Download Did the recent financial crisis affect credibility of credit rating agencies?

Did the recent financial crisis affect credibility of credit rating agencies?
An empirical research including sovereign ratings.
Finance Master Thesis
Date: 11-12-2013
Name: T.L.A. van Doorne
ANR: S155415
Supervisor 1: Prof. Dr. F.C.J.M. de Jong
Supervisor 2: Drs. J. Grazell
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Abstract
The object of this study is to examine the credibility of credit rating agencies by doing empirical
research on the impact of sovereign rating adjustments. Recent events prior to the subprime crisis
and sovereign debt crisis could have damaged the reputation of credit rating agencies. Next to
negative statements of German Chancellor Angela Merkel and the US Justice Department about
credit rating agencies, papers of for instance Benmelech and Dlugosz (2010) and Hunt (2009)
criticize the whole mechanism the credit rating business is based on.
Results imply a different reaction of investors to sovereign rating adjustments for the periods
before, and since the worldwide financial crisis. Where, in line with related literature, negative
rating adjustments cause a significant rise in yield before the credit crisis, this is not the case for
the period since the start of the credit crisis. For the period 2008 – 2012, credit rating agencies
did not significantly impact government bonds yields. Concluding that a loss of credibility is the
reason for the difference between the two periods however seems far-fetched. That is, the two
periods cannot be compared and the magnitude of events and other variables influencing
government bond yields is hard to control for. The fact that sovereign rating adjustments can get
surpassed by other events (especially in periods of crisis) containing yield sensitive information,
explains mainly the change in impact on bond markets caused by credit rating announcements.
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1. Introduction
John moody created the credit rating business in 1909, when he published the first publicly
available bond ratings about creditworthiness of railroad companies. A rating gives investors
information about the debtors’ ability to pay back the debt and about the debtors’ likelihood of
default. Since inception of the credit rating business, the firms and business evolved over time
and credit rating agencies gained power. An important reason of existence of credit rating
agencies is reducing information asymmetry existing between borrowers and lenders (given the
fact that a rating agency is an independent third-party). They play an essential role in the
financial system, assumed that information asymmetry leads to inefficient investment decisions
(Stiglitz & Weiss, 1981). Standard & Poors formulates its rating business as having the following
goal: ‘help closing the information gap between lenders and borrowers by providing independent
opinions of creditworthiness’. When information asymmetry is reduced, investors are able and
willing to make more confident and considered investment decisions. So credit rating agencies
(attempt to) make markets more efficient and (try to) improve the functioning of markets.
Despite the fact rating agencies contribute to market efficiency, they are widely criticized since
the beginning of the financial crisis. A large number of structured financial products like subprime residential mortgage-backed securities got rated incorrectly. Next to that, rating agencies
were unable to anticipate the sovereign debt crisis. Greece for instance received a single A-rating
till June 2010 and was in the investment-grade category till January 2011, while it had to
restructure its debt in February 2012 (Gaillard, 2013). Furthermore, German chancellor Angela
Merkel and French (ex) president Nicholas Sarkozy criticized the agencies for acting
precipitately in downgrading Eurozone sovereigns, thereby intensifying an impending crisis (Hill
and Faff, 2010). Additionally, in February 2013 Standard and Poors got sued by the US Justice
Department, claiming S&P misled investors by assigning too favorable ratings for mortgage
backed securities.
Taking into consideration the events mentioned above, the research question of this thesis is:
-
‘Did the recent financial crisis affect credibility of credit rating agencies?’
Assuming that reputation of rating agencies can be measured by the reaction of investors to
rating announcements, a reduced reaction to rating adjustments is expected for the period since
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the fall of Lehman Brothers. Next to examining if credibility of credit rating agencies got
damaged the recent few years, the study regarding the effect of sovereign ratings on investment
decisions will be expanded using up to date data. Furthermore the reactions of investors to rating
and outlook adjustments during times of crisis will be observed.
The paper is organized as follows: In the next section relevant literature is reviewed, afterwards
section 3 describes the dataset and methodology. In section 4 the results are presented and
analyzed. Section five concludes.
2. Literature review
Rating agencies have been gaining power since John moody published its first rating. Where
Moody’s started with rating solely the creditworthiness of railroad bonds, nowadays heads of
states attach great importance to ratings assigned to their countries creditworthiness. A decrease
in creditworthiness could logically increase the interest countries/corporates have to pay on its
bond issues, which makes receiving a high rating important to governments/managers. US
president Barack Obama for example said the following on national television on the 29th of July
2011 ‘If we don't come to an agreement [on the debt] we could lose our country's triple-A credit
rating’ (The guardian, 2011). Illustrating that, credit ratings are even influencing decision
making of national governments. 15 years earlier on February 13th 1996, Thomas Friedman a
columnist of the New York Times, made the following statement about the power of rating
agencies during an interview: ‘There are two superpowers in the world today in my opinion.
There's the United States and there's Moody's Bond Rating Service, The United States can
destroy you by dropping bombs, and Moody's can destroy you by downgrading your bonds. And
believe me, it's not clear sometimes who's more powerful’ (Sylla, 2001). This statement
illustrates the power credit ratings agencies obtained during the past century. To understand how
rating agencies got this influential, a closer look has to be taken at the credit rating business.
For decades the credit rating business is dominated by three big players, Moody’s, Standard &
Poors and Fitch. All together they contain an estimated markets share of 95%. One could speak
of the existence of an oligopoly within the credit rating market (Partnoy, 2006). The credit rating
business can roughly be divided in the rating of three different components: Rating the
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creditworthiness of corporates, sovereigns (countries) and financial structured products. Some
reasons for existence and evolution of the three rating components are mentioned below.
As mentioned earlier, the purpose of credit rating agencies is to close the information gap
existing between lenders and borrowers by providing independent ratings. Next to that, Sylla
(2001) mentions the agency theory as one of the explanations for importance of rating corporates.
The principal-agent problem between investors and company managers can be solved by
independently rating issued bonds and by monitoring the issuing company. Without the
continued threat of a downgrade, managers might engage in behavior that improves their own
and/or stockholders positions at the expense of bondholders.
Furthermore, charts containing historical data about corporate defaults show that rating agencies
often link the right rating to the right probability of default. This is illustrated by figure 1 which
shows that, based on historic data, the probability of a rapid default is higher for corporates with
a lower rating. This can give investors’ confidence to rely on the opinion of credit rating
agencies. Additionally, obtaining free information about the creditworthiness of a certain entity
by the use of just one grade expressed by a character seems for an investor very attractive.
*Figure 1, Source: S&P 2011 Annual U.S. Corporate Default Study And Rating Transitions
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The study of White (2010) concludes that the creation of the category ‘nationally recognized
statistical rating organization’ (NRSRO) in 1975 by the US Securities and Exchange
Commission (SEC) gave a large impulse to the credit rating industry. Moody’s, S&P and Fitch
directly received a NRSRO status in 1975. Major investment banks and security firms were,
since that decision, permitted to use credit ratings for certain regulatory purposes (like for
instance determination of capital requirements). The new regulatory rules of the SEC made credit
ratings of central importance in bond markets. Since the NRSRO positions were ascribed to
rating agencies, both bond buyers and bond sellers had to pay more attention to credit ratings.
Credit rating agencies also play a key role in the structured products market. Structured financial
products exist of pooled assets. The claims on the cash flows backed by these pooled assets are
sold in tranches to investors. Examples of structured products are mortgage backed securities
(MBS) and credit default swaps (CDS). Investors rely more heavily on the ratings of structured
financial products than on ratings of corporates and sovereigns, because the complex structure of
structured products makes it hard to compute their riskiness. So investors rely on rating agencies
to do it for them (Fender and Mitchell, 2005). The existence of structured financial products is
good for the credit rating business.
2.1. Causes explaining the current attitude regarding credit rating agencies
Due to the central position rating agencies obtained within the bond and structured product
markets, inaccuracies and errors of the agencies could have a large impact on those markets.
Partnoy mentioned in his study of 1999 that analysts have called the concentration of power at
the two leading rating agencies (Moody’s and S&P) ‘dangerous’. It is possible those analysts
were right as several academics (for instance Benmelech & Dlugosz, 2009; Skreta & Veldkamp,
2009; Ashcraft et al., 2010) and market observers (for instance Roger Lowenstein and Robert
Reich) argue that credit rating agencies were partly responsible for the origination of the current
financial crisis.
Especially the assigned riskiness of a large proportion of structured products causes the current
negative attitude towards credit rating agencies. Before the start of the subprime crisis, 80-95%
of typical subprime or Alt-A mortgage-backed securities received the highest possible triple-A
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rating. Nowadays many of these securities trade significantly below par after experiencing
historically large downgrades and even losses (Ashcraft et al., 2010). By December 2008
structured financial securities accounted for 35% (11 trillion) of the U.S. bond market debt of
which more than half was rated ripple A by Moody’s. 36,346 of these tranches were downgraded
of which nearly one-third bore the AAA rating (Benmelech & Dlugosz, 2009), resulting in large
losses for a wide range of domestic (U.S) and foreign investors who assumed they were invested
in relatively save assets. Apparently investors could no longer rely on rating agencies to
truthfully assess quality of securities (Kelly & Scalet, 2012).
Several academics assume credit rating agencies also played a key role in the development of the
sovereign debt crisis. For instance, country specific credit ratings of Greece have influenced
(next to the spreads of Greek government bonds itself) spreads of countries with weak fiscal
fundamentals like Ireland, Portugal, Italy and Spain (De Santis, 2012). The euro sovereign debt
crisis revealed probabilities of default (given a certain rating) are significantly higher than was
assessed before intensification of the crisis (Table 1).
*Table 1: Source: IMF, (2012) ‘Global Financial Stability Report’
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2.1.1. Critique before the financial crisis
Given the statements mentioned above, nowadays criticism on the credit rating business is more
common in the media and academic papers than before the current financial crisis. However,
before the start of the worldwide financial crisis (considered to be September 15th of 2008, the
fall of Lehman Brothers) criticism on rating agencies already existed. Before the start of the
credit crisis, academics criticism was generally about the increase of instability and volatility that
rating agencies (would) cause.
Reisen et al. (1999) conducted event studies surrounding the Mexican and Asian currency crises.
Their study concluded that up- and downgrades of the tree leading agencies (S&P, Moody’s and
Fitch) significantly affect bond markets for a combination of ratings by the three leading
agencies. So the three agencies jointly have the potential to moderate boom-bust cycles by
leading yield spreads due to revealing new information to the market. That way, financial
markets can anticipate on future (negative or positive) events which results in a reduced
intensity of shocks as investors know what to expect. Euphoric expectations will get damped and
private short-term capital flows reduced (Mckinnon and Pill, 1996). However, this potential has
not been exploited by the agencies. In fact, if rating agencies lag yield spreads (meaning that
rating changes are made after information already is incorporated in the market), they intensify
boom-bust cycles. When rating agencies are lagging the market (and rating adjustments have
significant market impact), upgrades would strengthen positive expectations and stimulate
excessive capital inflows. Vice versa, downgrades lead to excessive capital outflows, driving
sovereign yield spreads up. A reason why agencies are not able to lead the market is for instance
the fact that sovereign-risk ratings are mainly based on publicly-available information. Acquiring
superior information about sovereigns seems challenging. The study of Reisen et al. found
significant impact of rating adjustments, despite the strong anticipation of rating events,
concluding that rating agencies increase volatility and instability.
Loffler (2005) concludes in his study that rating agencies only change credit ratings when a new
rating is unlikely to be reversed shortly afterwards. Subsequently concluding that rating changes
lag changes in default risk of issuers. In line with Reisen and Loffler, Kaminsky and Schmunkler
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(2002) explain that rating agencies provide bad news in bad times and vice versa. This reinforces
the expectation (and reaction) of investors and contributes to instability in financial markets. This
assumption is to be compared to the conclusion of Ferri et al. (1999), who illustrated that the procyclical nature of ratings contributes to exacerbation of financial crises.
It is striking that above statements are contrary to the general assumption of Fama that markets
are assumed to be efficient. If rating agencies are truly lagging the markets as concluded by
academics mentioned above, one could conclude that rating agencies are ‘summarizing’
information that reached the market earlier. Meaning this information most likely already
influenced prices and yield spreads, so rating changes should not contain any additional
information. Probably the reaction of yields to ‘lagged’ rating announcements can be explained
by the fact that several institutional investors have connected their regulatory purposes (like for
instance determination of capital requirements) to opinions of NRSRO’s. This makes them partly
react to rating adjustments instead of ‘actual’ economic news. Von Maltzan and Reisen (1999)
already draw a similar conclusion, saying ‘Impact may be due to prudential regulation and
internal guidelines of institutional investors which debar them from holding securities below
certain rating categories’ (p. 18).
2.1.2. Critique since the financial crisis
Since the recent financial crisis, critique on rating agencies increased and changed. Nowadays,
especially the power rating agencies have to impact the worldwide economy is criticized, as the
whole credit rating mechanism seems to show imperfections. Croce et al. (2011) find results in
their study that confirm the existence rating shopping. Rating shopping means that the issuer
accepts only the rating from the agency that is willing to provide the most favorable rating.
Rating shopping is done by issuers, but the system of the credit rating business makes it possible.
Benmelech and Dlugosz (2010) support the probability of rating shopping by showing that
tranches rated solely by one agency were more likely to be downgraded during 2008. Rating
shopping results in inflated ratings as the issuer only accepts the most favorable rating. The fact
that issuers pay for a rating gives rating agencies an incentive to offer high creditworthiness
levels. This so called issuer-pay model is a matter of great concern about conflicts of interest.
Bongaerts (2013) uses a theory model showing that ratings paid or produced by investors have
less potential to contain rating inflation.
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Hunt (2009) and Rochet et al. (2008) suggest that the whole credit rating mechanism, which is
based on the ‘reputational-capital’ model, does not work. When a specific rating agency
produces high quality ratings, its reputation should positively be affected. This results in higher
future profits (because the amount of rating requests increases) for the rating agency as more
investors value the ratings. So profitability of rating agencies is directly tied to reputation.
However, the lack of competition in the market and the high demand for ratings (also because
the NSRSO’s are of central importance in financial regulation) causes the reputational-capital
mechanism to dysfunction. The lack of competition and high demand for ratings results in a
decreasing incentive to produce high-quality ratings.
2.2. Emperical studies
In this paper the effect of rating/outlook adjustments (about creditworthiness of sovereigns) on
government bonds yields is examined. Like other credit ratings, sovereign ratings are
assessments of the relative likelihood that a borrower will default on its obligations (Cantor and
Packer, 1995). Several papers analyzed the effect of sovereign ratings. Cantor and Packer (1996)
conclude that six factors play an important role determining the rating of a country: Per capita
income, GDP growth, inflation, external debt, level of economic development and default history.
According to Cantor and Packer, rating actions result in bond yield changes that are statistically
significant. This means rating actions independently affect market spreads concluding rating
announcements provide investors with additional information (beyond public data), otherwise
the market would not react. Their study furthermore shows that impact of rating announcements
is larger for below-investment grade sovereigns than for investment-grade sovereigns.
Hill and Faff (2010) use a sample of 101 countries for the period 1990 – 2006. They find an
asymmetric reaction to positive and negative events. Negative rating/outlook changes induce
significant abnormal returns after the rating/outlook is negatively adjusted, positive
rating/outlook changes do not. However, the impact of downgrades decreases when Hill and Faff
exclude periods of crisis from their dataset. Afonso et al. (2012), using a dataset from 1995 to
2010 for the EU countries, also found a significant increase in sovereign yield spreads in case of
a downgrade or negative change in outlook. Next to that, they imply rating announcements know
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spillover effects, especially from countries having a low rating to higher rated countries. A
downgrade of for instance Greece could influence the yield of other ‘related’ countries. De
Santis (2012) agrees and mentions that that rating downgrades of Greece have contributed to
developments in spreads of countries with weaker fundamentals like for instance Ireland,
Portugal, Italy, Spain, Belgium and France.
Many studies, like for instance the study of Nordon and Weber (2004), are about the impact
credit rating agencies have on the financial markets by rating corporates instead of sovereigns.
Often these studies result in similar outcomes as studies examining the effect of sovereign ratings.
Nordon and Weber (using credit rating announcements during the period 2000-2002) also find
significant effects on stock and CDS markets resulting from downgrades. Next to that, they
conclude that the level of old ratings as well as previous rating events significantly influences the
magnitude of the abnormal performance resulting from a rating adjustment.
While rating adjustments generally inform investors about creditworthiness of a specific entity,
Barron et al. (1997) show that not only bonds yields get influenced by ratings. According to their
study, using the UK capital market as dataset, also stock returns react to rating/outlook
adjustments. (Corporate) bond rating downgrades result in significant negative excess stock
returns and (to a lesser extent) positive outlook announcements result in positive excess stock
returns. This means credit rating agencies do not only affect bond and structured products
markets, but also equity markets. The statement indicated above is in line with the findings of
Ederington and Goh (1993). They however highlight that this reaction should not be expected for
all downgrades, because some rating changes are already anticipated by market participants.
Next to that, downgrades resulting from the decision to transfer wealth from bondholders to
stockholders should be positive news for stockholders. The fact that downgrades can already be
anticipated by market participants is proven by Holthausen et al. (1992). Their study on
corporate ratings shows a disappearing average negative excess return for bonds when
observations with a contaminating event are eliminated from the sample. However, average
negative excess returns for stocks remain when using the ‘noncontaminated’ sample.
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A possible explanation for the assumption that upgrades do not cause a significant reaction and
downgrades do is the prospect theory. Investors make irrational decisions since they are more
sensitive to losses than to gains (figure 2), resulting in a utility function that is steeper for losses
than for gains. (Köbberling, Wakker, 2005). That is why, in case of a downgrade, investors sell
their bonds, but in case of an upgrade do not massively buy bonds. A second possible
explanation is the asymmetric reaction of investors to good and bad news. This theory, which has
connections to the prospect theory, suggests that responses to positive and negative information
are asymmetric. Negative information should have a significant larger impact on individual’s
attitudes than positive information, because investors are in principal risk averse (Soroka, 2006).
If investors would make rational decisions, this would probably result in equally sized reactions
to both up and downgrades.
Figure 2. Prospect theory: With a possible loss and gain of the same magnitude, the loss feels greater. Figure
replicated from Shefrin and Statman (1985).
2.3. Conclusion literature review
Next to papers trying to assess the explanatory power of rating adjustments, many academics
focus on criticizing (the role of) credit rating agencies. The empirical/explanatory papers show
that especially negative downgrades result in significant excess returns for bonds and stocks.
This makes it plausible that the results of the empirical research in this study will also show
significant reactions to downgrades. Several academics who criticize the role of rating agencies,
consider rating announcements as lagging the market. This reinforces expectations (and reactions)
of investors, contributing to instability in financial markets. Furthermore the reputational-capital
model where the rating business is based on does not work (anymore) according to several
academics, given the proved presence of rating shopping during the 21st century. The literature
review shows much happened the past few years surrounding the credit rating industry. This
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could lead to some remarkable results in this study regarding the reaction of investors to
rating/outlook changes.
3. Data and methodology
3.1. Dataset
The yields of 10 year government bonds of the 34 countries of the OECD are used for the
empirical research (table 1, appendix). The daily dataset starts at August 30th of 1993 and ends
on December 31st of 2012. This results in 161,472 observations of 10 year government bond
yields. Similar to conducting an event study on stock ‘returns’, in this study also the ‘change’
(return) of the yield is used instead of the yield itself. The end of the day data of the 10 year
government bonds (measured by their yield) are derived from DataStream. For this survey, the
rating/outlook adjustments of credit rating agencies S&P and Fitch are used. For the period
mentioned above (1993-2012) there are 712 sovereign rating/outlook announcements available
for the OECD countries, made by the two rating agencies. Rating/outlook adjustments of foreign
currency ratings/outlooks are used. Foreign and local currency ratings/outlooks often do not
differ. Next to that, investors are more interested in foreign currency ratings
For some of the OECD countries, 10 year government bond yields since 1993 were not available
in DataStream. Because of the insufficient amount of 10 year government bonds yields,
especially in the first years of the dataset, the effect of some rating adjustments cannot be
examined. For Estonia no data were available at all, so the effects of rating/outlook adjustments
on Estonia’s government bonds are not considered in this study. However, the majority of the
OECD-countries are present in the dataset since the year 1993. An overview of the availability of
data for each country is to be found in the appendix (Appendix, table 1). All ratings that can be
used because of available yield data are used. This leaves a dataset as described in table 2.
In total, 712 rating/outlook announcements are available in the dataset of which 256 do not
announce a change in rating/outlook, they just confirm the rating/outlook assigned before. 140 of
all announcements are upgrades, 124 are downgrades, 94 times the outlook changes positively
and 98 times the outlook changes negatively. Only announcements showing a real change in
rating/outlook are used in the event study.
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Upgrades
Data availability
Downgrades
Data availability
Positive outlook change
Data availability
Negative outlook change
Data availability
Total
Total
140
83
124
95
94
54
98
71
456
Before 15-9-2008
125
75
44
19
77
40
46
25
292
After 15-09-2008
15
8
80
76
17
14
52
46
164
*Table 2 presents the division of ratings in the four possible outputs
Out of all announcements within the dataset, 513 took place before the fall of Lehman Brothers
and 199 after the fall of Lehman (reasonable, given the lengths of the periods in the dataset
before and after the fall of Lehman). However, when looking at real changes, 292 real changes to
ratings/outlooks are made before the start of de subprime crisis, where 164 real changes are made
after inception of the subprime crisis. Therefore relatively more real changes to ratings/outlooks
are made during times of crisis (ratio 0.43 before the subprime crisis vs. 0.82 since the fall of
Lehman). It is striking that there are more upgrades than positive outlook changes and more
downgrades than negative outlook changes, as one could presume credit rating agencies would
‘warn’ investors first by adjusting the outlook. However, apparently agencies often directly
decide to up or downgrade, instead of first changing the outlook.
Comparing the magnitude of downgrades before and after inception of the financial crisis,
expectations arise that downgrades since the fall of Lehman have affected bonds yields more
than they did before the fall of Lehman. Since inception of the worldwide crisis, on average the
difference in rating before and after a rating adjustment has been larger. So the rating agencies
made bigger steps between ratings. When a downgrade occurred before the fall of Lehman, on
average the rating dropped by 1.05 notches. Since inception of the subprime crisis, on average
ratings dropped by 1.48 notches (only announcements showing a real rating adjustment and of
which DataStream has yield information available are considered).
3.2. Methodology
The standard event study methodology is used to analyze the response of government yields to
sovereign credit ratings. To reduce contamination problems only small 3- (-1,1) and 5-day (-2,2)
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event windows are used. When using a larger window other factors are possibly included in the
window and could affect the results.
An event study is used to study the possible occurrence of abnormal differences surrounding a
particular event. To check if the observed data are indeed abnormal, they are compared to normal
data. These normal data are computed using the yield changes of the specific country for the
period before (or surrounding) the event and the relation of that yield to a benchmark which is
not affected by the particular event. For developing a model that calculates normal yields,
countries that do not have an adjusted rating during the period can be used as control group, as
they should show normal returns. Next to that Longstaff et al. (2011) show that sovereign yields
in general are high correlated. German government bonds yields seem appropriate for calculating
normal yields as Germany keeps an AAA status during the whole dataset window (1993 – 2012).
Choosing the right model for calculating normal returns is essential. The predicted returns for
each OECD- country are calculated by running a separate regression between German bond
yields and country individual yields. The intercept (alpha) and coefficient (beta) computed by the
regressions are used for predicting normal returns (Hand et Al., 1992). A moving estimation
window of 200 days (-100,100) is used here to calculate the intercept and coefficient. A moving
estimation window means that periods before and after the event are used. The estimation
window typically is the period prior to the event (often 120 days, according to MacKinlay, 1997).
However, several papers mention that rating adjustments result from several events happening
prior to rating adjustments (e.g. Reisen et al., 1999; Loffler, 2005). So a larger estimation
window including also the period after the event is used to diminish the influence of these events
on the intercept and coefficient. To check if an estimation window of 200 results in reliable
alphas and betas, the estimation window was enlarged to 400 days (-200,200), this however did
not result in significant different results for the intercept and coefficient. The model generated
return Rit (1) depends on the return of the market portfolio Rge, which is represented here by the
(change in) yield of ten-year German government bonds.
(
)
(
)
(1)
Afterwards, the daily abnormal return is calculated by deducting the predicted normal yield
change from the actual yield change for each day. A T-test (2) is used to check if the actual yield
change (AR) differs significantly from the computed normal yield change (R).
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(
)
(
) √
(2)
4. Results
4.1. Main results
The effect of rating/outlook adjustments will be conducted in this part of the paper. Table 4
shows the first results. Event studies are conducted for all upgrades, downgrades, positive
outlook adjustments and negative outlook adjustments. The studies are executed for event
windows of 3 and 5 days. There is made a distinction in the dataset between events before
inception of the credit crisis and after inception of the credit crisis.
Table 4
Yield changes by rating/outlook adjustments
Period
Upgrades
(-1,1)
(-2,2)
Before fall of Lehman -0.001 (-0.63) -0.010 (-1.20)
After fall of Lehman
-0.084 (-1.10) -0.090 (-1.08)
Total period
-0.009 (-1.18) -0.010 (-1.23)
Downgrades
(-1,1)
0.014*** (3.57)
-0.006 (-0.57)
-0.002 (-0.22)
(-2,2)
0.009 (1.63)
-0.004 (-0.39)
-0.002 (-0.18)
Period
Positive outlook adjustments
Negative outlook adjustments
(-1,1)
(-2,2)
(-1,1)
(-2,2)
Before fall of Lehman -0.003 (-1.29) -0.001 (-0.43)
0.002 (0.47)
-0.000 (-0.06)
After fall of Lehman
0.008* (1.76) 0.007 (1.09)
0.014 (1.23)
0.005 (0.47)
Total period
-0.001 (-0.21) 0.001 (0.38)
0.010 (1.30)
0.003 (0.43)
Note: Associated T-statistics are reported in brackets behind the coëficient. ***,**,*
means significance at 1%, 5%, 10% level respectively.
One could expect upgrades to result in a significant drop in yield. When issuing the bond in the
past, the height of the interest rate is determined by the risk an investor will incur. So the interest
rate/coupon payments more or less match the rating of the issuing country. When the rating goes
up, this means the investor is receiving coupon payments that actually are too high for the (new)
sovereign rating, so the government is overpaying its bondholders for the risk carried. According
to the rating agencies the (default) risk has dropped. This should result in a higher bond price as
the bond gets more attractive (same coupon payments, less risk), which should result in a lower
yield (to maturity) (3).
(3)
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C identifies the coupon payment, F the face value of the bond, P the current price and n gives the
amount of years (or amount of coupon payments) to maturity. According to this function, a
higher bond price should decrease a bonds yield to maturity1. However, the event study does not
show a significant result. Nor for the period before the fall of Lehman (-0.63 significance level),
neither for the period since the fall of Lehman (-1.10 significance level), concluding that
upgrades on average did not substantially affect the yield of government bonds for OECD
countries (1993 – 2012).
In contrast to the upgrade results, the downgrades (before the fall of Lehman) show a significant
coefficient of 0.014 (3.57). When a rating agency downgrades a certain country, this results in a
significant rise in the yield of that specific country. This means the price of the bond will drop,
because investors conclude they get undercompensated for the risk they are taking. However, the
impact of a downgrade on the bond market disappears when calculating the effect of a
downgrade since inception of the credit crisis (Coefficient -0.006 (-0.57)). Did times change
since the beginning of the credit crisis? The expected result of this event study was a larger
positive coefficient compared to the reaction of yields to downgrades before the credit crisis.
This expectation is caused by the magnitude of downgrades since the fall of Lehman. Although
rating agencies took bigger leaps the last years (caused by amongst others the situations of
Greece, Iceland, Ireland, Portugal and Spain), this did not result in a larger positive coefficient; it
did result in a coefficient that is neither significant nor positive at all.
Previous studies in literature (Afonso et al., 2012; Hill & Faff, 2010; Nordon & Weber, 2004)
found comparable results to the results presented in table 3 for the period before the fall of
Lehman. Positive rating adjustments do not result in significant impact on financial markets
while negative adjustments do. Afonso et al. used a dataset (existing of 24 EU countries for the
period 1995-2010) fairly comparable to the one used in this paper.
1
The yield to maturity function actually is an approximate YTM function. To find the exact yield to maturity a trial
and error method needs to be used.
(
)
(
)
The exact yield to maturity (here denoted as r) is found once the calculated bond price matches the actual bond price.
17
Furthermore the change in results caused by the size of the event window is in line with the study
Barron et al. (1997) conducted. Their paper shows a decreasing T-value when adding more days
to the window, in case of calculating the effect of rating adjustments on stock returns. This is in
line with (the semi-strong version of) the efficient market hypothesis proposed by Fama in 1970.
Markets are assumed to be efficient, meaning that prices reflect public information and rapidly
react to it.
4.2. Period since fall of Lehman Brothers
The insignificant reaction of investors to a downgrade since inception of the financial crisis is the
most unexpected result of the event study conducted above. Hill and Faff (2010) found less
significant results when excluding periods of crisis from their experiment. In line with the results
of Hill and Faff the expectation for this study was that coefficients and significance levels would
be larger for the period since the fall of Lehman. However, the period since 15 September 2008,
including a subprime- and sovereign debt crisis, does not show results as expected. When
splitting up the period since the fall of Lehman in separate years, defining an explanation for the
insignificant result of this specific event study could become possible. Table 5 shows the effect
of downgrades for the separate years since inception of the credit crisis.
Table 5
Downgrades by year since fall of Lehman Brothers
Year(s)
#
(-1,1)
(-2,2)
2008
6
-0.071*** (-2.63)
-0.125* (-1.68)
2009
14
0.021*** (2.67)
0.030*** (3.33)
2010
12
0.023* (1.82)
0.020* (1.85)
2011
26
0.013 (1.58)
0.024* (1.90)
2012
18
-0.051 (-1.46)
-0.047* (-1.82)
Note: Associated T-statistics are reported in brackets
behind the coëficient. ***,**,* means significance at 1%,
5%, 10% level respectively.
The years 2009 – 2011 show reasonable reactions to downgrades. One explanation for the
negative effect in 2008 could be the low correlation between stocks and bonds over the short
term. When volatility on the stock market increases substantially and during days when stock
turnover is unexpectedly high, bond returns tend to be high (Bansal et al., 2010). A related theory
to this is called ‘flight to safety/quality’, meaning investors will invest their money in safer assets
like bonds. A flight to safety (period) is defined by Baele et al. (2013) as ‘being a period of
18
market stress (high equity market volatility), entailing large and positive bond returns, large
negative equity returns and a high-frequency correlation between bond and stock markets’(p. 2).
Figure 4 shows a decreasing market capitalization for the global equity market, which probably
is the result of investors taking away capital from the stock market during the period in advance
of and after the fall of Lehman Brothers. This could possibly be a flight-to-safety period. Baele et
al. identify large market crashes as flight to safety episodes, also the Lehman bankruptcy incident
is classified by their methodology as being a flight to safety period. In their paper they also
illustrate that in flight-to-safety periods on average nominal government bond yields decline,
which could explain the significant negative yield change during 2008. Because of the fact that
the 6 downgrade observations all occur in a period nearby October 15th 2008, it could be the
sovereign yields were on average decreasing (due to rising bond prices) as a consequence of the
growing amount of capital which got invested in government bonds.
Figure 4, data source: Bloomberg
A second explanation for the negative coefficients for the year 2008 occurs when looking at all
individual downgrades. Two observations, both Icelandic downgrades, show a relatively larger
decrease in yield than the other observations in the year 2008. The yield of Iceland dropped in
three days from 8.95 to 8.08 surrounding the downgrade of Fitch on 30 September 2008. A
second downgrade of Iceland, this time by S&P, on October 6th of 2008 was accompanied
(window -1,1) with a drop in yield from 8.46 to 7.82. For both downgrades one would expect a
19
positive yield change. Those two downgrades probably have a significant impact on the t-test
and are results of the Icelandic financial crisis. The fact that both downgrades are accompanied
by a decrease in yield has to do with the fact that Iceland was trying to peg the Icelandic Krona
to the euro at that moment in time. This resulted in investors buying government bonds and
Icelandic Kronas to profit from the predicted favorable fixed rate that would result from pegging
the Krona to the Euro, resulting in a decreasing yield. When looking at figure 1 - 7 (appendix), it
is obvious that yield of Iceland and other European countries knew very high volatility during
the years 2008-2011 compared to for instance the Netherlands. Looking at the Icelandic example,
one could carefully conclude that the effect of rating announcements in times of crisis and high
volatility faiths away, as if they are subordinate to a range of events/news items which are valued
as being more important by investors.
In relation to the unexpected reaction of Icelandic yields to rating adjustments, different studies
indicated rating agencies are lagging rather than leading the market (Reisen et al., 1999), in other
words, ratings are reactive rather than preventive (Larrain et al., 1997). It is possible that the
Icelandic yield did already adjust to a news feed prior to the downgrade and is now reacting to
another market impulse, considered by investors as being more important than a rating
adjustment. It seems like the rating adjustments of S&P and Fitch get ‘overruled’ by other events.
To check if the expected reaction to a downgrade occurs for countries experiencing relatively
less volatility during the financial crisis, the countries that were in financial distress during the
last 5 years are left out the analysis now (table 6).
Table 6
Downgrades since fall of Lehman Brothers
Year(s)
#
(-1,1)
(-2,2)
Sample 1 26
-0.005 (-0.65)
-0.004 (-0.43)
Sample 2 22
-0.003 (-0.36)
-0.008 (-0.74)
Sample 3 17
-0.002 (-0.25)
-0.011 (-0.85)
Note: Associated T-statistics are reported in brackets
behind the coëficient. ***,**,* means significance at 1%,
5%, 10% level respectively.
In sample 1, the following countries are omitted out of the OECD-group for the experiment:
Greece, Iceland, Ireland, Portugal and Spain. However, this still does not result in significant
coefficients. Adding Italy (sample 2) and Hungary (sample 3) to the group of left outs does not
20
result in substantial changes either. Apparently the ‘healthy’ countries are less sensitive to
downgrades during times of worldwide financial distress. The worldwide financial situation
seems to influence the reaction to rating adjustments (at least in case of downgrades).
Where it was possible to find an explanation for the negative coefficient for the year 2008, this is
not the case for the year 2012. The year 2012 counts 18 observations of which 11 result in a
negative coefficient. Next to that, downgrades during the years 2010 and 2011 do not result in
convincing t-values (both confidence level 90%) compared to the period before the fall of
Lehman Brothers. Altogether, considering that times are changed seems premature. However, it
seems possible to conclude that in times of crisis, which are associated with large quantities of
yield sensitive information, rating adjustments seem to lose their effect partially.
4.3. Robustness checks
4.3.1. Placebo test
When describing the data it was mentioned 256 announcements just confirm the rating/outlook
assigned before and do not announce a change in rating/outlook. On could assume confirmations
not to affect government bond yields. To check if this assumption holds, an event study is
conducted using these ‘confirmation’ rating announcements. Table 7 shows the results.
Table 7
Yield changes due to confirmation announcements
Period
(-1,1)
(-2,2)
Before fall of Lehman 0.001 (0.84)
0.001 (0.72)
After fall of Lehman
-0.016** (-2.02)
-0.024** (-2.08)
Total period
-0.003 (-1.09)
-0.004 (-1.32)
Note: Associated T-statistics are reported in brackets behind the
coëficient. ***,**,* means significance at 1%, 5%, 10% level
respectively.
Where the period before the fall of Lehman brothers shows results as expected, the period since
the fall of Lehman shows unexpected results. When a rating (outlook) is confirmed by a rating
agency, one could expect this rating announcement as being already anticipated and processed by
the market. However, in the period since the fall of Lehman a rating confirmation results in a
rising bond price and a decreasing yield. Using the available information, this can be explained
as followed:
21
-
Confirming a credit rating in times of crisis and financial distress can be interpreted as a
positive event. It means there is no deterioration of the current situation, where this could
have been the expectation. Next to that, the sample of the event study exists of 29 events
(actually 35, but caused by the limited availability of some countries, 29 observations are
useful). Of these 29 observations, 15 ratings consider PIIGS-countries2, which makes this
explanation more plausible. Boot et al. (2006) explain in their study a positive stock price
reaction can be expected when a rating confirmation occurs after a ‘credit watch
procedure’. When a firm or country receives the credit watch signal of rating agencies,
this means they can expect a downgrade if they do not take measures to increase their
creditworthiness. Although most likely not all 29 observations were under credit watch, a
situation of financial distress could perhaps be compared to a credit watch period.
-
Credit watch procedures are not included in this event study, while they could influence
yield reactions to rating confirmations.
4.3.2. Event Window
Although the paper of Barron et al. (1997) shows a diminishing effect in the stock market when
enlarging the event window, several papers (and the fact that the government bond market is
studied) motivate to enlarge the event window. For instance the paper of Sadka (2006) about post
earnings announcement drift, which argues that investors tend to underreact to earnings
information, suggests using a larger event window. It is possible that the market needs more time
to adjust the yield to the new default rating of a certain country. However, when enlarging the
event window for downgrades that occurred before and after inception of the credit crisis (Table
8), Stata shows results comparable to the findings of Barron et al.
Results get insignificant when enlarging the event window and some coefficients are negative
implying an unexpected yield reaction in case of a downgrade. A larger event window will show
different results and coefficients, but results in noisy event study. Finding out if rating
adjustments know a post-announcement drift seems difficult when not controlling for all other
2
PIIGS – countries are 5 European countries (Portugal, Italy, Ireland, Greece and Spain) which are considered to be
economically weaker than other European countries following the financial crisis.
22
Table 8
Various event windows
Window
Size Before 15-09-2008
After 15-09-2008
(-1,1)
3
0.014*** (3.57)
-0.005 (-0.57)
(-5,5)
11
-0.00 (-0.52)
-0.004 (-0.33)
(-10,10)
21
-0.031 (-0.99)
0.005 (0.29)
(-20,20)
41
-0.044 (-1.32)
0.020 (0.84)
(0,10)
11
-0.001 (-0.10)
-0.005 (-0.30)
(0,20)
21
-0.012 (-0.88)
0.007 (0.28)
Note: Associated T-statistics are reported in brackets
behind the coëficient. ***,**,* means significance at 1%,
5%, 10% level respectively.
factors that could possibly affect bond-yield and are now influencing the experiment too.
So the event window is kept small. That way only the effect of the ratings change is captured.
Although a larger event window results in a biased event study, it is still interesting to see if the
change to yield resulting from a downgrade takes place before or after the announcement. In
other words, is the change of yield caused by a pre- or post-announcement drift? Table 9 shows
the results of event studies conducted for upgrades, downgrades, positive outlook adjustments
and negative outlook adjustments. The event studies are executed for event windows of (-2,0), (1,0), (0,1) and (0,2).
Again, and in line with table 4, downgrades before the fall of Lehman result in significant effects
on the yield of government bonds. Next to that, negative outlook adjustments show some
positive significant results. Also 2 significant coefficients occur resulting from a positive outlook
adjustment; those results however do not match with the theory as they are positive. Since there
is not a clear pattern of when a result is significant or not, there is not a real distinction to be
made between changes in yield occurred before and after an announcement. Although it seems
like reactions to a negative outlook adjustment only occur after the announcement (often at 90%
confidence level).
However, drawing a conclusion about for instance the post announcement drift being larger than
the pre-announcement drift seems not possible.
23
Table 9
Yield changes by rating/outlook adjustments
Period
Upgrades
(-2,0)
(-1,0)
Before fall of Lehman -0.001 (-0.87) -0.000 (-0.02)
After fall of Lehman
-0.085 (-1.09) -0.077 (-1.08)
Total period
-0.009 (-1.20) -0.007 (-1.06)
(0,1)
-0.000 (-0.43)
-0.007 (-0.92)
-0.011 (-0.91)
(0,2)
-0.000 (-0.02)
-0.005 (-0.59)
-0.001 (-0.39)
Positive outlook adjustment
-0.003 (-1.42) -0.003 (-1.51)
0.010** (2.40) 0.007** (2.46)
0.000 (0.07)
-0.001 (-0.31)
-0.001 (-0.76)
0.004 (0.91)
0.000 (0.02)
0.001 (0.93)
0.001 (0.14)
0.001 (0.033)
Downgrades
(-2,0)
0.002 (0.64)
0.003 (0.48)
0.003 (0.59)
(0,1)
0.014** (2.56)
-0.005 (-0.55)
-0.001 (-0.18)
(0,2)
0.014** (2.52)
-0.010 (-0.82)
-0.005 (-0.53)
Before fall of Lehman
After fall of Lehman
Total period
Period
Before fall of Lehman
After fall of Lehman
Total period
(-1,0)
0.007** (2.41)
-0.003 (-0.86)
-0.001 (-0.38)
Negative outlook adjustment
Before fall of Lehman -0.001 (-0.23) -0.002 (-0.50)
0.007** (2.17) 0.004 (1.23)
After fall of Lehman
0.016 (-1.33) -0.008 (-0.69)
0.017 (1.44)
0.016* (1.76)
Total period
-0.011 (-1.35) -0.006 (-0.76)
0.013* (1.74)
0.012* (1.95)
Note: Associated T-statistics are reported in brackets behind the coëficient. ***,**,*
means significance at 1%, 5%, 10% level respectively.
4.3.3. Robustness check
Instead of predicting normal returns using an intercept and coefficient related to the German
bond yield changes, one could also just compare the bond yield changes of country i to the yield
changes of Germany (being the unaffected benchmark). This results in the following model:
(4)
The expectation is that, in case of an up- or downgrade of country i, bond returns of Germany
and country i will differ more compared to periods without rating adjustments. This model finds
results fairly comparable to results presented earlier (table 10). Again only downgrades prior to
the fall of Lehman Brothers show significant reactions. It is striking that all upgrades show a
negative coefficient and all downgrades show a positive coefficient, where this was not the case
using the actual model. Spillover effects, the flights to safety model of Baele et al. (2013) and the
fact that in both models Germany is an important determinant for calculating respectively the
expected (normal) return and the abnormal return, makes it reasonable to use a model without
Germany playing such a central role.
24
Table 10
Yield changes by rating adjustments
Period
Upgrades
Downgrades
(-1,1)
(-2,2)
(-1,1)
(-2,2)
Before fall of Lehman -0.001 (-0.80)
-0.002 (-0.74)
0.010** (2.13)
0.002 (0.21)
After fall of Lehman
-0.076 (-0.98)
-0.081 (-0.93)
0.002 (0.20)
0.002 (0.21)
Total period
-0.009 (-1.10)
-0.010 (-1.08)
0.004 (0.42)
0.003 (0.33)
Note: Associated T-statistics are reported in brackets behind the coëficient. ***,**,* means
significance at 1%, 5%, 10% level respectively.
It is possible that in case of a downgrade of country i, the bond price of Germany is affected
positively as investors make the switch to safer assets and vice versa. This could positively affect
the results of the study and result in higher coefficients. To check the robustness of both models,
an event study is conducted using the (unweighted) average yield changes of all OECD countries
as benchmark (5).
(5)
Results are presented in table 11. None of the results creates uncertainty about models used
earlier, as in line with other conducted event studies only downgrades before the fall of Lehman
are significant.
Table 11
Yield changes by rating adjustments
Period
Upgrades
Downgrades
(-1,1)
(-2,2)
(-1,1)
(-2,2)
Before fall of Lehman -0.002 (-1.27)
-0.002 (-0.96)
0.013*** (3.49) 0.010** (2.08)
After fall of Lehman
-0.057 (-1.20)
-0.063 (-1.22)
-0.002 (-0.21)
-0.001 (-0.09)
Total period
-0.008 (-1.44)
-0.009 (-1.41)
0.001 (0.20)
0.001 (0.17)
Note: Associated T-statistics are reported in brackets behind the coëficient. ***,**,* means
significance at 1%, 5%, 10% level respectively.
4.4. Effect by rating level
The results presented so far indicate that only downgrades have significant impact on bond
markets. Up to this moment all up- and downgrades were pooled together to find one coefficient.
However, it is unlikely that each up/downgrade has the same impact as other up/downgrades. A
downgrade from AAA to AA+ could have a different impact on yield than a downgrade from
BB+ to BB for instance. Also the fact that an upgrade does not show a significant (negative)
effect on a bonds yield, as established earlier, does not mean that every upgrade indeed has no
significant effect on yield. Next, to examine the effect of a rating change on a certain level,
25
event studies will be conducted for all up/downgrades pooled together by rating level. Especially
ratings surrounding de border of (BBB-,BB+) are expected to show significant impact on bond
yields. The two ratings are defined as followed by Standard and Poors:
- BBB-:
Considered being the lowest investment grade by market participants.
- BB+ :
Considered being the highest speculative grade by market participants.
The reason for expecting high significance levels at this ‘investment grade boundary’ is caused
by the fact that many pension funds, insurers and other institutional investors have policies that
require their portfolio managers to limit the investments in bonds to investment-grade issues
(Cantor & Packer., 1997). So downgrading a sovereign credit rating to the speculative category
would probably cause an outflow of a substantial amount of funds. Vice versa, an upgrade to the
investment-grade class could result in a substantial inflow of funds from institutional investors.
Results from the event study described above are presented in table 12. When interpreting the
results, one should take into consideration that some samples got quite small due to pooling the
rating adjustments together by rating level. This could result in less reliable results.
Upgrades result predominantly in negative coefficients and downgrades predominantly in
positive coefficients. As expected, the category ‘downgrades’ shows more significant results
than the upgrade class. However, the upgrade class is showing some significant results
(confidence level 90%) whereas till now, the upgrade class did not generate significant reactions
on the bond market at all. Some of the results in table 12 require further attention and
explanation:
-
The upgrade to the BBB- rank results in a significant negative reaction to the yield. BBBis the direct border between investment and speculative grade. One could conclude that
the significant coefficient is the result of the inflow of funds coming from institutional
investors. Although there are only two observations of a positive rating adjustment to the
BBB- level, this can be perceived as small evidence that an upgrade to the investment
grade class has a significant impact on a countries government bond yield. Especially
because other upgrades do not show significant impact (apart from the upgrade to A-)
26
-
The downgrade to the A- class shows a strange negative significant result. Probably this
is partly caused by the two downgrades (by S&P and Fitch) of Iceland to the A- class,
mentioned earlier, which are in this event study.
-
The downgrade from investment grade to speculative grade BB+ does not show a
substantial impact on yield. Despite the fact that the coefficient resulting from this event
study is larger than the coefficient of downgrades to higher classes, it is not possible to
conclude that the increase in yield is significant.
Table 12
Yield change per rating adjustment
Rating
Upgrades
Downgrades
#
(-1.1)
(-2.2)
#
(-1.1)
(-2.2)
AAA
13
-0.017 (-0.52)
-0.036 (-1.24)
0
AA+
12
0.003 (0.99)
0.003 (0.87)
8
0.007 (0.57)
-0.007 (-0.30)
AA
11
-0.002 (-0.90)
-0.005 (-1.27)
8
0.014 (1.11)
0.018 (1.11)
AA3
-0.012 (-0.58)
-0.0165 (-0.73)
16
0.009* (1.79)
0.013* (1.86)
A+
9
-0.000 (-0.05)
0.008 (1.35)
8
0.017** (2.28)
0.011 (0.94)
A
10
-0.002 (-0.49)
-0.002 (-0.48)
8
-0.010 (-0.66)
-0.016 (-0.69)
A11
-0.0018 (-0.58)
-0.011* (-1.91)
9
-0.043** (-2.02) -0.043* (-1.77)
BBB+
8
-0.0012 (-0.18)
0.010 (1.18)
8
0.016 (0.89)
0.014 (0.61)
BBB
2
0.001 (0.07)
-0.005 (-0.24)
8
-0.010 (-0.61)
-0.042 (-0.69)
BBB2
-0.013* (-1.87)
-0.018 (-1.05)
5
0.005 (0.27)
0.030 (1.37)
BB+
0
6
0.044 (1.44)
0.042 (1.30)
BB
0
1
0.083 (0.67)
0.093 (0.76)
BB0
1
0.014*** (4.26)
0.019*** (3.37)
B+
0
1
0.065** (2.38)
0.061 (1.49)
B
0
1
-0.022 (-1.37)
-0.019 (-1.03)
B1
-0.614 (-1.03)
-0.668 (-1.14)
0
CCC+
0
0
CCC
1
0.004 (0.84)
0.002 (0.12)
3
0.008 (0.64)
0.043* (1.67)
CCC0
0
CC+
0
0
CC
0
1
-0.029*** (-5.13) -0.042*** (-4.22)
c
0
1
0.014 (0.34)
0.003 (0.07)
SD/RD
0
2
-0.297 (-0.94)
-0.194 (-0.89)
Total
83
95
Note: Associated T-statistics are reported in brackets behind the coëficient. ***,**,* means significance at
1%, 5%, 10% level respectively. Due to the fact OECD-countries are generally investment grade countries,
there are not many results for up- or downgrades lower than BB+. Only Greece and Turkey got far under the
investment grade boundary. However, there were no yield-data available for the period Turkey got speculative
grade ratings, so all ratings below BB are applicable to Greece. Rating agencies changed ratings to certain
speculative grade levels at most once, which doesn't result in reliable coefficients. Therefore those results
should not be considered.
-
The fact that up- or downgrades of Greece do not always show significant results can
probably be explained by the fact that rating agencies are lagging the market as
27
mentioned earlier. Probably the yield already adjusted to the situation of Greece as a
consequence of news announcements that reached the market earlier3. Furthermore, the
yield of Greece on January 1st of 2012 amounts 37.259 and on the 31st of December 2012
the yield amounts 11.472, implying that the yield of Greece knew excessive volatility
during the year 2012.
Rating adjustments did barely result in (reasonable) outcomes for Greece (and Iceland, discussed
earlier) during times of crisis, concluding that rating agencies are playing a less significant role
in providing investment information during times of financial distress. It is possible the agencies
still have impact during periods of crisis, but this impact seems to get surpassed by the presence
of other events. One could control for the crisis situation of a country to perceive what impact is
caused by rating agencies, this however seems difficult assuming that different crisis situations
are hard to compare and default risk is not the only factor determining changes in bond yields.
4.5. Other determinants of yield
De Santis (2012) mentions in his paper several (other) determinants of government bond yields.
According to his findings, one could divide those factors into three main categories:
1. Aggregate risk: consisting of monetary policy, global uncertainty and risk aversion.
2. Country specific risk: Consisting of default risk, funding risk and liquidity risk.
3. Contagion risk: Identified by the possibility that economic instability of a specific
country is transmitted to other countries.
In times of financial distress all factors are constantly present and influencing the yield of
government bonds. For all 7 factors De Santis finds empirical evidence of them significantly
affecting the yield, although liquidity and funding risk play a marginal role in determining bond
yields.
3
The following news item of S&P is an example of a rating announcement, subsequently to a prior press release:
‘LONDON (Standard & Poor's) May 2, 2012 - Standard & Poor's Ratings Services today raised its long-term local
and foreign currency sovereign credit ratings on the Hellenic Republic (Greece) to 'CCC' from 'SD' (selective
default) The rating action reflects the completion on April 25, 2012, of Greece's distressed debt exchange’.
28
With all seven factors determining bond yields, the influence of credit rating agencies seems
slightly overestimated as for example upgrades do not even have a significant effect on yields.
Next to that, other risk determinants like global uncertainty and risk aversion are present at all
time and their exact impact seems challenging to measure and is hard to control for. This does
not mean default risk does not have a large impact on sovereign yields, however it implies that
default risk measured by credit ratings does perhaps have less impact as expected. When
comparing the effect of an upgrade to monetary decisions, the effect of rating agencies seems
rather small. In table 13 the effect of 13 rather influential monetary policy decisions on sovereign
yield is examined (the 13 events are described in the appendix, table 2). Comparable to upgrades,
decisions were picked which could have a positive impact on a countries bond price, meaning a
negative effect on its yield. An event study is conducted using the announcement dates of the
monetary decisions.
Table 13
Positive effects of monetary policy
#
(-1,1)
(-2,2)
13
-0,047** (-2,30)
-0,042* (-1,88)
Note: Associated T-statistics are reported in brackets
behind the coëficient. ***,**,* means significance at 1%, 5%,
10% level respectively.
Where upgrades do not significantly impact credit ratings, monetary decisions clearly do. Next to
that, the coefficient in case of a ‘positive’ monetary decision seems quite large, also compared to
the impact of downgrades on sovereign yields. The coefficient implies that the yield changes by
– 4.7% more than it would do in case of no announcement. Of course one should take into
consideration that these announcements of for instance the ECB were done during times of
financial distress and were important during the sovereign debt crisis.
However, rating upgrades during this period were done during the same period as these
announcements and did not result in any significant yield changes. Next to that, monetary policy
has direct impact on the economy, where rating agencies just provide information leaving
investors the decision to react on it or not.
29
5. Conclusion
Rating agencies have been gaining power during the past century. Where the central position of
rating agencies was criticized before the recent financial crisis and the concentration of power at
the two leading rating agencies (Moody’s and S&P) was even called ‘dangerous’, the
intensification of the recent financial crisis confirmed those concerns according to several
academics (e.g. Gaillard, 2013; Hill and Faff, 2010; Hunt, 2009; Rochet et al., 2008). Rating
announcements could be lagging and therefore intensifying periods of crisis. Next to that the
‘reputational-capital’ model the rating business is based on is not working properly given the
existence for ‘rating shopping’ during the past decade.
The inflated ratings of structured products, credit rating agencies being not able to anticipate a
financial crisis and the fact that sovereign ratings apparently are influencing more than only the
yield of the country that actually gets rated, did no good for the reputation of credit rating
agencies. Assuming that reputation of rating agencies can be measured by the reaction of
investors to rating announcements, a reduced reaction to rating adjustments is expected for the
period since the fall of Lehman Brothers.
When studying the direct reaction to rating/outlook announcements, the results presented in this
paper indicate different reactions to rating/outlook changes between the periods before and since
the fall of Lehman Brothers. During the period 1993 – 2008 downgrades significantly influenced
sovereign yields, upgrades and outlook changes did not. For the period since the fall of Lehman
Brothers however, the significant effect of downgrades diminishes even though many
observations of downgrades are available. Related literature also finds a significant increase in
yield in case of a downgrade, concluding that credit rating agencies play a substantial role within
the financial system by providing additional information to investors. In contrast to this survey
Hill and Faff (2010) even find higher significance levels in times of financial distress while,
according to this study, sovereign rating adjustments lose their significant impact on yields
during the financial crisis. Assigning the loss in substantial impact to a damaged reputation of
credit rating agencies seems far-fetched as the periods before and since the fall of Lehman are
not to be compared. The loss of impact of rating agencies can, at least partially, be explained by
the magnitude and amount of events influencing government bonds yields in the past 5 years like
for instance monetary policy, global uncertainty, risk aversion and contagion risk. Apparently
30
sovereign rating adjustments can get surpassed by other events (especially in periods of crisis),
containing yield sensitive information. It is likely the reputation of credit rating agencies got
damaged in the past few years. If this has substantial impact on the credit rating business
however is questionable, given the central position credit rating agencies obtained within the
current financial system.
To find empirical evidence for any incurred reputational damage of credit rating agencies for the
period 2008 – 2012, one should control for all factors influencing yield that did not exist in the
period 1993 – 2008. This seems challenging taking into consideration the amount of factors
(which are hard to measure or are undefined) determining yield changes during a financial crisis.
Next to that it will be interesting repeating a comparable study for corporate ratings as far more
rating announcements are available for corporates than for sovereigns, resulting in more reliable
results.
31
Appendix
Table 1
#
OECD
countries
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
Australia
Austria
Belgium
Canada
Chile
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Iceland
Ireland
Israel
Italy
Japan
Korea
Luxembourg
Mexico
Netherlands
New Zealand
Norway
Poland
Portugal
Slovak Republic
Slovenia
Spain
Sweden
Switzerland
Turkey
United Kingdom
United States
Data available
since:
30-8-1993
30-8-1993
30-8-1993
30-8-1993
29-3-2007
1-5-2000
30-8-1993
30-8-1993
30-8-1993
30-8-1993
1-4-1999
19-1-1999
21-8-2003
30-8-1993
9-4-2002
30-8-1993
30-8-1993
25-10-2000
30-8-1993
31-7-2001
30-8-1993
30-8-1993
30-8-1993
25-11-1999
30-8-1993
7-1-2004
3-4-2007
30-8-1993
30-8-1993
30-8-1993
27-1-2010
30-8-1993
30-8-1993
32
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
33
Figure 7
Table 2
#
1
2
Date:
09/02/2010
25/03/2010
Country
Greece
Greece
3
4
30/04/2010
07/05/2010
Greece
Greece
5
6
7
07/07/2010
21/03/2011
06/04/2011
Greece
Ireland
Portugal
8
05/05/2011
Portugal
9
10
16/05/2011
29/06/2011
Portugal
Greece
11
12
13
22/07/2011
20/07/2012
26/07/2012
Greece
Spain
Spain
Announcement
Greek Parliament approves first austerity package measures.
Euro area leaders agree, together with the IMF, to offer
financial support to Greece if the country should ask for it.
Greek parliament passes pension reform.
Greek prime minister and IMF agree on 110 billion worth of bailout
package.
Final approval of IMF to 40 billion bailout for Greece.
ECB welcomes Irish authorities’ decision to strengthen Irish banks.
Ministers acknowledged the Portuguese authorities' request for financial
assistance.
ECB welcomes Portugal’s economic and financial adjustment
programme.
Eurozone leaders approve bailout package of 78 billion for Portugal.
Greece parliament approves new austerity measures (voting for 40 billion
of tax increases and spending cuts plus the sale of state owned assets).
EU and IMF agree to give Greece another bailout worth 155 billion.
Eurogroup grants financial assistance to spain’s banking sector.
President of the ECB Mario Draghi mentions, in relation to Spain, the
ECB is ready to do whatever it takes to preserve the Euro.
Sources table 2:
- ECB: Key dates of the financial crisis (since December 2005), retrieved from:
http://www.ecb.europa.eu/ecb/html/crisis.en.html
- BBC news
- The Guardian
- Reuters.com
34
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