ordered response comparison between fitch, standard & poor, moody ratings.pdf

7/28/2019 Ordered Response Comparison between Fitch, Standard & Poor, Moody Ratings.pdf

1/12


2/12Electronic copy available at: http://ssrn.com/abstract=2253659

interview with top executives and divisional chiefs of bond issuers. Also, that of quantitativeaspects was said to be done by analyses using financial data.According to the website of S&P,the agency said that the credit ratings were based on the information offered by bond issuersand on other information sources which they deemed reliable to use.

However the agencies have never revealed entire mechanisms of how they decided credit

ratings. In other words, we cannot know how many contributions qualitative factors make indetermining credit ratings but can research to what extent publicly available financial datacontributes in determining credit ratings. Thus, there have been many studies aimed atexploring quantitatively the determinant factors of credit ratings.

Credit ratings are ordinal measures of through-the-cycle expected loss. As such, while they arecertainly based on the current financial strength of the issuer, they incorporate expectations offuture performance as well - not just issuer performance, but the industry and overall economy.Ratings also measure the relative permanence or stability of the issuer's financial position:fleeting or noisy disturbances, even those which might be reflected in bond spreads, do notimpact credit ratings. Consequently, while we can hope to construct a "good" and "useful"mapping between conventional financial metrics and ratings, we know from the outset that we

can never construct a perfect map, since we simply cannot include all the factors whichdetermine ratings.

This has not prevented the development of a variety of rating prediction models, both byacademics and industry practitioners. They generally fall into two types: linear regression andordered probit/Logit . Basic linear regression projects ratings (usually measured in linear ornotch space, for instance with Aaa = 1 and C = 21) on various financial metrics.The result is alinear index with fixed coefficients which maps directly to rating space. The ordered probit (orlogit) relaxes the assumption of a linear rating scale by adding endogenously determined "breakpoints" against which a similar fixed coefficient linear index is measured.In this paper I havefocused on Ordered response Functions only .

A public finance credit risk rating is an opinion about a local governments ability and willingnessto pay. With no credit rating these two attributes would be difficult to evaluate in Mexico due to

the lack of timely and reliable information about public finances. As the events in the ongoing

worldwide crisis have revealed, failing to assess credit quality based on quality information can

lead to financial bankruptcy, default, crisis and contagion. Despite the heavy criticism to rating

agencies, credit risk ratingswith all their imperfectionsare tools still widely considered by

analysts and are among the very few parameters available to monitor the health and soundness

of local government's public finances.Commercial banks and financial creditors for instance use

risk ratings as a benchmark to calculate capital reserves and to manage default risk. The bigger

the gap between the State credit risk rate and the sovereign risk rate, the bigger will be the

required reserve capital and, therefore, a higher interest rate the local government will be

charged for such credit.

Credit rating agencies such as Fitch, Moody's and Standard & Poors have been issuing ratings

for corporate debt since the first half of the 20th century. Today, the majority of large companies

in developed markets possess a credit rating. Over time, the rating process has not undergone

major changes. Rating agencies combine quantitative information such as accounting ratios

with qualitative assessments of management quality and other factors. The final rating decision


3/12

is made by a rating committee. According to rating agencies, it does not rest on a fixed

weighting algorithm (Standard and Poors (2008)).

In this paper i have tried to see the results from all three approach and determined what are the

factors which relates to this significantly. In my results I have seen that in some of the factors

affects one rating significantly but not the others.

Objectives:

1.Does the parameters using all three different companys ratings make significant difference on

the explanatory variables?

2. Which one is closely related to the actual rating of the country in their domain?

2. Literature Review:

There have been done so many studies related to defining the Debt /Credit ratings of a country.

I have studied some of the studied related to this and found some interesting results .

Determinants and impact of sovereign credit ratings by Cantor, R., Packer, F. (1996) . In

this paper they may be regarded as an earlier study in this area, analyzing the determinants and

impact of sovereign credit ratings using a cross-section of 49 countries by applying OLS

methodology. In their analysis, six factors appear to play an important role in determining a

countrys rating: per capita income, GDP growth, inflation, external debt, level of economic

development, and default history. Their findings do not support a statistically significantrelationship between ratings and either fiscal or current account deficits. In fact, the empirical

literature on sovereign ratings only extends in a few strands.

The study of Afonso (2003) Understanding the determinants of sovereign debt ratings:

Evidence for the two leading agencies examines possible determinants of sovereign credit

ratings assigned by Moodys and the S&P for a sample of 81 countries consisting of 29

developed and 52 developing countries for the year 2001 by using the OLS method. Rating

scales are transformed by using linear, logistic and exponential transformations. The variables

that have statistically significant explanatory power for the rating levels are GDP per capita,

external debt as a percentage of exports, the level of economic development, default history,

real growth rate, and the inflation rate. The results of the logistic transformation estimationsappear to be better for the overall sample s, especially for the countries located at the top end

of the rating scale.

Butler and Fauver(2006) Institutional environment and sovereign credit ratings examine

the cross-sectional determinants of sovereign credit ratings by using a sample of 86 countries

as of March 2004. The main findings of the study display that the quality of a countrys legal and


4/12

political institutions, which are measured by its rule of law, political stability, voice of the

people, corruption control, government effectiveness, or regulatory quality, has a vital role in

determining these ratings. Strikingly, credit ratings are found to be over three times as sensitive

to a change in the legal environment composite as they are to GDP per capita, inflation, foreign

debt per GDP, and overall economic development. Linear panel models generalizing a cross

section specification to panel data are also used by Monfort and Mulder (2000) Using CreditRatings for Capital Requirements on Lending to Emerging Market Economies: Possible

Impact of a New Basel Accord, Eliasson (2002) Sovereign credit ratings. Deutche Bank

Research, and Canuto et al. (2012) Macroeconomics and sovereign risk ratings in their

papers .

PROVINCIAL CREDIT RATINGS IN CANADA: An Ordered Probit Analysis by Stella

Cheung In this paper they have tried to see the effect of variables on rating using some

independent variables like debt to GDP ratio ,last year employment ratio ,federal transfer as

proportion of total provincial revenues provincial GDP as a share of total Canadian GDP.They

have used the 25 years data from 1970 to 1995.They found that a number of other variables aresignificant such as the employment ratio, the relative size of the provincial economy, the

governments dependence on federal transfers, and the relative importance of UI benefits.

Determinants and Impact of Sovereign Credit Ratings by Richard Cantor and Frank

Packer.In this paper they tried to focus on basic variables (GDP per capita,GDP growth

,Inflation, Fiscal Balance,External balance )that affects the credit ratings and also compared the

moodys and S&P ratings.They found all the variables significant in 10% significant level.They

have used 79 observations.There was not that huge difference between these two ratings

results.

As we can see that there has been done enough work using panel data ,cross sectional data

,time series data with different estimation method like OLS and etc. There have been also done

many similar kind of studies using different sample size so. it can be easily said that Sample

size is the important factor to obtain realistic Credit ratings.

3. ESTIMATED MODEL & METHODOLOGY and DATA Sources:

Model: I am using the model which is given below for determining the relationship between

independent and dependent variables.

Latent Variable (unobserved)-

Y* = a0+a2*GDPG +a3*Infl +a4*Fisbal+a5*Extbal +a6*Extdebt+a7*Ecodvlpmt +a8*Deflthist+ a9*EU

+error

Initially I also took GDP per capita in my model but because of collinearity in GDP per capita

and GDP growth I dropped GDP per Capita. So finally I have taken few variables (GDP

growth,Inflation rate ,External balance,Fiscal balance,External debt,dummies for European

countries, Economic development or Industrialized country and Default history) for the


5/12

regression as my independent variables which comes from literature for the 79 countries sample

size . I have taken different Ratings as my dependent variables using ordinal function.

4. Defining the Variables:

To estimate the relationship between different ratings and independent variables I am using

Maximum likelihood estimation with cross sectional data in ordered logit /Probit Models.

Basic Ordered Logit/Probit Models:

I have shown only Moodys ratings in form of ordered logit /Probit but we can also define the other

Ratings models like Standard & Poor, Fitch in the same way. The only difference that we will see is the


6/12

particular rank associated with particular rating and the cut off points which basically defines as the total

no of rank defined less than one.

Aaa (best rating) Y*


7/12

5. Results:

In my first result I have regressed Moodys ratings on host of dependent variables which are

defined earlier. for this i have used both the results from Ordered Logit/Probit to check the

difference. From the figure 1&2 similar kind of things I have performed for S&P,Fitch regressionresults can be seen in the appendix.I have used robust standard error for the

heteroscedasticity.

In Moodys result I have found 5 variables (Inflation rate,External balance,Fiscal balance

,Economic development and EU dummy) with 95 % significance level. As GDP growth ,Fiscal

balance ,External balance ,External debt,Economic development increases it also increases the

probability of any country getting good Credit ratings.In this case I have found 18 cut off point

through which probability can be calculated .While in the other hand increment in Inflation rate

,Default history and EU dummies decreases the probabilty of getting the good ratings.

In S&P ratings results are totally similar but the significant variable changed .In this case GDP

growth, inflation rate, external balance and economic development are highly significant while

others not.

In Fitch ratings results and significant variables are same so these two approaches are gives

quite close ratings of the countries rather than the S&P. to check the results see the appendix.

I have also calculated the variance and covariance matrix to see the correlation among the

variables.

From the graph It can be clearly seen that GDP growth has mildve correlation with external

balance ,Economic development, Economic debt and EU dummy GDP growth is +vely correlated to


8/12

Inflation rate ,Fiscal balance and Default history

From the graph we can see that our fitted values are quite close to actual values.

Residual values are higher for under Developed countries may be because of because of data.

For Developed/Developing countries residual values are close zero .

6. Conclusions:-

For the regression I have taken the 2011 data for 79 countries including Developed, Developing / Under

Developing countries.

InMoodys Rating-

Country rating will improve as GDP growth, Fiscal Bal, External balance, External debt increase

and it declines in increment inflation rate

In Fitch Ratings-

Country rating will improve as GDP growth, External balance, External debt and Fiscal balance

increase and it declines in increment inflation rate

In Standard & Poor Ratings-

Country rating will improve as GDP growth, External balance, Fiscal Balance increase and it declines

in increment inflation rate

Major finding is that in all three approaches External balance, Inflation rate, External Debt and EU

dummy has most significant (5% probability) impact on Country credit ratings.


9/12

References:

Afonso, A. (2003), Understanding the determinants of sovereign debt ratings: Evidence for the

two leading agencies. Journal of Economics and Finance, 27(1), 5674.

Antonio Afonso & Pedro Gomes & Philipp Rother, 2009. "Ordered response models for

sovereign debt ratings," Applied Economics Letters, Taylor and Francis Journals, vol. 16(8),

pages 769-773.

Bissoondoyal-Bheenick, E. (2005). An analysis of the determinants of sovereign ratings. Global

Finance Journal 15 (3), 251-280.

Cantor, R., Packer, F. (1996), Determinants and impact of sovereign credit ratings. Economic

Policy Review, 2(2), 37-54.

Canuto, O., Dos Santos, P.F.P., Porto, P.C.D.S. (2012), Macroeconomics and sovereign risk

ratings. Journal of International Commerce, Economics and Policy (JICEP), 3(2), 1250011-25.

Richard Cantor & Frank Packer, 1996. "Determinants and impacts of sovereign credit ratings,"

Research Paper 9608, Federal Reserve Bank of New York .

Cheung, Stella, Provincial Credit Ratings in Canada: An Ordered Probit Analysis (April 1996).

Working Paper 96-6

Wooldridge, J. (2002). Econometric Analysis of Cross Section and Panel Data. MIT Press.


10/12

Appendix :

Regression results : Moodys Ratings:

Fig.1 Ordered Logit Result for Moodys rating

Fig.2 Ordered Probit Result for Moodys ratings

Standard and Poors Ratings


11/12

Fig.3 Ordered Logit Result for S&P s ratings

Fig.4 Ordered Probit Result for S&Ps ratings

Fitch Ratings


12/12

Fig.5 Ordered Logit Result for Fitchs ratings

Fig.6 Ordered Probit Result for Fitchs ratings

ordered response comparison between fitch, standard & poor, moody ratings.pdf

Documents