An analysis of sovereign credit ratings
Sovereign Credit Ratings
Niall Murray
November 2018
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Introduction
Following a review of various papers centered around the determination of credit
ratings I decided to review Short and Long-run Determinants of Sovereign Debt
Credit Ratings written by Afonso, Gomes and Rother.
My motivation for covering this paper stems from my interest in the financial
markets and the important role which sovereign credit ratings serve. Inflated
credit ratings played big role in misleading investors in the financial crisis of
2008, the ten year anniversary of this disaster reminds us of the importance of
understanding what actually drives these ratings. Using various types of statistical models from pooled OLS to random effects ordered probit estimation,
the authors have uncovered which model is the most suitable for this data and
what metrics agencies like Moodys and Fitch tend to use in their models. As
well this....
Sovereign credit ratings crucial role in the financial system can broken down
as follows:
• The primary objective of sovereign ratings is their role in influencing the
interest rates at which countries can obtain credit on the international
financial markets. This rate is then the basis for which individuals in that
country can then borrow and lend at.
• Sovereign ratings also influence credit ratings of national banks and companies based in that country. Agencies very rarely assign a credit rating
to a bank, company or local municipality that is higher than that of the
home country of the issuer (Cantor and Packer, 1996)and affect their attractiveness to foreign investors by directly impacting the ability of firms
in that country to access global capital markets.
• Many institutional investors can only invest in debt rated at a certain
level, any rating below these thresholds may severely limit the flow of
capital to a nation. Thus, sovereign credit ratings are strong predictors of
a country’s equity market returns and valuations this was documented by
(Correa et al. 2014).
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Summary
The researchers sought out to analyze what indicators influence sovereign debt
ratings and their effects on a short and longer term time horizon. To do this
they utilised linear and ordered response models.
The linear panel model used is:
Rit = BXit + λZi + αi + uit
(1)
Where Rit represents a linear transformation of credit ratings with AAA=17,
Xit is a vector containing time varying variables and Zi is a vector of time
invariant variables which include regional dummies. Finally αi stands for the
individual effects for each country i. A variety of models such as random effects, fixed effects and pooled OLS were considered to estimate this equation.
They had suspicions that the country specific error is likely correlated with the
regressors due to factors such as political turmoil, poor infastructure or nations
tax policy. There suspicions were confirmed by using a Hausman test where
the null hypothesis of no correlation is rejected with p-value of 0.00. With the
knowledge that E(αi |Xit , Zi ) 6= 0, one would think you should use fixed effects
as both random effects and pooled OLS produce biased and inconsistent estimates under these conditions. Conditional Mean zero assumption has
been violated
However due to nature of the data, specifically the lack of variation of a country’s
rating over time meant that the geographic dummies included in the regression
would capture the country’s average rating, while all the other variables would
only capture movements in the ratings across time. Essentially leaving the fixed
effects regression stripped of meaning.
This led the researchers to use random effects and try to deal with the correlation between the αi and the regressors. They highlighted two methodologies to
go about this, Hausman-Taylor IV and modelling αi . They decided to model the
error term αi as Hausman-Taylor IV estimation would have been a ”Herculean
task”.GIVE COMMENTARY They implement this by setting the expected
value of the country specific error as a linear combination of time-averages of
the regressors X̄i .
E(αi |Xit , Zi ) = η X̄i
(2)
They modify there initial equation (1) with: αi = η X̄i + εi
And after rearranging some terms this produces:
Rit = B(Xit − X̄i ) + δ X̄i + λZi + εi + uit
(3)
Where δ = η + B and εi is an error term which is by definition uncorrelated
with the regressors. This expression is formulated well as we can think of B and
δ as our short term and long term coefficients, B represents the impact of the
explanatory variables changing throughout time while δ tracks the long-term
effects of our variables.
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The ordered probit model:
∗
Rit
= B(Xit − X̄i ) + δ X̄i + λZi + εi + uit
(4)
∗
The unobserved latent variable Rit
represents each agencies continuous evaluation of a nations’s credit-worthiness. An ordered probit is a more natural fit
for this type of problem, because the rating is a discrete variable and reflects an
order in terms of probability of default.
The variables c1 to c16 found in (5) represent the threshold parameters,
these parameters are limited by the number of credit ratings. This transformation differs significantly from the linear transformation used in the previous
models. Utilizing this methodology we get to uncover the shape of ratings curve
which is rather illuminating as it tells us whether the system is non-linear or
whether one agency may require bigger changes then others. The parameters
of equation (4) and (5), specifically B, η , λ and the threshold parameters c1 to
c16 are estimated using maximum likelihood. A random effects ordered probit
model is used, as it considers both errors εi + uit to be normally distributed.
GIVE COMMENTARY This allows one to consistently estimate the parameters.
Using previous literature on this subject such as Cantor Packer (1996) and
Bissoondoyal-Bheenick (2005), they identified some key variables which may
influence sovereign credit ratings. The regressors were then split into four sections.
Macroeconomic
GDP per capita
Real GDP growth
Unemployment
Inflation
Government
Government debt
Fiscal balance
Government effectiveness
External
External debt
Foreign reserves
Current account balance
Other
Default history
European Union
Regional dummies
The ratings data was gathered from Standard Poor’s, Moody’s and Fitch Ratings. In an effort to get more comparable figures fiscal balance, current
account and government debt are calculated as a percentage of GDP,
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foreign reserves are as percentage of imports and external debt as percentage of exports. 3-year averages were taken for the following variables;
inflation, unemployment, GDP growth, fiscal balance and current account. The
long term average of these figures are used to mirror the rating agencies focus
to lessen the impact of the business cycle on a sovereign rating.Unfortunately
due to patchy data they end up with an unbalanced panel with 66 countries for
Moody’s, 65 for SP and 58 for Fitch, with an average 8 yearly observations per
country.
Table 1: Results: Significant at 5% level for at least two agencies
Short Term
GDP per capita
Real GDP growth
Government debt
Fiscal Balance
Long Term
GDP per capita
Default history
Government effectiveness
Inflation
External debt
Creating the model with both short and long term coefficients proved to be
a success as the vast majority of variables were found to have different effects
compared to their long term average. The long term average of variables such
as GDP per capita and government effectiveness were found to be significant for
each agency which means if they hadn’t included them in the regression they
would have suffered from issues of endogeneity. As well as this they used a Hausman test to confirm that the country specific error was now uncorrelated with
the explanatory variables. The standout variable form these results is GDP per
capita which is significant at 5% level for all agencies across short and long term
horizons, indicating what a big role it plays in the determination of these ratings.
The random effects ordered probit results were quite similar to that of the linear
random effects model with the main difference being additional variables were
found to be significant for Fitch, this is likely due to the standard errors being
smaller with this model. Ordered probit is more efficient?? Could Fitch
having the least data play a role or the fact that this model produces
lower standard errors? The key drivers on the short term side were GDP per
capita, real GDP growth, government debt which were found to be significant
for all agencies.
Surprisingly short-term unemployment has a positive impact SP scores at 1%
significance level, while long term unemployment has a negative impact for the
two other agencies at a significant level which is more in line with my expectations. The short term result seems rather unintuitive to me as unemployment
is usually associated with economic downturns, however after further reading
a short term rise in unemployment may be a consequence of additional people
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looking for a job indicating people are positive about the future or that an economy may be experiencing substantial technological innovation which should be
followed by prosperous economic growth. The contrast in outcomes between
the short and long term coefficients is fascinating and causes one to think more
deeply about the specific variable.
The assumption that the ratings schedule was linear proved to be reasonable as for the most part the ratings are evenly spaced between each other. SP
were found to have highest value for their final threshold parameter indicating an AAA rating from them may be slightly more difficult to obtain. The
gap between parameters of junk (BB+) and investment grade bonds(BBB-) is
marginally bigger than the average leap between ratings, this is a result I expected and could be explained by the stark consequences of moving between
those ratings. Many pensions and other financial institutions are not permitted
to invest into debt which is rated below investment grade.
The final section and perhaps the most intriguing involves predicting the ratings and the changes of a rating for each agency. The table above highlights
the importance of controlling for country-specific effects as we see random effects including the estimated country effect is the most successful model. It
correctly forecasted 70 per cent of all observations and more than 95 per cent
of the predicted ratings lie within one notch (one notch is difference between
two adjacent ratings say AAA to AA+). This result comes as no surprise as I
mentioned above factors such as political risk, geopolitical uncertainty and tax
policies are contained in this fixed effect term which boosts the accuracy of the
model. For me this result points towards the rating agencies focus on including
the qualitative variables likely to be contained in the error term when building
their proprietary models.
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Excluding the error term, simple ordered probit came out on top as far as prediction is concerned as it correctly forecasts around 45 per cent of all observations
and more then 80 per cent within one notch, random effects ordered probit was
slightly behind correctly forecasting 40% and with 78% falling within one notch.
COMMENTARY When it came to forecasting a change in a nations rating
the models correctly predict between one third and one half of both upgrades
and downgrades. Ordered probit and random effects ordered probit predict
significantly more changes than the OLS and random effects counterparts.
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What I liked
I liked the use of three agencies, as it allowed one to compare and contrast
the methodologies of each firm. Although the results seemed pretty consistent
across the board they did diverge on a few variables. The EU dummy is one of
them, with Moodys placing a much larger emphasis on it compared to the rest.
The formation of the SP threshold parameters was very revealing, the space
between each parameter was wider at very top compared to elsewhere on the
schedule indicating they require big moves in the fundamentals of a nation to
obtain the best ranking. Also we see Fitch place a higher emphasis on average
current account deficits and average government balances, its coefficients for
these variables are twice the size of their competitors indicating Fitch is more
concerned with fiscal responsibility.
The process of altering the random effects model was another interesting factor
of this paper. I had become accustomed to thinking fixed effects or first difference models were always the way one should deal with correlation between
αi and explanatory variables, and they would have went down that road if the
data had permitted them but due to stable nature of these ratings they had to
think out side the box. RE usually needed the assumption cov(Xit , αi ) = 0,
estimating RE without this key assumption was a surprise, and open my mind
to how flexible some of these models can be.
Another benefit that came with the alterations they made was the level of
granularity now offered by model. Splitting the model into short and long term
coefficients allows one to see what variables matter and also how they will influence a credit score over time. The current account variable is a good example
of this, its short term coefficient is negative while its long term is a large and
positive coefficient. So countries who run deficits in the short term are rewarded
while those who have a sustained deficits are punished severely, this nuanced
information would be crucial for any practitioner involved with investing fixed
income. Another feature of this model is that it allows us to somewhat explain
the stability of the ratings, we see the long term coefficient of a variable often
have greater magnitude then their short term counterpart. If the opposite was
the case I think we would see far more changes from the rating agencies. This
then brings up the question why do the focus on stable ratings?
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What could be Improved
Since there is a large set explanatory which may be correlated with each other
the results of these regressions may be adversely affected by multicollinearity.
To remedy this issue other researchers such as Teker, Pala and Kent (2013)
and Sehgala,Mathurb, Arorab and Guptab (2018) utilised Principal component
analysis or factor analysis. PCA allows one to discover if the regressors can
be mostly explained in terms of a much smaller number of variables which are
called factors. This allows one to reduce the dimensionality of the data set
and to identify meaningful underlying factors. In this case it’s possible the researchers were less concerned with the coefficients of the regressions and placed
greater emphasis on the prediction results thus they neglected any potential
issues which may arise from multicollinearity.
I think a greater sample period would allow for increased confidence in their
results. During their sample Moody’s was twice as likely to upgrade a rating
rather than downgrade, this begs the question is Moody’s overly kind with their
ratings or was it the case that this period was time of great economic prosperity
and thus downgrades were few and far between.
The use of an artificial neural network (ANN) may be better suited to dealing with non-linear relationships, Benell, Crabbe, Thomas and Gwilym (2006)
showed that an ANN model is superior to ordered probit model when it comes
to modelling sovereign credit ratings as it had increased accuracy when it comes
to forecasting. Results like that point towarsd the fact the rating agencies are
using neural networks themselves to generate these scores.
Use of another model? or any other model provide a better fit?
Split the ratings up and see if it performed better for investment grade ratings...
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