown between Standard and Poor’s and Moody’s (with a disagreement rate of 44.3 percent). Although Moody’s follows Standard and Poor’s in terms of ratings, there are still major disagreements between the agencies’ ratings. There is further evidence that Moody’s does not only follow Standard and Poor’s, but also Fitch and NKC to a certain extent. This could support the inclination from chapter 3 that there is some underinvestment from Moody’s in African sovereign ratings. Also, seeing that Moody’s is the largest rating agency in the world, their focus on smaller economies remains questionable. This also corresponds to the research by Alsakka and apGwilym (2010a), which concluded that Moody’s tends to follow smaller rating agencies during downgrades.

When upgrade and downgrade actions are taken into account, it can be seen that Fitch leads the other three agencies during upgrades specifically. This is quite an interesting trend, considering that Fitch has the smallest market share of the major three rating agencies (Alsakka and apGwilym, 2010). Other dynamics when upgrades are considered include: Moody’s leads Standard and Poor’s, Standard and Poor’s

leads NKC and NKC leads Moody’s. There is no specific pattern identifiable between leaders and followers when considering downgrade actions.

CHAPTER 6

PROCYCLICALITY AND ASYMMETRY IN AFRICAN SOVEREIGN CREDIT RATINGS

6.1 INTRODUCTION

Credit rating agencies are in a difficult position. On the one hand, they have to ensure rating stability; and, on the other hand, rating accuracy. Considering past trends, it seems that it is nearly impossible to attain both these qualities in the rating assignment process at the same time. Rating stability is normally a by-product of independent ratings or ratings that are formulated without taking the business cycle into account. Ratings that are formulated without taking the business cycle into account are known in the literature as rating ‘through-the-cycle’. ‘Point-in-time’ ratings are the opposite of rating ‘through-the-cycle’. Point-in-time ratings are normally procyclical because they take the business cycle into account. Point-in-time ratings tend to be more accurate than through-the-cycle ratings because they take current economic conditions into account (Topp and Perl, 2010).

Credit rating agencies have been criticised in the past for downgrading (and upgrading) sovereigns during bad times (and good times) in excess of what was really necessary. It was shown in chapter 5 that the number of downgrades by the respective credit rating agencies between African sovereign ratings exceeds the number of upgrades. This trend was ascribed to the existence of possible asymmetries in the sovereign credit ratings market. This asymmetry refers specifically to the divergent behaviour between upgrade and downgrade phases. Asymmetry can, in return, be attributed to procyclicality in the ratings market. According to Dimitrakopoulos and Kolossiatis (2015), ratings are procyclical when credit rating agencies allocate higher ratings than deserved before crises periods and create misleading expectations and thereafter downgrade sovereigns more than what macroeconomic fundamentals prescribe.

The aim of this chapter is to investigate the influence of the business cycle on credit rating levels in order to see if the ratings of respective rating agencies for African sovereigns are through-the-cycle or point-in-time ratings. The purpose of this chapter is also to identify any asymmetries in the credit ratings of African sovereigns. The ratings of NKC African Economics, Standard and Poor’s, Fitch and Moody’s will be used and the determinants identified in chapter 3 will be included. In addition, this chapter will outline the business cycle and the upgrade and downgrade actions of agencies to accommodate for any other factors that could influence sovereign credit ratings. According to Broto and Molina (2014), upgrade phases have not been investigated as extensively as the downgrade phases in sovereign credit ratings literature, making the investigation of the interaction with the upgrade variable in this study insightful.

The chapter is organised into three sections. Section 2 covers the literature review of the procyclicality and asymmetry trends of credit ratings. Section 3 discusses the data and methods used in this study. In Section 4, the estimation results are presented and discussed; and Section 5 concludes the chapter.

6.2 LITERATURE REVIEW

According to Topp and Perl (2010), there exist two ‘philosophies’ when determining the rating of an issuer: one that takes cyclical effects into account (procyclical) and one that does not (independent). When ratings are procyclical, they are also known as ‘point-in-time’ ratings seeing that the current situation of an issuer is taken into account: therefore, cyclical and permanent influences are taken into account (Topp and Perl, 2010). When ratings are independent of the business cycle, it is known as rating ‘through-the-cycle’ - also known as ‘sticky ratings’ in the literature. These ratings are based on issuer-specific characteristics (Freitag, 2015). The focus of the rating through-the-cycle philosophy is on permanent influences, therefore these ratings are not as volatile as point-in-time ratings (Topp and Perl, 2010).

According to Kiff, Kisser and Schumacer (2013); rating agencies are caught between attaining rating stability and rating accuracy. Ratings through-the-cycle are typically more stable, whereas point-in-time ratings are more accurate. Users of credit ratings

abidingly prefer rating stability. This is mainly due to the trading transactions costs that accompany frequent rating changes (Kiff et al., 2013). In the research of Kiff et al. (2013), a structural credit risk model was used to compare the stability and accuracy characteristics of through-the-cycle and point-in-time ratings. Their results proved that through-the-cycle ratings are indeed more stable than point-in-time ratings; however, they are also predisposed to ‘cliff effects and a decreased capacity to forecast defaults in the future. Cliff effects refer to the situation where a specific rating is initially stable, but later exposed to several impulsive downgrades (Kiff et al., 2013).

There are supporters of both philosophies – namely, point-in-time ratings and through- the-cycle methodology - in the literature. The supporters of point-in-time ratings argue that it is better to keep market participants up to speed by taking into account current conditions and adapt credit ratings accordingly (Broto and Molina, 2014). Freitag (2015) has a contrasting view: according to him, credit rating agencies are supposed to have a long-term outlook and should not vary in a procyclical manner and should rather be independent. Research that discuss procyclicality in corporate and sovereign ratings will be discussed next.

The majority of research on procyclicality in the credit rating markets focuses on corporate ratings. Topp and Perl (2010) investigated 7355 firms rated by Standard and Poor’s between 1986 and 2006. They concluded that the ratings investigated did not show the typical characteristics of through-the-cycle ratings. When the specific industry involved is taken into account and the respective rating classes, the agency adjusts the ratings in relation to the business cycle (Topp and Perl, 2010). According to Topp and Perl (2010), rating agencies claim that firms with the same rating position should have the same probability of a downgrade or an upgrade action in the future, irrespective of their rating actions in the past (path dependence). However, as was shown by, for example, Nickell, Perraudin and Varotto (2010) and Lando and Skodeberg, (2002); rating momentum exists. Rating momentum occurs when firms who have undergone several downgrades in the past have an increased likelihood of further downgrades in the future than firms with upgrade histories (Topp and Perl, 2010).

Contrary to the findings of Topp and Perl (2010), Amato and Furfine (2003) argued that there is no sense in assigning ratings procyclically; that is, assigning high ratings to an issuer that is experiencing temporary success. In their research, corporate ratings of Standard and Poor’s were taken into account from the period 1981 to 2001. Ordered probit models were used in this study to show if the business cycle, macroeconomic and financial variables have an influence on credit ratings. It was concluded that when rating agencies do make changes to corporate ratings (which does not occur very often); they overreact to the given circumstances: and the “nature of this overreaction is positively correlated with the state of the aggregate economy and not through-the-cycle” (Amato and Furfine, 2003:12).

Altman and Rijken (2006) investigated the dynamics of through-the-cycle methodology. According to Altman and Rijken (2006), rating stability, rating timeliness and rating performance in predicting defaults are conflicting objectives. Although some investors are of the opinion that ratings are not adjusted in a timely manner (see, for example, research by Ellis (1998) and Baker and Mansi (2002)); investors do require a level of stability when it comes to rating changes. There are many advantages of rating stability. Rating stability prohibits procyclical effects and subsequently does not put extra pressure on financial crises, upholding the reputation of credit rating agencies by avoiding rating reversals over the short-run (Altman and Rijken, 2006). Altman and Rijken (2006) made use of 1629 corporate issuers rated by Standard and Poor’s from January 1981 to July 2002. In their methodology, they made use of proxies (ratings based on credit scores) of point-in-time ratings. Their results show that through-the-cycle methodology delays rating changes, showing that these ratings are more stable. It was also shown that agencies are slightly more responsive during downgrade actions than upgrade actions, which could lead to asymmetric behaviour. It was also proven that the through-the-cycle methodology affects the accuracy of default prediction negatively, making it less accurate (Altman and Rijken, 2006).

Löffler (2005) investigated whether the criticism that agencies receive, with regard to adjusting ratings too slowly, is the outcome of agencies’ aspirations to meet the market’s preference of stable ratings. He makes use of a formal presentation of the rating methodology. Löffler (2005) concludes that rating agencies change ratings seldomly in order to avoid rating reversals. The avoidance of rating reversals can be

more harmful than monitoring credit quality only two times per year. Rating quality, in terms of stability and accuracy, is even more important when considering sovereign credit ratings since sovereign credit ratings create a rating ceiling for corporate ratings.

According to Freitag (2015), there exists limited research on procyclicality and asymmetry of sovereigns because most sovereign rating research is on developed sovereigns and the majority of sovereigns have not experienced substantial downgrades. Kaminsky and Schmukler (2002) found evidence of procyclicality: they found that ratings exacerbate the boom-bust cycle and rating changes mimic the market during good and bad economic times. They made use of the sovereign ratings of Standard and Poor’s, Fitch and Moody’s from 16 emerging countries (none from Africa) between January 1990 and June 2000. Panel regressions were estimated to see the immediate effect of changes of ratings on financial markets. They also used event studies to test the dynamic effects of rating changes on financial markets. Relevant results showed upgrades occurred following market upswings and downgrades took place as markets were already collapsing; indicating a slow reaction from agencies, casting doubt on the effectiveness of ratings.

According to Ferri et al. (1999), if the ratings that are estimated from a model that take economic fundamentals into account are constantly higher than the actual ratings of a sovereign; it could be an indication that the qualitative judgement portion of rating determination might understate the ratings estimated by using economic fundamentals, with the reverse also being true. Actual ratings are a weighted average (with weights generally unknown to the public) of fundamental factors, as well as ad hoc information, that represent agencies qualitative judgements. Therefore credit agencies are using their ‘idiosyncratic judgement’ to adapt ratings estimated by using economic fundamentals (Ferri et al., 1999). This behaviour could lead to procyclical sovereign ratings.

The effect of procyclicality during economic crises is even worse. If credit rating agencies allocate ratings above what economic fundamentals indicate and do not warn investors regarding specific risks before a crisis; agencies would try to excessively downgrade ratings in order to protect their ‘reputation capital’ (Ferri et al.,

1999). Rating agencies effectively worsen an already dire situation by accelerating capital outflows and diminishing further capital inflows in the future (Ferri et al., 1999)

Ferri et al. (1999) found evidence of this trend during and after the East Asian crisis. Higher weights were assigned to qualitative judgements than to economic fundamentals before and after the crisis; leading to a pattern where economic fundamentals were ignored when the economy was booming and deteriorating (Ferri et al., 1999). Ferri et al. (1999) ascribes this procyclical behaviour to ‘reputation incentives that rating agencies face. There is an incentive for rating agencies to become more conservative during economic crises especially, so that they can compensate for the damage that they caused for not being able to predict crises before the event and to reconstruct their own reputational capital (Ferri et al., 1999).

In a similar study to Ferri et al. (1999), which questioned the role of credit rating agencies in the East Asian crisis; Mora (2006) showed that ratings are sticky rather than procyclical. The study made use of the sovereign ratings of 105 countries rated by Moody’s and Standard and Poor’s in 1999. The study simulated specifications by Ferri et al. (1999) and Hu, Kiesel and Perraudin (2002) and improved them by making use of other techniques like ordered probit models and including additional variables like market sentiment. In contrast to Kaminsky and Schmukler (2002), Mora (2006) concluded that it is highly questionable whether ratings really aggravated boom-bust cycles because they were most probably responding to macroeconomic and market news.

Related to the study by Mora in 2006, a recent study by Dimitrakopoulos and Kolossiatis (2015) investigated whether ratings were procyclical or sticky during the period before the East Asian crises, the European debt crises and the period during the crisis. Their research shows that ratings were not procyclical during the crisis mentioned, but rather sticky. According to Norden (2008), ratings are sticky when rating changes only occur a certain period after the market expects; and agencies are thus slow to react.

Another recent study by Freitag (2015) investigated the procyclicality and path dependence of European sovereign credit ratings. According to Freitag (2015:309),

ratings are procyclical if they are ‘positively correlated with economic or credit cycle fluctuations’. Path dependence refers to the situation where prior ratings influence current ratings (Freitag, 2015). Probit models - where either rating changes in terms of upgrades or downgrades or actual rating levels were used as dependent variables - were utilised while controlling for typical macroeconomic variables that are normally taken into account when determining credit ratings (Freitag, 2015). The ratings of Standard and Poor’s, Moody’s and Fitch were all used in the study. It was concluded that there are certain circumstances where rating agencies act procyclically; however, there was no clear pattern visible under these circumstances (Freitag, 2015). It was also shown that there is clear evidence of dynamic elements in rating changes: if a rating change occurs at present, there is an increased probability of a rating change in the future. According to Freitag (2015), this could lead to a vicious cycle where economies in trouble are downgraded further, which leads to increased financial suffering due to increased costs of borrowing. It was proved that credit rating agencies take rating histories into account and ratings are therefore path dependent (Freitag, 2015).