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Credit Ratings and the Cost of Debt:The Sovereign Ceiling Channel
Felipe Restrepo∗
Carroll School of ManagementBoston Collegerestrepf@bc.edu
https://www2.bc.edu/felipe-restrepogomez
This Draft: October 30, 2013Please do not distribute
Abstract
A sovereign’s credit rating generally represents the highest attainable rating by most is-suers domiciled within their respective country. In this paper I show that the sovereignceiling represents a meaningful institutional friction, and that through this channelcredit ratings have an important effect on borrowing costs in the private sector. Specif-ically, I estimate the differential effect of contractions and relaxations in the sovereignceiling on the bond spreads of firms that are exactly at the sovereign bound, relative toother firms that are near but not at the bound. I find that following a sovereign down-grade, the spreads of bound firms increase significantly more relative to non-boundfirms. I also show that firms that are bound tend to be rated more unfavorably andtheir default rates tend to be lower relative to non-bound firms.
Keywords: Sovereign Ceiling; Corporate Ratings; Cost of Debt; Event Study, FuzzyRegression Discontinuity Design.
JEL Classifications: G15, G23, G32.∗I would like to especially thank Phil Strahan for his advice and support. I would also like to thank
Clifford Holderness, Darren Kisgen, Jun Qian (QJ), Jérôme Taillard, and seminar participants at BostonCollege for helpful comments and suggestions.
1
1 Introduction
Credit rating agencies (CRAs) play a central role in providing information about the ability
and willingness of issuers, including governments and firms, to repay their debts. A crucial
link between sovereign and corporate credit ratings is the “sovereign ceiling”, a policy still
strongly implemented by CRAs whereby a government’s foreign-currency rating generally
represent the highest attainable rating by corporate issuers within that country.1 CFO
Magazine (2013) summarized the key implication of the sovereign ceiling as follows: “If a
company is a better credit risk than its home country, it might still have trouble getting a
credit rating agency to recognize that fact”. In this paper I show that the sovereign ceiling
represents a meaningful institutional friction, and that through this channel credit ratings
have an important effect on borrowing costs in the private sector. Specifically, I estimate the
differential effect of contractions and relaxations in the sovereign ceiling on the bond spreads
of firms that are exactly at the sovereign bound, relative to other firms that are near but
not at the bound.
The main hypothesis I test in this study is whether the sovereign ceiling policy by
CRAs represents a meaningful friction that affects the cost of debt of firms issuing USD-
denominated bonds in international markets. I evaluate two main implications of this hy-
pothesis. First, if sovereign ratings represent a meaningful constraint for firms that are
exactly bound by the sovereign ceiling, then being bound by the ceiling should be associated
with credit ratings that are more pessimistic relative to firms that are not bound. Second,
if the sovereign ceiling is an important channel through which credit ratings affect a firm’s
cost of debt, then contractions and relaxations in the ceiling (i.e. sovereign downgrades and
upgrades respectively) should lead to more pronounced changes in the yield spreads of those
firms that are exactly at the sovereign bound, relative to non-bound firms. The main chal-1Rating agencies assign different types of ratings depending on the maturity (short term or long term)
and currency denomination (local currency or foreign currency) of an issuance. The focus of this study ison foreign-currency long-term ratings, where CRAs use a sovereign’s rating as a strong upper bound on theratings of bonds issued by firms domiciled within each country.
2
lenge when tracing the effect of the sovereign ceiling on corporate outcomes is the inherent
endogeneity between a sovereign’s credit quality and the creditworthiness of firms in that
country. I explicitly address this concern in my empirical design by focusing on the differen-
tial effect stemming from contractions and relaxations in the sovereign ceiling on firms that
are at the sovereign bound, relative to other firms in the same country that are near but not
at the bound.
To evaluate these testable implications of the sovereign ceiling channel hypothesis, I
exploit two important empirical regularities associated with the sovereign ceiling. First, I
document that the distribution of corporate ratings across sovereign rating levels is system-
atically concentrated exactly at each country’s sovereign rating. As Borensztein et al. (2013)
point out, this implies that even though CRAs have moved away from fully enforcing the
sovereign ceiling over the last two decades, sovereign ratings still represent a strong upper
bound for the ratings of corporate borrowers. Second, I show that within the month of a
sovereign rating change, the probability of a corporate issuer obtaining a rating adjustment
in the same direction and magnitude as its corresponding sovereign jumps exactly at the
sovereign rating bound. Specifically, conditional on the event of a sovereign rating change,
firms that are at the bound have a probability of approximately 60% of obtaining the same
rating adjustment as the sovereign within a month, compared to less than 10% for firms that
are within three notches of the sovereign rating. This jump in the probability of obtaining
a corporate rating change allows me to use a firm’s bound status as an instrument to esti-
mate the effect of a rating adjustment directly related to the sovereign ceiling channel. Since
sovereign rating changes provide no additional firm-level private information, any differential
effect between bound and non-bound firms should result from a jump in the probability of
a rating change for those firms that are at the sovereign ceiling bound. My identification
strategy assumes that changes in fundamentals are the same for treatment (firms bound by
the ceiling) and control firms (firms near but not bound by the ceiling). I focus on a large
sample of bond and firm level data between 1999 and 2012 for 51 countries that had at least
3
one sovereign rating change between this period. The sample includes not only developing
countries (29) but also developed economies (22).
I first find that the sovereign ceiling policy is associated with ratings that tend to be more
pessimistic for firms that are bound by the sovereign ceiling, relative to firms not bound by it.
Specifically, I use as a benchmark financial statements data on firms in AAA countries (where
the sovereign ceiling does not represent a constraint) to estimate the predicted ratings of
firms in non-AAA countries, and find that bound firms tend to be rated more pessimistically
relative to non-bound firms. Similarly, I find that the probability of transitioning into default
during a 5-year window is lower for firms that are bound compared to non-bound firms,
conditional on their rating. I also show that investors, to some extent, recognize that bound
firms are on average of better credit quality when compared to non-bound firms, and thus
their yield spreads tend to be lower for a given corporate rating.
Furthermore, even though the market appears to partially incorporate the fact that bound
firms tend to be more pessimistically rated, my main results indicate that the sovereign
ceiling still has a sizable impact on the cost of debt of firms bound by it. I find that in
the month following a sovereign rating downgrade, the spread of bound firms increases by
approximately 54 bp more relative to other firms in the same country that are near but
below the sovereign ceiling. This differential effect is even more pronounced as the pre
and post event window widens, while remaining statistically significant. Although sovereign
upgrades tend to be associated with a decrease in the spreads of bound relative to non-
bound firms, the magnitude and statistical significance of their effect is lower. Finally, I also
find evidence suggesting that through the sovereign ceiling channel firms’ investment policies
are affected following a sovereign rating downgrade. In particular, in the year following a
sovereign downgrade, capital expenditures scaled by total assets decline by 3.2% more for
bound firms relative to non-bound firms. I perform a falsification test of these results by
examining whether the effect on both corporate spreads and investment are also present one
year before each actual sovereign rating change event in my sample, and find no evidence
4
supporting the alternative explanation that pre-event trends are driving the results.
Overall, the results from this paper are consistent with the hypothesis that the sovereign
ceiling policy is a meaningful institutional friction for firms that issue USD-denominated debt
in international markets, and that through this channel sovereign ratings have an important
effect on borrowing costs in the private sector. The findings of this paper also indicate that
since corporate debt costs can increase when public finances deteriorate, policies that affect
a sovereign’s creditworthiness have a potentially important impact on the cost of external
financing in the private sector through the sovereign rating channel.
2 Literature Review and Relative Contribution
Previous research documents that credit ratings affect a firm’s cost of capital (e.g. Kliger
and Sarig (2000); Jorion et al. (2005); Kisgen and Strahan (2010)), and that sovereign
ratings are an important determinant of corporate ratings (e.g. Borensztein et al. (2013);
Williams et al. (2012)). I extend the existing literature by identifying an unexplored di-
mension, the sovereign ceiling channel, through which credit ratings affect a firm’s cost of
debt. Furthermore, I contribute to prior research examining the link between sovereign and
corporate credit risk by implementing an identification strategy that allows me to estimate
the impact of the sovereign ceiling on corporate bond spreads, while explicitly controlling
for the endogenous component of the country-level information contained in sovereign rating
changes.
2.1 The Sovereign Ceiling and Credit Ratings
This paper is related to prior research that examines the link between sovereign and corporate
ratings. Borensztein et al. (2013) show that, even though CRAs have moved away from
a policy of never rating a firm above the sovereign ceiling, sovereign ratings still have a
5
significant effect on corporate ratings after controlling for country specific and firm-level
characteristics. Williams et al. (2012) analyze the effect of sovereign rating changes on
banks’ ratings in a sample emerging markets countries between 1999 and 2009, and find that
sovereign rating upgrades (downgrades) are strongly associated with bank rating upgrades
(downgrades). The evidence from these studies indicates that the sovereign ceiling still
represents a strong bound for the credit ratings of firms that issue USD-denominated bonds
in international markets. However, these studies stop short of tracing any causal link between
the sovereign ceiling policy and a firm’s cost of debt or corporate policies, which is the main
goal of my study. Relative to these
Researchers have also examined the relationship between sovereign and corporate credit
risk using bond spreads data. Durbin and Ng (2005) compare the yield spreads of foreign
currency denominated corporate bonds to those of bonds issued by their respective home
government. Although Durbin and Ng are limited by a small sample size, they show that
11 corporate bonds out of 108 in their sample have yields that are below their compara-
ble government spreads. They find that these bonds tend to be issued by firms that are
export driven or that have a close relationship with either a foreign firm or the home gov-
ernment. Similarly, Lee et al. (2013) use credit default swaps (CDS) data on a larges sample
of 2,364 companies in 54 countries to examine the characteristics under which firms obtain
lower CDS spreads relative to their respective sovereigns. They find that firms exposed to
better property rights institutions and firms listed on stock exchanges with stricter disclo-
sure requirements are more frequently able to obtain CDS spreads below their sovereign
counterparts. These studies show that firms that manage to “pierce” the sovereign ceiling
are typically export driven, are exposed to better property rights institutions through their
foreign assets positions, and have their stocks listed on exchanges with stricter disclosure
requirements.2 However, these papers do not examine whether the sovereign ceiling still2The findings from these papers are overall consistent with the key features of the relaxation of the
sovereign ceiling by CRAs. For instance, Fitch (2004) explained that: “. . . exceptional banks and corporatescan be rated above the sovereign ceiling if their ’stand-alone’ credit fundamentals imply that they are morecreditworthy than the sovereign and they are shielded from the risk of exchange controls by substantial
6
affects corporate spreads. That is, even if a corporate that is constrained by the sovereign
rating ceiling has a lower spread than its corresponding sovereign, the fact that the firm is
bound by the ceiling could still be hindering that issuer from obtaining an even lower cost
of debt. I explicitly test this hypothesis in my main empirical analysis.
2.2 Credit Ratings, Capital Structure and the Cost of Capital
Existing literature also provides strong evidence that corporate credit ratings affect firms’
financial policies. Kisgen (2006) finds that firms near a rating change issue less debt relative
to equity than firms not near a rating adjustment. Kisgen (2009) shows that following
a rating downgrade, a firm is more likely to reduce leverage. Sufi (2009) shows that the
introduction of syndicated bank loan ratings in 1995 by S&P and Moody’s resulted in an
increase in the use of debt by firms, and also in an increase in cash acquisitions and investment
in working capital.
This paper also relates to papers examining how ratings affect a firm’s cost of capital.
Kliger and Sarig (2000) use the refinement of Moody’s ratings in April, 1986, and find that
corporate rating changes contain additional information that help explain bond spreads.
By focusing on this specific event, Kliger and Sarig (2000) are able to examine the effect of
rating changes that exclusively reflect rating information and not fundamental changes in the
issuer’s risk. Tang (2009) also exploit Moody’s 1982 rating refinement to identify the impact
of information asymmetry on real outcomes: firms with refinement upgrades experience an
additional decrease in their cost of debt compared with firms with downgrades. Tang also
show that this lower cost of debt ultimately leads firms to issue more debt, to invest more and
to hold less cash. Jorion et al. (2005) find that following the implementation of Regulation
Fair Disclosure (Reg FD) in October, 2000, rating downgrades are related to larger declines
in stock prices relative to the pre-Reg FD period. They also find that rating upgrades,
foreign exchange revenues, off-shore assets or more highly rated foreign partners/parents willing to providefinancial support.”
7
which are found to be generally insignificant in previous studies, are associated with greater
increases in stock prices.
Finally, recent literature also shows that credit ratings are important because securi-
ties rules, regulations and institutional investors’ investment policies often depend on them.
Kisgen and Strahan (2010) examine the introduction in the U.S. of DBRS, a fourth credit
rating agency, and show that investments rules and regulations related to bond ratings also
have an impact on corporate borrowing costs. Bongaerts et al. (2012) find that additional
credit ratings matter mainly for regulatory purposes, and they do not appear to add signif-
icant additional information related to credit quality.
3 The Sovereign Ceiling and Corporate Ratings: In-
stitutional Background
Credit rating agencies play a crucial role in providing information about the ability and
willingness of issuers, including governments and private firms, to meet their financial obli-
gations. The three major CRAs -Standard and Poor’s, Moody’s and Fitch- assign different
types or ratings depending on the maturity (short term or long term) and currency de-
nomination of an issuance (foreign currency or local currency). This study focuses on the
foreign-currency, long-term borrowing space, where CRAs use a sovereign’s rating as a strong
upper bound on the credit ratings of firms that operate within each country. Even though
the sovereign ceiling has typically represented a more important constraint for firms in devel-
oping countries where sovereign’ ratings tend to be lower, the relationship between the credit
risk of a sovereign and private sector borrowers has received increased attention following the
recent European sovereign debt crisis, where several developed countries including Greece,
Italy, Ireland, France, Portugal and Spain experienced sovereign rating downgrades.
Until 1997, rating agencies strictly followed the policy of not granting a private company a
8
rating higher than the sovereign rating. In April of that year, S&P first relaxed its sovereign
ceiling rule in three dollarized economies: Argentina, Panama, and Uruguay.3 Fitch and
Moody’s followed suit in 1998 and 2001 respectively. Although rating agencies have moved
away from strictly enforcing the sovereign ceiling over the last two decades, corporate ratings
that “pierce” the ceiling are still not common. For instance, in 2012, S&P reported, that
only 114 corporate and local government FC LT ratings in 20 countries exceeded the ratings
on their corresponding sovereign (S&P’s Rating Services, 2012).4 The limited number of
firms above the sovereign ceiling coupled with the fact that prior research shows that firms
that pierce the ceiling are systematically different than firms at or below the ceiling, are the
central reasons I do not use these firms as part of the control group in my empirical analysis.
The key credit rating implication of the sovereign ceiling is shown in figure 1, where I
plot the distribution of corporate credit ratings by each sovereign rating level. This figure
show that although there are corporate issues that manage to “pierce” the ceiling, almost
systematically the largest concentration of ratings is located exactly at the sovereign rating.
Evidence from figure 1 also suggests that, as expected, the sovereign ceiling policy represents
less of a rating constraint for firms in countries with the highest sovereign ratings (i.e. there
is a lower concentration of corporates at the ceiling in countries with AA-, AA, and AA+
ratings).
Why do CRAs use sovereign’ rating as a strong upper bound when rating corporate
issues? The central argument by rating agencies is that a sovereign default may result
in the government imposing exchange controls and other restrictive measures that limit a
firm’s access to the foreign currency necessary to service their financial obligations. Moody’s
explains that “most non-structured locally-domiciled issuers are rated at or below the level of
the sovereign because they operate in the same economic and financial environment and are
therefore vulnerable to the same broad credit pressures as the sovereign” (Moody’s Investor3For instance, in the case of Argentina S&P increased the credit rating of 14 firms above the sovereign
rating of BB.4The focus of this study is solely on corporate ratings and not on local or state governments issues.
9
Service (2012)). Similar arguments are also made by S&P and Fitch (S&P’s Rating Services,
2012: FitchRatings, 2012). However, the extent to which the sovereign ceiling policy is
implemented, although similar, is not identical across CRAs. S&P is the least likely to
assign a corporate rating above the sovereign, followed by Fitch and Moody’s respectively.
As depicted in figure 2, the percentage of firms with a rating at or below the ceiling in
countries with a sovereign rating below AAA is 90.7% for S&P, 79.3% for Moody’s, and
85.2% for Fitch.5
There are two key factors CRAs use when rating foreign-currency corporate issues: 1)
the issuer’s inherent likelihood of repayment (which is the same as local ratings), and 2) the
issuer profile after taking into account the risk of exchange controls being imposed by the
government that would hinder the ability of non-sovereign issuers to convert local currency
into foreign currency to meet their financial obligations. Thus, firms that “pierce” the ceiling
are particularly strong corporates whose exposure to the risk of not been able to meet their
foreign currency obligations in the case of a sovereign default is clearly very limited. Firms
with foreign assets, high export earnings and foreign parents tend to have a higher probability
of being rated above their corresponding sovereign.6 In general, CRAs only grant an issuer
a rating above the sovereign if it is able to demonstrate a strong resilience and low default
dependence with the sovereign, as well as a degree of insulation from the domestic economic
and financial disruptions that are typically associated with sovereign defaults. Note however
that there is not a clear reason why the creditworthiness of firms that are exactly at the bound
should be affected more by a change in a sovereign’s credit quality than other firms also near
the sovereign ceiling but not exactly at it, which is a key component of my identification
strategy.5Moody’s for instance explains that when rating corporate debt in foreign currency, they are willing to
assign a rating that is up two to notches above the sovereign (Moody’s Investor Service, 2012).6The sovereign ceiling can also be pierced if bonds are offered with sufficient collateral. For example, The
Economist (2006) reported that in 2005 the Emirates National Securitization Corporation (ENSeC) issuednotes for $350m, using as collateral property leases in The Palm, a property development on islands shapedlike a palm tree, as well cash collateral. The notes received a rare AAA rating by both S&P and Moody’s.
10
4 Data and Summary Statistics
Since the goal of this paper is to examine how, through the sovereign ceiling channel, credit
ratings affect the cost of debt of firms, I start my sample construction by collecting data on
long-term foreign-currency sovereign ratings directly from S&P, Moody’s and Fitch. Sim-
ilarly, I obtain from Bloomberg corporate ratings for USD-denominated bonds by non-US
firms, and I match each issue with its corresponding sovereign data. I obtain a bond’s S&P,
Moody’s and Fitch ratings, as well as its end of the month yield where available. I also
collect issue specific information (issuance and maturity dates, amount issued, coupon pay-
ment and frequency and collateral type), firm-level information (e.g. issuer ticker, industry
classification) and financial statements data where available. I convert both sovereign and
corporate ratings to a numerical scale ranging from 0 to 21, where 21 represents a AAA
rating (see table A.1 in the Appendix for the numerical conversion). Since bond pricing data
is available from Bloomberg starting in 1999, I construct my matched sample of sovereign
ratings, corporate ratings and corporate bond yields from 1999 to 2012.
The fact that I use USD denominated bonds implies that spreads above US treasury
yields represent default risk, rather than currency risk (Domowitz et al. (1998); Durbin
and Ng (2005)). Thus, I calculate corporate yield spreads by subtracting the equivalent
maturity US Treasury yield for each issue.7 I eliminate a small number of observations with
negative spreads, I require that yields for consecutive months are not equal, and I winsorize
at the 1% level to reduce the influence of outliers. Since my empirical strategy exploits
sovereign rating changes for identification, I exclude from my sample countries and firms
where sovereign ratings for all three CRAs were unchanged between 1999 and 2012.
Table 1, panel A shows the number of bonds (CUSIPs), firms and countries each year
in my sample. Panel B in table 1 displays the composition of firms by industry, using the
Dow Jones’s Industry Classification Benchmark (ICB). The final matched sample consists7I obtain constant maturity U.S. treasury rates data from the Federal Reserve Economic Data (FRED)
website: http://research.stlouisfed.org/fred2/
11
of rating history data for 51 countries, 566 firms and 1,935 distinct issues. Table 2 provides
corporate yield spreads summary statistics by rating category, and shows that conditional
on a firm’s corporate rating, yield spreads tend to be on average 90 bp lower if the firm is
bound by the sovereign ceiling. Table 2 also shows that this difference is generally more
pronounced for lower ratings (e.g. the bound vs. below bound difference is -1.7% for B+
firms) and less important for higher ratings (e.g. for A+ firms the difference is +0.2%,
although it is statistically no different to zero).8 Table 4 summarizes the coverage of the
data as well as the number of sovereign rating changes by country. In table 3 I report several
bond and firm level characteristics for bound and non-bound firms, and compare whether
their averages are statistically different from each other. I find that bonds issued by bound
firms do not have a statistically different maturity when issued, although they do appear to
be issued with a higher face value (the average issue for a bound firm has a face value of
436million, vs.385 million for non-bound firms). In terms of firm-level characteristics, bound
firms on average have a leverage ratio of 60%, which is slightly higher than the 56% ratio
for non-bound firms. A firm’s EBITDA over Assets, Capex over Assets, and Total Debt
Issuance over Assets are not statistically different from each other depending on a firm’s
bound status.
5 Methodology and Results
The fundamental hypothesis of this paper is that the sovereign ceiling represents a meaningful
institutional friction that affects the cost of debt of firms that issue USD-denominated bonds
in international markets. I evaluate two main empirical implications of this hypothesis.
First, if sovereign ratings do in fact represent a meaningful constraint for firms that are at
the sovereign ceiling, then being bound should be systematically associated to credit ratings
that are more pessimistic relative to firms that are not bound. Second if the sovereign8I perform a more formal test of the bound vs. below bound effect on bond spreads in the following
section to also account, for instance, for time variation in spreads.
12
ceiling is an important channel through which credit ratings affect firms’ cost of debt, then
contractions and relaxations in the ceiling should lead to more pronounced changes in the
yield spreads of those firms that are at the sovereign bound, relative to non-bound firms.
The main challenge when tracing the effect of sovereign ceiling contractions or relaxations
on corporate outcomes is the inherent endogeneity between a sovereign’s credit quality and
the creditworthiness of firms in that country. I explicitly address this concern in my empirical
strategy by examining the differential effect stemming from sovereign rating changes on firms
that are bound by the sovereign ceiling, relative to other firms in the same country that are
near but not bound by it. I do this by exploiting two important empirical regularities
associated with the sovereign ceiling. First, as discussed earlier and depicted in figure 1, the
distribution of corporate ratings across sovereign rating levels is systematically concentrated
exactly at each country’s sovereign rating. This implies that even though CRAs have moved
away from fully enforcing the sovereign ceiling over the last two decades, sovereign ratings
still represent a meaningful upper bound for corporate borrowers issuing USD-denominate
bonds in international markets. Second, as figure 3 shows, the probability of a corporate
issuer obtaining a rating adjustment in the same direction and magnitude as its sovereign
within the month of a sovereign rating change is also discontinuous exactly at the sovereign
rating bound (where a firm’s “distance-from-sovereign”, the difference between a firm’s rating
and its corresponding sovereign, is equal to zero). More precisely, the middle panel in figure 3
shows that conditional on the event of a sovereign rating change, firms that are at the bound
have a probability of approximately 60.3% of obtaining the same rating adjustment as the
sovereign within a month, compared to 9.9%, 5.0% and 2.5% for firms that are respectively
one, two and three notches below the sovereign rating. The left and right panels in figure 3
also show that this disparity in the response of corporate of ratings is not observed either
the month before or the month after the sovereign change.
As a result, the key identifying assumption in my empirical strategy is that sovereign
rating changes do not provide additional firm-specific information, and thus the differential
13
effect on corporate yields between bound and non-bound firms in the event of a contraction
or relaxation of the sovereign ceiling should be stemming from an increased probability of
obtaining a corporate rating change in the same direction as the sovereign for those firms
that are exactly at the ceiling bound. Consequently, if the sovereign ceiling constrains firms
from potentially obtaining higher ratings, and if this is not fully incorporated in market
prices, then following an upgrade in a country’s sovereign rating, corporate borrowing costs
should decrease more for those firms that are bound by the sovereign ceiling, relative to firms
in the same country that are not bound by the constraint. Conversely, if a contraction in the
sovereign ceiling results in firms at the bound obtaining a lower rating, then yield spreads
should increase more for bound firms relative to non-bound firms.
5.1 Does being bound by the sovereign rating lead to a pessimistic
rating?
I first test whether firms that are bound by the sovereign ceiling (i.e. that their rating
is equal to the sovereign rating) have a more “pessimistic” rating relative to firms that
are not bound by it. If the sovereign ceiling represents a meaningful friction and not just
an unbiased and accurate assessment of a firm’s creditworthiness, then this rating practice
should be systematically associated with ratings that are more pessimistic for bound firms
relative to other firms with the same actual ratings but that are not bound by the sovereign
ceiling. Thus, I first examine whether the sovereign ceiling policy is consistent with CRAs’
providing an unbiased assessment on the creditworthiness of borrowers by comparing the
differential effect of being bound on a firm’s predicted rating, as well as on its probability of
transitioning into default.
14
5.1.1 Rating Analysis: The effect of “bound status” on ratings
I explore whether bound firms tend to be pessimistically rated using a two-step procedure.
First, I use as a benchmark annual financial data on rated firms that issue USD-denominated
debt in AAA countries (where this friction does not matter) to predict the corporate ratings
of firms in non-AAA countries, where the sovereign ceiling rule potentially matters.9 Using
this sample of firms in countries with a AAA sovereign rating I estimate a regression using
a set of explanatory variables used in previous studies predicting credit ratings (see Kisgen,
2006 and Horrigan, 1966 for a similar implementation). The dependent variable is a firm’s
credit rating, which takes a value of 1 for a rating of D, up to a value of 21 to AAA (see
table A.1 in appendix for the numerical conversion). I estimate the following regression for
firms in AAA countries:
Rtgi,t =β1(NI/Ai,t) + β2(D/TotCapi,t) + β3Ln(Ai,t) + β4Square(NI/Ai,t)
+ β5Square(D/TotCapi,t) + β6Square[Ln(Ai,t)] + TimeFEt + SectorFEi + εi,t
(1)
For firm i and year t. NI/A is net income over assets, D/TotCap is the ratio of debt to
total capitalization and Ln(A) is the log of total assets. I include year fixed effects to control
for time specific shocks common to all firms, as well as sector fixed effects. I estimate the
model above for firms in AAA countries and then I use the estimated coefficients to calculate
the predicted credit ratings for the sample of firms in non-AAA countries (which I denote
as Rtg), where the sovereign rating ceiling potentially represent a meaningful institutional
friction.10
In the second step, after having calculated the predicted rating for the sample of non-
AAA countries, I compare, for each actual corporate rating level, whether predicted rating
are systematically higher for firms that are bound relative to other firms with the same
actual rating but that are not bound by the sovereign ceiling. Thus, I estimate the following9I do not include data for U.S. firms, as the ratings for USD-denominated debt are in that case local-
currency, and not foreign-currency as they are elsewhere in my data.10Estimating the model in equation 1 for the sample of firms in AAA countries results in an adjusted R2
of 0.51. Table A.2 in the appendix shows the estimated coefficients obtained from this regression.
15
regression:
Rtgi,t = β0 + β1RtgFEi,t + β2(RtgFEi,t ∗Boundi,t) + Sov.RatFEi,t + TimeFEt + εi,t (2)
Where β2 is a vector of coefficients that captures the differential effect, for each rating
level (denoted by Rtg.FE), of being bound by the sovereign ceiling on a firm’s predicted
rating. If firms that are bound are rated fairly relative to firms that are not at the ceiling
bound, then predicted ratings should not systematically differ based on whether firms are
below or at the sovereign bound. On the other hand, if firms that are bound tend to be
pessimistically rated, then their predicted rating should be higher relative to other firms with
the same actual corporate rating but that are not bound by the sovereign rating. ). I focus
only on firms that have a corporate rating below the sovereign rating. As highlighted earlier,
previous research and CRAs themselves indicate that firms that are above the ceiling have
typically strong ties to more financially developed countries and other specific characteristics
that make them fundamentally different than other firms that do not pierce the ceiling.
Table 5 shows that when comparing the predicted rating for bound versus below bound
firms by estimating equation 2, bound firms tend to have a predicted rating that is above the
predicted rating of the latter. For example, a bound firm with a B+ rating has a predicted
rating that is 1.1 notches higher than a firm that is also rated B+ but that is not bounded by
the sovereign ceiling. The difference between the predicted ratings of bound vs. below bound
firms is positive and statistically significant in 12 of the 14 actual rating levels evaluated.
The difference between the predicted rating of bound versus below bound firms is only not
positive in two of the highest rating levels (AA- and AA) where, as indicated before, the
sovereign ceiling rule represents a less meaningful restriction.11
11I also perform this comparison for bound firms relative to above-bound issuers in unreported tests andfind similar results. Note however that due to the limited number of observations for firms above the sovereignceiling, the power of the tests is significantly smaller.
16
5.1.2 Default analysis: The effect of “bound status” on transitioning into de-
fault
An alternative test to evaluate whether a firm’s “bound status“ leads to a systematically
pessimistic rating is to examine whether bound firms are associated with a lower default
rate than non-bound firms, for the same actual rating. Specifically, I asses the unbiasedness
of credit ratings in predicting that a firms transitions into default, depending on whether
the firm is bound by the sovereign rating or not. The power of any default based test is
constrained by the fact that actual defaults do not occur frequently. Out of the 566 firms
in my full sample of non-AAA countries, only 15 had at any time a default rating (“D”).
Thus, to perform this particular test I extend this sample to include firms with a rating of
CCC+ and below, which more precisely proxy for being “close to default”. These firms are
characterized by S&P as having “significant speculative characteristics, currently vulnerable”.
The number of close-to-default firms in the sample is 64. I estimate a logit regression where
the dependent variable is a dummy variable that indicates whether a firm had a rating of
CCC+ or below during the last five years. I examine if a firm’s bound status affects its
probability of being close to default, after controlling for its credit rating by estimating the
following model:
Close-to-defaulti,j,[t,t+T ] =β0 + β1RtgFEi,j,t + β2(RtgFEi,j,t ∗Boundi,j,t)
+ SovRatFEi,t + TimeFEt + εi,j,t
(3)
where the coefficients vector β2 measures the interaction between each corporate rating
and Bound, a dummy variable that takes a value of one for bounded firms and zero other-
wise. β2 captures, for each corporate rating, whether being bound by the sovereign rating is
associated with a lower default. I include time (year) fixed effects to account for variations in
default rates through the business cycle. Similarly, I include sovereign rating fixed to control
for differences in default rates that vary dependent on the overall level of creditworthiness
17
of a sovereign.
The results from estimating the default model in equation 3 are shown in tables 6 and
7, which respectively report the estimated coefficients and the marginal effects from the
estimated logit model. Note that there are no estimated coefficients for firms with ratings
above A, as there are no firms with these higher ratings that transition into “close-to-default”
in my sample. Consistent with the finding from the previous subsection that bound firms
tend to be rated more pessimistically, the predicted probabilities in table 7 indicate that
bound firms tend to have a lower probability of transitioning into default, for a given rating,
than non-bound firms. For instance, the probability of a non-bound firm with a B+ rating
transitioning into “close-to-default” in a 5-year window is 7.1%. This is significantly higher
than the 4.3% probability of a firm also with a B+ rating but bound by the sovereign ceiling
of transitioning into “close-to-default”. As it was the case in the predicted ratings test, the
difference between bound and non-bound firms is more pronounced for firms in lower rating
levels.
5.1.3 Are bound firms perceived as more creditworthy by the market?
The evidence from the previous two tests indicates that bound firms tend to be more un-
favorably rated by CRAs and tend to have lower probabilities of transitioning into default
than non-bound firms with the same rating. Thus, the next natural question is whether
investors in the corporate bond market “follow the ratings”, or do they see past them and
recognize bound firms’ relatively higher credit quality. To test whether the market actually
recognizes bound firms’ potentially higher credit quality, I estimate the following regression:
Spreadi,j,t =β0 + β1RtgFEi,j,t + β2(RtgFEi,j,t ∗Boundi,j,t)
+ Sov.RatFEi,t + TimeFEt + εi,j,t
(4)
where the dependent variable is a bond’s yield spread, and the coefficients vector β2
18
measures the interaction between each corporate rating and Bound, a dummy variable that
takes a value of one for bound firms and zero otherwise. The coefficients vector β2 captures,
for each corporate rating, whether being bound by the sovereign rating or not results in
differences in corporate spreads for a given rating level, after including sovereign rating fixed
effects and time (month) fixed effects. Table 8 shows the estimation results from model 4.
Overall, the average spread of bound firms, after being demeaned by sovereign rating and
time effects, tends to be lower for each rating category when compared to non-bound firms.
For instance, a B+ rated firm bound by the sovereign ceiling, the average, time-demeaned
spread is 7.13%, 66 basis points higher than the average spread of 6.46% for a non-bound
B+ rated firm. This suggest that investors do incorporate, to an extent, the better quality
and lower probability of default of bound firms relative to non-bound firms given the same
corporate rating.
5.2 The Effect of the Sovereign Ceiling Channel on Corporate
Yields
The results from the empirical tests in the previous subsection show that bound firms tend to
be rated more pessimistically, have lower probabilities of transitioning into default, and are
often priced at lower yield spreads by the market than non-bound firms. However, the fact
that investors price the debt of bound firms at lower yields does not in itself mean that the
sovereign ceiling policy has no impact on firms affected by it. More precisely, it is possible
that relaxations or contractions in the sovereign ceiling result in even a more pronounced
reduction or increase in the cost of debt of firms bound by the sovereign rating. To test this,
in this subsection I first implement reduced form regressions to examine whether firms’ bor-
rowing costs increase (decrease) more for those firms that are bound by the ceiling following
a contraction or a relaxation in the sovereign ceiling. I then use a fuzzy regression discon-
tinuity design (RDD) to instrument a one-notch corporate rating change directly related to
19
the sovereign ceiling channel, using as an instrument the interaction between a firm’s bound
status and a sovereign rating change.
5.2.1 Reduced Form Regressions
I examine the change in corporate spreads around sovereign rating downgrades and upgrades
for firms that are bound by the sovereign ceiling, relative to firms that are near but not
bound by it. That is, I use a firm’s bound status as an instrument to estimate the effect of
a contraction or a relaxation in the sovereign ceiling on corporate spreads. I estimate the
following reduced form pooled regression for sovereign downgrades and upgrades:
∆Spreadi,j,[t−s] =β1Sov.Downi,j + β2Sov.Upi,j + β3 (Sov.Downi,j ∗Boundi,j)
+ β4 (Sov.Upi,j ∗Boundi,j) + εi,j
(5)
where the dependent variable is the change in spread around sovereign rating changes:
the spread on a firm’s bond t months after each sovereign event minus its spread s months
prior to the event. I focus on the differential effect on bound firms relative to firms that
are near but not bound by the sovereign ceiling. The main coefficient of interests are β3
and β4, the interaction terms between sovereign downgrades and upgrades respectively, and
the bound status identifier. I focus only on firms that have a corporate rating below the
sovereign rating, as firms that pierce the ceiling tend to have certain specific characteristics
that make them fundamentally different. I then face a trade-off between using as controls
firms that are not bound by the sovereign rating but that are not too far away from it,
and having enough firms as controls. Thus, I constrain non-bound firms to be three rating
notches or less below the sovereign. I also limit my analysis to firms that have a rating
of B- or higher.12 Because rating changes can be anticipated, I perform event studies with
different values of t around the time of the sovereign rating announcements to capture the
response of financial markets. The indicator variables Sov.Down takes a value of one the12Only 0.1% of the observations in my sample have a corporate rating in the CCC, CC or D categories.
20
month of a sovereign downgrade. Similarly, the dummy variable Sov.Up takes a value of
one the month of a sovereign upgrade. The dummy variable Bound takes a value of one if
a corporate issue has the same rating as the corresponding sovereign prior to a sovereign
rating change, and zero if a firm is three notches or less below the sovereign bound. Since
firms can have two or more bonds at any given point, I weight observations based on the
number of CUSIPs observed for each firm at each point in time, and I cluster standard errors
by each country-sovereign event. If the sovereign ceiling represents a meaningful constraint
for firms, I expect the interaction coefficients β3, which measures the differential effect of
sovereign downgrades on the change in spreads of bound firms, to be positive, indicating a
higher increase in yields for bound firms. Similarly, I expect β4, which measures the added
effect of sovereign upgrades on bound firms, to be negative.
Table 9, panel A reports the results from estimating the model in eq. 5 for event windows
starting three months prior to a sovereign rating change. Results from panel A indicates
that following a sovereign rating downgrade, and when comparing the change in spread one
month after the event vs. the spread three months prior to the sovereign rating adjustment,
the average spread for firms that are bound by the sovereign ceiling increases by 103 bp more
than for firms that are not bound by the sovereign ceiling. As the event windows widens
to three months after a sovereign downgrade the differential effect increases to 117 bp,
while remaining statistically significant. Results for the upgrade interaction coefficient are
statistically significant although economically weaker: the spread for bound firms decreases
by 27 bp more following a sovereign upgrade. The fact the differential effect of sovereign
rating changes is stronger for downgrades than for upgrades is consistent with previous
studies who also find and asymmetry in the response to each type of event (e.g. Brooks
et al., 2004; Gande and Parsley, 2005; Ferreira and Gama, 2007)
Since I focus on the change in spreads around sovereign events for bound firms and firms
that are not bound but still close to the bound, the empirical design I set up in equation 5
should not require the use for other baseline covariates; that is, their inclusion should not
21
affect the consistency of the parameters of interest β3 and β4. Nevertheless, to further pin
down the identification of the effect of changes in the sovereign rating ceiling on spreads I
extend the specification in model 5 by including country-event specific fixed effects (i.e. fixed
effects for each sovereign rating downgrade or upgrade for each country). This approach,
which reduces the likelihood that the coefficient estimates β3 and β4 will be biased by a
correlation between country-time specific effects and spread changes, has a high cost in
terms of lost degrees of freedom. Including country-event fixed effects equates to estimating
the differential impact of the sovereign ceiling on the cost of debt of firms that are bound
by the sovereign ceiling, relative to firms in the same country that are not bound by the
constraint. I estimate the following model:
∆Spreadi,j,[t−s] = β1 (Sov.Downi,j ∗Boundi,j) + β2 (Sov.Upi,j ∗Boundi,j)
+ CountryEventFE + εi,j
(6)
where the main coefficients of interest are β1 and β2 (note that including country-event
specific fixed effects absorbs both the constant term and the Sov.Down term included in
the pooled regression). Panel B in table 9 shows the estimation results of this specification.
Although including country-event fixed effects dampens the overall magnitudes compared to
the pooled OLS results in panel A, the coefficients for the interaction term Sov.Downi,j,t ∗
Boundi,j,t remain statistically and economically significant. For example, the spread for
firms that are bound by the sovereign ceiling increases by 54 bp more than for firms that
are not bound by the sovereign ceiling using a one month after the event. When using a
three-month pre vs. one month post window, the effect of a sovereign rating upgrade on
firms bound by the ceiling is a decrease of 12 bp relative to non-bound firms. However,
results for sovereign upgrades yield no statistical significance as the event window widens
once country-event fixed effects are introduced.
22
5.2.2 Fuzzy Regression Discontinuity Design (RDD)
In this subsection I implement a fuzzy RDD to instrument a one-notch corporate rating
change directly related to the sovereign ceiling channel, using as an instrument the interac-
tion between a firm’s bound status with a sovereign rating downgrade (or upgrade). More
precisely, I implement a fuzzy RDD where the effect evaluated is the impact on a firm’s yield
spread resulting from a corporate credit rating change directly related to a sovereign ceiling
contraction or relaxation. As it has been noted, CRAs do not strictly follow the sovereign
ceiling policy and thus a sovereign rating change does not lead to a 100% probability in
bound firms obtaining the same rating change, as it would be required for a “sharp” RDD.
However, as previously shown in figure 3, the probability of treatment jumps sharply from
less than 10% for firms that are three notches or less below the sovereign ceiling to just above
60 % for firms that are exactly at the bound.
The reduced form estimated using equations 5 and 6 and the fuzzy RD are closely related
to each other. However, since the probability of a firm obtaining the same rating change as
the sovereign is less than one at the sovereign bound, the jump in the relationship between
δSpread and Bound can only be interpreted as the average treatment effect of a corporate
rating change stemming from the sovereign ceiling channel if Bound does not affect the
changes in spreads outside of its influence through treatment receipt. As Lee and Lemieux
(2010) point out, the treatment effect can still be obtained by instrumenting the treatment
dummy (obtaining a corporate downgrade or upgrade due to the sovereign ceiling) with a
firm bound’s status. Thus, I implement the following fuzzy RD design described by the
following equation system:
∆Corp.Downi,j = α1(Boundi,j ∗ ∆Sov.Downi,j) + CountryEventFE + εi,j (7a)
∆Corp.Upi,j = γ2(Boundi,j ∗ ∆Sov.Upi,j) + CountryEventFE + εi,j (7b)
23
∆Spreadi,j,[t−s] = β1 ∆Corp.Downi,j + β2 ∆Corp.Upi,j + CountryEventFE + εi,j (8)
where the first two equations correspond to the first stage where the change in corporate
ratings are estimated after a sovereign downgrade or upgrade respectively. These are then
used in the second stage regression where the main coefficients of interest are β1 and β2.
Table 10 shows the estimation results of the RDD setting, which I obtain using 2SLS. The
results from this set of regressions are overall consistent with previous results from the
reduced form regressions, but more precisely identify the effect of a one-notch corporate
rating change directly related to the sovereign ceiling channel. For example, the effect of a
one-notch corporate rating downgrade directly stemming from the sovereign ceiling channel
is 88 bp using a one month post vs. three month pre-event window. As before, the effect of
downgrades remains economically and statistically significant as the event window widens.
5.2.3 Effect on a Firm’s Investment and Financing
The results above indicate that through the sovereign ceiling channel credit ratings have an
important effect on a firm’s cost of debt. Thus, in this subsection I examine whether a firm’s
investment and financing policies are affected, for the subset of non-financial firms in my
sample. I focus my analysis around contractions and relaxations in the sovereign ceiling, but
now I examine the effect on corporate policies one year after a sovereign event relative to the
year prior to the event. I estimate the following regression using the instrumented variables
Corp.Downi,t and Corp.Upi,t estimated as above in the RDD eq. 8:
Corp.Outcomei,t = β1 δCorp.Downi,t + β2 δCorp.Upi,t
+ CountryEventFE + SectorFE + εi,t
(9)
Where Corp.Outcome is one of five dependent variables examined: capital expenditures,
net issuance of long-term debt, net issuance of short-term debt, total net debt issuance,
24
and net equity issuance. All variables are scaled by beginning of the year total assets.
Since financial statements data are only available for a subsample of firms, the number
of observations in these regressions is significantly lower. Table 11 reports the estimation
results from estimating the model in eq. 9 for each the evaluated corporate outcomes.
Panel A focuses on sovereign downgrades and Panel B reports the effect around sovereign
upgrades. The evidence around sovereign downgrades suggests that through the sovereign
ceiling channel, a firm’s capital expenditures decline by 3.2%. However, evidence in terms of
financial policies is not statistically significant. Similarly, there is little statistical evidence
that sovereign upgrades affect a firm’s investment or financing policies, which is consistent
with the findings above on the low effect on corporate spread changes around sovereign
upgrades.
5.3 Robustness Tests
5.3.1 Falsification test: Effect of the sovereign ceiling one year before the actual
event
I perform a falsification test to addresses concerns for pre-event trends driving the differential
effects on spreads. I estimate the same model with country-event fixed effects as in panel
B in table 9, with the only difference that I focus on the differential effect of bound versus
non-bound firms one year before the actual event. I report the results of this test in table
12. Consistent with the main hypothesis in this paper that the contractions and relaxations
in the sovereign ceiling are the main drivers behind the identified differential changes in
corporate spreads, I find that none of the coefficients reported in table 12 are statistically
different from zero.
Similarly, in table 13 I perform a falsification test that evaluates the concern for pre-
event trends driving the results on firms’ investment policies. There, I estimate the same
model as in table 11, with the only difference that I focus on the differential effect of bound
25
versus non-bound firms one year before the actual event. As it was the case with spreads,
the coefficients reported in table 13 indicate that for instance, the differential effect found
around sovereign downgrade on capital expenditures are not found one year prior to the
actual sovereign rating adjustment.
6 Conclusions
In this paper I investigate how, through the sovereign ceiling channel, credit ratings af-
fect the cost of debt of firms that issue USD-denominated bonds in international markets.
Specifically, I estimate the differential effect of contractions and relaxations in the sovereign
ceiling on firms that are bound by the sovereign rating ceiling relative to firms that are not
constrained by it. The empirical design and results of this paper add to the credit ratings
literature by identifying an unexplored dimension through which credit ratings affect firms,
and by exploiting an identification strategy that allows me to isolate the effect of contractions
and relaxations in the sovereign ceiling on corporate outcomes.
I first show that the sovereign ceiling policy is associated with pessimistically biased
ratings for firms that are bound by the sovereign ceiling, compared to non-bound firms.
Similarly, I find that the probability of bound firms transitioning into default using a 5-year
window is lower for bound firms. Consistent with these findings that suggest that bound
firms tend to be of better quality than non-bound firms, bond market participants tend to
price at lower yield spreads the bonds of firms that are bound by the sovereign ceiling, given
the same corporate rating. Although this might indicate that the market, at least partially,
sees through this rating policy and recognizes the higher credit quality of bound firms, I
find evidence indicating that the sovereign ceiling channel still has a sizeable effect on firms
affected by it. Specifically, I show that following a sovereign downgrade, the cost of debt
increases significantly more for firms that are bound by the sovereign rating, compared to
firms with ratings below the ceiling.
26
The results of this paper also highlight a particular link through which corporate debt
costs can be increased when public finances deteriorate. My findings suggest that through
the sovereign ceiling channel, policies that affect a sovereign’s creditworthiness might have
an important impact on the cost of external financing for firms in the private sector. This
represents a potential externality of public debt on private borrowers that have the same
rating as their government, evidence of which has not been previously been identified in the
literature.
27
References
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Figure 1: Distribution of corporate credit ratings by sovereign rating.This figure depicts the frequency distribution of long-term foreign-currency (LT FC) corporate ratings bythe LT FC sovereign rating of the corresponding country of domicile. Observations for countries withAAA ratings are excluded as, by definition, the sovereign ceiling policy does not represent a constraint forcorporates when the sovereign has the maximum attainable rating. The figure includes ratings from S&P,Moody’s and Fitch which are homogeneized using the numerical conversion in Table A.1 in the Appendix.The bars in dark blue in the diagonal represent the sovereign rating ceiling (i.e. where corporate and sovereignratings equate).
CCC+
B-
B
B+
BB-
BB
BB+
BBB-
BBB
BBB+
A-
A
A+
AA-
AA
AA+
AAA
Cor
pora
te R
atin
g -
FC
LT
CCC+ B- B B+BB-
BBBB+
BBB-BBB
BBB+ A- A A+AA-
AAAA+
Sovereign Rating - FC LT
Frequency of Corporate Ratings by Sovereign Rating
30
Figure 2: Distribution of corporate bonds’ “Distance-from-Sovereign” (difference be-tween a corporate ratings and its corresponding sovereign rating)This figure plots the distribution of the difference between long-term foreign-currency (LT FC) corporateratings and LT FC sovereign ratings for S&P, Moody’s and Fitch. Observations for countries with a AAArating are excluded from the graph, as well as for countries where no sovereign rating changes are observedfrom any credit rating agency between 1999 and 2012.
0.1
.2.3
Den
sity
-16 -12 -8 -4 0 4 8S&P Corporate Rating minus Sovereign Rating
0.1
.2.3
Den
sity
-16 -12 -8 -4 0 4 8Moody's Corporate Rating minus Sovereign Rating
0.1
.2.3
Den
sity
-16 -12 -8 -4 0 4 8Fitch Corporate Rating minus Sovereign Rating
31
Figure 3: Proportion of corporate rating changes around sovereign rating changes by “distance-from-sovereign”This figure plots the fraction of corporate rating changes the month before, the month of, and the month after a sovereign rating change. Observationsare grouped according to each corporate’s “distance-from-sovereign” (the difference between the corporate rating and its corresponding sovereign).For example, a “distance-from-sovereign” of zero means that the corporate rating is equal to the sovereign rating. The value of each bar indicates thefraction of corporate issues in that group whose rating changes in the same direction and magnitude as the sovereign change. Values of “distance-from-sovereign” lower than -6 and greater than +2 are grouped at the “<= -6” and “>= +2” bins respectively due to limited observations beyondthese values.
1.2 1.9 1.9 0.4 0.4 1.7 1.1 1.73.4
0
10
20
30
40
50
60
70
80
(%)
<= -6 -5 -4 -3 -2 -1 0 +1 >= +2Distance-from-Sovereign
One Month Before a Sovereign Change
1.0 0.43.8 2.5
5.0
9.8
60.2
22.4
14.9
0
10
20
30
40
50
60
70
80
(%)
<= -6 -5 -4 -3 -2 -1 0 +1 >= +2Distance-from-Sovereign
The Month of a Sovereign Change
1.3 1.8 2.40.9
2.5 3.1 3.14.8
1.8
0
10
20
30
40
50
60
70
80
(%)
<= -6 -5 -4 -3 -2 -1 0 +1 >= +2Distance-from-Sovereign
One Month After a Sovereign Change
Percentage of Corporate Rating Changes Around Sovereign Changesby Distance-from-Sovereign
32
Figure 4: Corporate spreads around sovereign rating changesThis figure depicts the regression coefficients of corporate spreads on monthly time dummies around sovereign downgrades (left panel) and upgrades(right panel) for two groups: firms that at the time of the sovereign event are exactly at the sovereign ceiling (“Bound Firms”) and firms thatare three notches or less below the sovereign ceiling (“Below Bound Firms”). The dependent variable is the corporate bond spread, regressed onevent-time dummies (months relative to the sovereign rating change), a dummy variable for each country-event (i.e. each sovereign rating change),and bond fixed effects. Thus, the plotted coefficients can be interpreted as the change in bond spreads through time around sovereign rating changes.The vertical dotted line between zero and one represents the event occurrence, which happens after the end of the month at t=-1 and before t=0.The base period for each group’s corporate rate changes is 10 months prior to each event. I require that a bond has at least one observation in thepre-event period and one in the post-event period. The regression estimated is:
Spreadi,j,t = β1EventT imeFE + β2 [EventT imeFE ∗Boundi,j,t] + Firm&EventFE + εi,j,t
where the estimated coefficient vectors β1 and β2 are used to plot the changes in spreads in event time. Standard errors clustered bycountry-event are used to calculate the 95% confidence interval of the difference between the two groups.
-1.00.01.02.03.0
(%)
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
(%)
-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9Event Time (months)
Bound Firms Bounds vs. Below Bound Difference
Below Bound Firms 95% Conf. Interval
Sovereign Downgrades
-2.0-1.00.01.02.0
(%)
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
(%)
-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9Event Time (months)
Bound Firms Bound vs. Below Bound Difference
Below Bound Firms 95% Conf. Interval
Sovereign Upgrades
Corporate spreads around sovereign rating changes
33
Table 1: Sample DescriptionThis table reports in panel A the number of observations over time included in the sample. Panel B showsthe distribution of firms in the sample by industry, using the Dow Jones’s Industry Classification Benchmark(ICB). The sample includes USD denominated bonds issued by non-US firms with at least one credit ratingand located in countries with at least one sovereign rating change between 1999 and 2012.
Panel A. Number of Bonds, Firms and Countries in the Sample by Year
YearNumber of
ObservationsNumber of Bonds Number of Issuers Number of Countries
1999 1,369 176 92 142000 1,967 197 110 182001 1,613 226 128 182002 2,161 225 126 202003 1,617 211 115 192004 2,463 271 145 172005 2,942 237 114 162006 2,111 220 119 182007 2,191 194 101 162008 1,893 221 109 222009 2,731 378 133 222010 6,150 567 203 272011 10,041 865 255 322012 12,529 932 265 34
1,93556651
Number of distinct bonds:Number of distinct issuers:Number of distinct countries:
Panel B. Number of Firms by Industry
Oil & Gas 45Basic Materials 37Industrials 56Consumer Goods 46Health Care 5Consumer Services 24Telecommunications 40Utilities 64Financials 243Technology 6
Total 566
Number of issuers by ICB industry
34
Table 2: Corporate yield spread summary statistics by ratingThis table reports sample statistics on yield spreads for the sample of corporate bonds. The sample includes monthly data on all USD-denominatedbonds issued by non-US firms located in countries with at least one sovereign rating change between 1999 and 2012.
Below Bound Bound Below Bound Bound Below Bound Bound(1) (2) (3) = (2) - (1) p-value (4) (5) (6) (7)
AA+ 2.1% 1.7% -0.4% 0.257 1.4% 1.5% 202 519AA 1.5% 1.7% 0.2% 0.636 1.0% 1.4% 936 271AA- 1.2% 1.4% 0.2% 0.465 0.9% 1.2% 822 522A+ 1.5% 1.8% 0.3%*** 0.007 0.9% 0.7% 1,317 2,380A 2.1% 1.6% -0.5%*** 0.003 1.0% 1.0% 1,111 1,713A- 1.9% 1.5% -0.4%** 0.043 1.2% 1.2% 2,088 1,140BBB+ 2.3% 2.0% -0.3% 0.210 1.4% 1.3% 3,157 1,303BBB 2.6% 2.3% -0.3% 0.196 1.3% 1.7% 2,677 1,688BBB- 2.8% 2.9% 0.1% 0.842 1.4% 1.9% 1,292 1,653BB+ 4.2% 3.2% -1.0%** 0.028 2.7% 2.2% 964 970BB 5.3% 3.3% -2.0%*** 0.000 3.4% 1.6% 1,590 644BB- 5.9% 3.9% -2.0%*** 0.001 3.3% 3.3% 1,833 598B+ 6.8% 5.1% -1.7%** 0.027 3.7% 3.4% 1,027 633B 8.8% 7.0% -1.8%** 0.021 3.8% 3.8% 714 528B- 7.6% 5.6% -2.0%** 0.014 3.8% 3.5% 254 175<= CCC+ 9.6% 5.7% -3.9%*** 0.003 4.7% 4.2% 215 200
All 3.5% 2.5% -0.9%*** 0.000 3.0% 2.3% 20,211 14,938
Yield spread: Summary Statistics by Corporate Rating and Bound StatusStd. Deviation Number of observations
Corporate Rating
MeanDifference
35
Table 3: Financial Ratios Summary Statistics: Bound vs. Non-Bound FirmsThis table reports annual financial ratios sample statistics for bound and non-bound firms. Standard errorsare clustered by firm; * indicates significance at the 10% level, ** at the 5% level, and *** at the 1% level.
Below Bound Bound(1) (2) (3) = (2) - (1) p-value
Bond-level characteristicsYrs to Maturity at issuance 10.431 10.453 0.022 0.973ln(Issue Amount) 19.291 19.419 0.128 0.208Amount Issued (USD millions) 385.097 436.393 51.296* 0.08
Firm-level characteristicsBV Debt / BV Assets 0.557 0.602 0.045* 0.065Net Income / Assets 0.003 0.025 0.022 0.502EBITDA / Assets 0.162 0.216 0.054 0.222Capex / Assets 0.078 0.085 0.007 0.327Total Debt Issuance / Assets 0.032 0.031 -0.001 0.924
Difference
36
Table 4: Country coverageThe first two columns in this table show the number of unique corporate bonds and firms by country usedin the sample. Columns 4, 5 and 6 report the number of total long-term foreign-currency (LT FC) sovereignrating changes by credit rating agency in each country.
S&P Moody’s FitchArgentina 64 28 12 4 1Australia 197 28 2 1 1Austria 14 3 1 0 0Azerbaijan 1 1 1 1 0Bahrain 3 3 1 2 1Belgium 3 1 1 1 3Bermuda 59 19 1 1 2Bolivia 1 1 3 3 2Brazil 194 71 7 7 8Canada 56 36 1 2 2Chile 128 25 3 3 2Hong Kong 30 17 4 3 1Colombia 7 3 4 3 4Croatia 4 2 1 0 1Cyprus 9 5 9 6 3Czech Republic 6 2 2 1 3Denmark 3 3 1 1 1Dominican Republic 2 1 3 2 1El Salvador 2 1 1 3 0Finland 8 3 2 0 0France 178 30 1 1 0Georgia 1 1 2 0 1Greece 2 2 4 2 4India 20 11 2 2 2Indonesia 15 6 12 6 6Ireland 115 34 6 5 3Israel 17 1 2 2 1Italy 16 4 4 4 1Jamaica 3 2 5 1 1Japan 70 29 5 3 1Kazakhstan 48 12 8 4 6Lebanon 1 1 6 4 2Malaysia 25 8 3 3 3Malta 2 1 2 4 0Mexico 166 47 5 1 5New Zealand 13 5 1 1 1Panama 7 4 4 2 2Peru 10 5 5 4 5Philippines 36 10 4 5 1Republic of Korea 278 44 5 6 4Russian Federation 7 7 8 4 7Singapore 13 4 0 1 1Slovakia 1 1 0 4 4South Africa 10 5 2 3 0Spain 27 9 8 4 2Sweden 24 9 1 2 2Thailand 14 9 2 2 4Turkey 10 5 7 2 7Ukraine 2 2 7 1 1Venezuela 11 4 10 2 5Viet Nam 2 1 1 1 1
Total 1,935 566 192 130 119
Number of LT FC Sovereign Rating ChangesNumber of Firms
Number of Corporate Bonds
Country
37
Table 5: Predicted Rating by Sovereign Bound StatusThis table reports the estimated coefficients from the regression model in eq. 2, which measure the effect ofbeing bound by the sovereign ceiling on a firm’s predicted rating:
Rtgi,t = β0 + β1RtgFEi,t + β2(RtgFEi,t ∗Boundi,t) + Sov.RatFEi,t + TimeFEt + εi,t
where β2 is a coefficients vector that captures the differential effect, for each rating level, of beingbound by the sovereign ceiling on a firm’s predicted rating. I weigh observations based on the number ofbonds observed each year for each firm. Standard errors clustered by firm.
Below Bound Bound(1) (2) (3) = (2) - (1) P-value
AA+ 14.25 15.26 1.01*** 0.003AA 15.89 15.12 -0.77*** 0.010AA- 16.06 15.95 -0.11 0.668A+ 14.64 16.40 1.76*** 0.000A 14.43 15.77 1.34*** 0.000A- 14.47 15.25 0.79*** 0.000BBB+ 14.73 15.14 0.41** 0.034BBB 14.63 15.23 0.61*** 0.001BBB- 14.06 15.10 1.04*** 0.000BB+ 13.57 15.17 1.6*** 0.000BB 13.62 14.87 1.25*** 0.000BB- 13.17 14.24 1.06** 0.044B+ 12.17 13.29 1.11*** 0.000B 11.07 13.06 1.99*** 0.001B- 10.66 13.28 2.62*** 0.000
Difference between "Bound" Predicted Rating by Bound Status
38
Table 6: Default and Bound Status: Logit Regression of “Close-to-Default” by Ratingand Bound StatusThis table reports the estimated coefficients from the logit model in eq. 3, which measure the effect of beingbound by the sovereign ceiling on a firm’s probability of transitioning into a “Close-to-Default” status:
Close-to-defaulti,j,[t,t+T ] = β0 +β1RtgFEi,j,t +β2(RtgFEi,j,t ∗Boundi,j,t)+SovRatFEi,t +TimeFEt + εi,j,t
where β2 is a coefficients vector that captures the differential effect, for each rating level, of beingbound by the sovereign ceiling on a firm’s predicted rating. I weigh observations based on the number ofbonds observed each year for each firm. Standard errors clustered by firm.
Coeff. S.E. p-valueA+ 2.953 0.759 0.000A -0.609 0.860 0.479A- 2.168 1.131 0.055BBB+ 3.525 0.736 0.000BBB 3.137 0.628 0.000BBB- 3.321 0.678 0.000BB+ 4.038 0.714 0.000BB 5.350 0.634 0.000BB- 5.519 0.621 0.000B+ 6.032 0.626 0.000B 6.322 0.621 0.000B- 7.584 0.624 0.000
Bound * A+ 0.000 . .
Bound * A 4.319 0.772 0.000
Bound * A- -0.327 0.991 0.741
Bound * BBB+ -3.113 0.796 0.000
Bound * BBB -1.143 0.517 0.027
Bound * BBB- -1.523 0.488 0.002
Bound * BB+ 0.104 0.620 0.866
Bound * BB -0.368 0.463 0.427
Bound * BB- -1.212 0.646 0.061
Bound * B+ -0.525 0.439 0.232
Bound * B -0.916 0.493 0.063
Bound * B- -2.250 0.703 0.001
Sovereign Rating FE Yes
Observations 16,459
Number of firms 556
Pseudo R2 0.2895
Outcome: Close-to-Default(5-year realization window)
39
Table 7: Default and Bound Status: Marginal Effects from Logit Regression of “Close-to-Default” by Rating and Bound StatusThis table reports the predicted probabilities (marginal effects) from the logit model 3, and whose estimatedcoefficients are reported in table 6. The marginal effects indicate the predicted probability of a firm witha given rating transitioning into “Close-to-Default” in a 5-year window, depending on whether the firm isbound or not by the sovereign ceiling.
Non-Bound Bound Difference p-valueA 0.01% 0.74% 0.73% 0.030 A- 0.16% 0.11% -0.04% 0.775 BBB+ 0.61% 0.03% -0.59% 0.020 BBB 0.42% 0.13% -0.28% 0.012 BBB- 0.50% 0.11% -0.39% 0.035 BB+ 1.02% 1.13% 0.11% 0.867 BB 3.69% 2.59% -1.11% 0.376 BB- 4.34% 1.33% -3.01% 0.004 B+ 7.05% 4.29% -2.75% 0.168 B 9.21% 3.90% -5.31% 0.015 B- 26.37% 3.64% -22.73% 0.000
Predicted Probability of Close-to-Default (5-year window)
40
Table 8: Corporate Yield Spreads and Bound StatusThis table reports the estimated coefficients from the regression model in eq. 4, which measure the effect ofbeing bound by the sovereign ceiling on a firm’s predicted rating:
Spreadi,j,t = β0 + β1RtgFEi,j,t + β2(RtgFEi,j,t ∗Boundi,j,t) + Sov.RatFEi,t + TimeFEt + εi,j,t
where β2 is a coefficients vector that captures the differential effect, for each rating level, of beingbound by the sovereign ceiling on a bond’s yield spread. I weigh observations based on the number of bondsobserved each month for each firm. Standard errors clustered by firm.
Coeff. S.E. p-valueAA+ -0.771 0.567 0.174AA -0.462 0.550 0.401AA- -0.216 0.557 0.697A+ -0.399 0.532 0.453A -0.085 0.505 0.866A- 0.074 0.531 0.890BBB+ 0.552 0.529 0.297BBB 0.828 0.536 0.122BBB- 1.345 0.535 0.012BB+ 2.636 0.556 0.000BB 3.163 0.545 0.000BB- 4.070 0.541 0.000B+ 5.258 0.553 0.000B 6.299 0.574 0.000B- 7.391 0.596 0.000
Bound * AA+ -0.226 0.272 0.406Bound * AA 0.406 0.491 0.409Bound * AA- -0.072 0.296 0.807Bound * A+ 0.286 0.105 0.007Bound * A 0.061 0.131 0.644Bound * A- 0.080 0.102 0.431Bound * BBB+ -0.282 0.119 0.018Bound * BBB -0.194 0.158 0.219Bound * BBB- -0.330 0.117 0.005Bound * BB+ -0.878 0.289 0.002Bound * BB -1.210 0.262 0.000Bound * BB- -2.023 0.362 0.000Bound * B+ -0.694 0.773 0.370Bound * B -0.801 0.491 0.103Bound * B- -1.303 0.513 0.011
Sovereign Rating FE YesTime FE YesObservations 162,140R2 0.657
Outcome: Spread
41
Table 9: Effect of sovereign rating changes on corporate spread changes by “bound” statusThis table reports the estimation results of eq. 5. The dependent variable is the yield spread change of a corporate bond around the time windowspecified in each column. For instance, the dependent variable in column (1) is the change in the spread 1 month after the sovereign rating changeversus 1 before the event. Bound is a dummy variable that takes a value of one if a corporate issue has the same rating as the corresponding sovereignprior to a sovereign rating change. Standard errors are clustered by firm. t-statistics are reported in parentheses below coefficients estimates; *indicates significance at the 10% level, ** at the 5% level, and *** at the 1% level.
Panel A. Pooled downgrades and upgrades(1) (2) (3) (4) (5) (6)
1mPost−3mPre 2mPost−3mPre 3mPost−3mPre 4mPost−3mPre 5mPost−3mPre 6mPost−3mPre
Constant -0.026 -0.043 0.145 0.121 0.263 0.476*(-0.25) (-0.35) (0.91) (0.64) (1.29) (1.75)
Sov.Down 0.090 0.041 -0.061 0.004 -0.009 -0.191(0.49) (0.18) (-0.20) (0.01) (-0.03) (-0.51)
Sov.Down * Bound 1.031*** 0.777*** 1.171*** 1.213*** 1.226*** 0.882**(3.33) (3.51) (3.06) (3.14) (3.24) (2.52)
Sov.Up * Bound -0.271*** -0.240** -0.385*** -0.340** -0.449** -0.486**(-3.28) (-2.15) (-2.79) (-2.15) (-2.57) (-2.18)
Observations 2319 2245 2137 2066 1834 1687r2 0.0974 0.0554 0.0685 0.0708 0.0720 0.0323
Panel B. Pooled downgrades and upgrades with Country-Event Fixed Effects(1) (2) (3) (4) (5) (6)
1mPost−3mPre 2mPost−3mPre 3mPost−3mPre 4mPost−3mPre 5mPost−3mPre 6mPost−3mPre
Sov.Down * Bound 0.537*** 0.643*** 0.658** 0.723** 0.746** 0.768*(3.02) (3.16) (2.34) (2.30) (2.10) (1.72)
Sov.Up * Bound -0.122* -0.003 -0.129 -0.002 -0.141 0.042(-1.75) (-0.04) (-1.17) (-0.02) (-1.07) (0.47)
Country-Event FE Yes Yes Yes Yes Yes Yes
Observations 2319 2245 2137 2066 1834 1687r2 0.700 0.737 0.764 0.788 0.751 0.760
42
Table 10: Effect of corporate rating changes on corporate spreads: 2SLS using sovereign rating change * bound statusas an instrumentThis table reports the estimation results of eq. 5. The dependent variable is the yield spread change of a corporate bond around the time windowspecified in each column. For instance, the dependent variable in column (1) is the change in the spread 1 month after the sovereign rating changeversus 1 before the event. Bound is a dummy variable that takes a value of one if a corporate issue has the same rating as the corresponding sovereignprior to a sovereign rating change. Standard errors are clustered by firm. t-statistics are reported in parentheses below coefficients estimates; *indicates significance at the 10% level, ** at the 5% level, and *** at the 1% level.
(1) (2) (3) (4) (5) (6)1mPost−3mPre 2mPost−3mPre 3mPost−3mPre 4mPost−3mPre 5mPost−3mPre 6mPost−3mPre
∆Corp.Dwg (2SLS) 0.883*** 0.946*** 0.982*** 1.359*** 1.359*** 1.626***(4.86) (5.74) (3.58) (4.10) (4.21) (4.02)
∆Corp.Upg (2SLS) -0.310 -0.013 -0.366 0.007 -0.413 0.053(-1.54) (-0.06) (-1.13) (0.03) (-1.08) (0.28)
Country-Event FE Yes Yes Yes Yes Yes Yes
Observations 2319 2245 2137 2066 1834 1687r2 0.704 0.743 0.767 0.797 0.760 0.77243
Table 11: Effect on a Firm’s Investment and FinancingThis table reports the estimation results of eq. 5 for each of the corporate outcomes at the top of each column. Standard errors are clustered by firm.t-statistics are reported in parentheses below coefficients estimates; * indicates significance at the 10% level, ** at the 5% level, and *** at the 1%level.
Panel A. Sovereign downgrades(1) (2) (3) (4)
Capex LT Debt Iss. ST Debt Iss. Tot. Equity Iss.
∆Corp.Down (2SLS) -0.032** -0.030 0.005 0.011(-2.16) (-0.75) (0.98) (0.59)
Country-Event FE Yes Yes Yes YesSector FE Yes Yes Yes Yes
Observations 164 147 147 88r2 0.460 0.501 0.206 0.437
Panel B. Sovereign upgrades(1) (2) (3) (4)
Capex LT Debt Iss. ST Debt Iss. Tot. Equity Iss.
∆Corp.Up (2SLS) 0.022 0.006 0.005 0.006(1.39) (0.19) (0.37) (0.37)
Country-Event FE Yes Yes Yes YesSector FE Yes Yes Yes Yes
Observations 659 591 599 444r2 0.473 0.299 0.235 0.329
44
Table 12: Falsification test: Effect of the sovereign ceiling on corporate spread changes one year before the actual eventThis table reports falsification tests for the regressions in table 9. Specifically, the same regressions are estimated one year before each actual sovereigndowngrade or upgrade. The dependent variable is the yield spread change of a corporate bond around the time window specified in each column.Bound is a dummy variable that takes a value of one if a corporate issue has the same rating as the corresponding sovereign prior to a sovereignrating change. Standard errors are clustered by firm. t-statistics are reported in parentheses below coefficients estimates; * indicates significance atthe 10% level, ** at the 5% level, and *** at the 1% level.
(1) (2) (3) (4) (5) (6)1mPost−3mPre 2mPost−3mPre 3mPost−3mPre 4mPost−3mPre 5mPost−3mPre 6mPost−3mPre
Sov.Down * Bound 0.146 0.134 0.164 0.138 0.266 0.105(0.61) (0.50) (0.75) (0.63) (0.70) (0.29)
Sov.Up * Bound -0.221 -0.323 -0.330 -0.287 -0.187 -0.228(-1.63) (-1.36) (-1.52) (-1.45) (-0.90) (-1.09)
Country-Event FE Yes Yes Yes Yes Yes Yes
Observations 1333 1295 1221 1162 1020 934r2 0.508 0.521 0.523 0.536 0.551 0.616
45
Table 13: Falsification Test: Effect on a Firm’s Investment and FinancingThis table reports falsification tests for the downgrade regressions in table 11. Specifically, the same regres-sions are estimated one year before each actual sovereign downgrade. Standard errors are clustered by firm.t-statistics are reported in parentheses below coefficients estimates; * indicates significance at the 10% level,** at the 5% level, and *** at the 1% level.
(1) (2) (3) (4)Capex LT Debt Iss. ST Debt Iss. Tot. Equity Iss.
∆Corp.Down (2SLS) -0.024 -0.024 -0.008 0.025(-1.02) (-0.50) (-0.59) (0.68)
Country-Event FE Yes Yes Yes YesSector FE Yes Yes Yes Yes
Observations 81 77 77 38r2 0.434 0.373 0.0741 0.323
46
A First Appendix
Figure A.1: Issuance of rated USD corporate bonds by non-US firms per year.This figure plots the annual total issuance of rated USD-denominated bonds by non-US firms. Own calcu-lations using data from Bloomberg.
0
100
200
300
400
500
600
US
D B
illio
ns
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Issuance of Rated USD Corporate Bonds by Non-US Firms
47
Figure A.2: Average Corporate Spread by Broad Credit Rating Category.This figure plots the average yield spread of USD-denominated corporate bonds issued by non-US firms.The yield spread for each bond is calculated by subtracting the yield of a Treasury bond with an equivalentmaturity. Each line represents the monthly average of all corporate yield spreads within each broad ratingcategory.
0
5
10
15
20
%
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
B BB BBB A AA AAA
Average Spread by Broad Rating Category
48
Figure A.3: Distribution of sovereign ratings by rating category.The figures in Panel A and B depict the distribution of long-term foreign-currency (LT FC) sovereign ratingsand rating changes for the countries and period in the sample for S&P, Moody’s and Fitch.
0.0
5.1
.15
.2D
ensi
ty
D/C CC
CC
C-
CC
C
CC
C+ B- B
B+
BB
-
BB
BB
+
BB
B-
BB
B
BB
B+ A- A
A+
AA
-
AA
AA
+
AA
A
S&P Sovereign FC LT Rating0
.05
.1.1
5.2
Den
sity
D/C CC
CC
C-
CC
C
CC
C+ B- B
B+
BB
-
BB
BB
+
BB
B-
BB
B
BB
B+ A- A
A+
AA
-
AA
AA
+
AA
A
Moody's Sovereign FC LT Rating
0.0
5.1
.15
.2D
ensi
ty
D/C CC
CC
C-
CC
C
CC
C+ B- B
B+
BB
-
BB
BB
+
BB
B-
BB
B
BB
B+ A- A
A+
AA
-
AA
AA
+
AA
A
Fitch Sovereign FC LT Rating
49
Figure A.4: Distribution of sovereign rating changes by year.The figures in Panel A and B depict the distribution of long-term foreign-currency (LT FC) sovereign ratingsand rating changes for the countries and period in the sample for S&P, Moody’s and Fitch.
0
10
20
30
Fre
quen
cy
1999 2001 2003 2005 2007 2009 2011
S&P Sovereign FC LT Rating
0
10
20
30
Fre
quen
cy
1999 2001 2003 2005 2007 2009 2011
Moody's Sovereign FC LT Rating
0
10
20
30
Fre
quen
cy
1999 2001 2003 2005 2007 2009 2011
Fitch Sovereign FC LT Rating
Sovereign Downgrades per Year
0
10
20
30
Fre
quen
cy
1999 2001 2003 2005 2007 2009 2011
S&P Sovereign FC LT Rating
0
10
20
30
Fre
quen
cy
1999 2001 2003 2005 2007 2009 2011
Moody's Sovereign FC LT Rating
0
10
20
30
Fre
quen
cy
1999 2001 2003 2005 2007 2009 2011
Fitch Sovereign FC LT Rating
Sovereign Upgrades per Year
50
Table A.1: Credit ratings numerical conversion by CRACredit ratings are translated into the following numerical rating scale ranging from 1 (C/D) to 21 (AAA).
Numerical Rating S&P Moody’s Fitch21 AAA Aaa AAA20 AA+ Aa1 AA+19 AA Aa2 AA18 AA- Aa3 AA-17 A+ A1 A+16 A A2 A15 A- A3 A-14 BBB+ Baa BBB+13 BBB Baa BBB12 BBB- Baa BBB-11 BB+ Ba1 BB+10 BB Ba2 BB9 BB- Ba3 BB-8 B+ B1 B+7 B B2 B6 B- B3 B-5 CCC+ Caa1 CCC+4 CCC Caa2 CCC3 CCC- Caa3 CCC-2 CC Ca CC1 C/D C C /D
51
Table A.2: Estimation of coefficients from firms in AAA countries to predict firmratings in non-AAA countriesThis table reports the estimation results of model 1. t-statistics are reported in parentheses below coefficientsestimates; * indicates significance at the 10% level, ** at the 5% level, and *** at the 1% level.
Pooled OLS downgrades and upgrades(1)
Corp. Rating
Constant 0.633(0.15)
Net Income / Assets 5.852***(3.66)
Debt / Total Capital -0.962***(-3.07)
ln(Assets) 2.876***(4.32)
Square of Net Income / Assets 5.298(1.53)
Square of Debt / Total Capital 0.030***(3.58)
Square of ln(Assets) -0.101***(-3.34)
Time FE YesSector FE YesCollat. FE Yes
Observations 17191r2 0.515
52
Recommended