Comparison of Bank Credit Ratings
Assigned by Rating Agencies
Alexander Karminsky
Vladimir Sosyurko
Alexander Vasilyuk
National Research University
“Higher School of Economics”
Moscow, Russia
EBES 2011 CONFERENCE - ISTANBUL
June, 1-3
Agenda


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Problem of credit rating comparison
Rating agencies in Russia
Multiple mapping of rating scales.
Concept & Development
Data gathering & Results
Conclusion
Karminsky, Sosyurko, Vasilyuk - Comparison of Bank Credit Ratings Assigned by Rating Agencies
EBES 2011 CONFERENCE - ISTANBUL
2
Purpose and constraints of credit ratings

Ratings are the independent estimates of:




financial performance of companies, banks or financial instruments
issuer’s creditworthiness (credit risk)
admission to various market products or activity
Ratings are the interest for business entities and market participants,
as far as for the authorities and regulating organizations (Central Banks,
Ministries of Finance, Deposit Insurance Agencies, etc.)

Limitations and constraints for ratings:




Low number of current relevant ratings
Problem of rating comparison for different rating agencies
Absence of multiplicative effect from presence of competitor’s rating
estimations
Requirement for expanded use of independent rating estimations
Karminsky, Sosyurko, Vasilyuk - Comparison of Bank Credit Ratings Assigned by Rating Agencies
EBES 2011 CONFERENCE - ISTANBUL
3
Problem of rating comparison

Most relevant:

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Lacks:

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Possibility of comparison of various agency ratings
Diversified estimations with use of rating modeling
Only pair comparisons are used, scales’ correspondences are
incompatible, displays are linear and use of econometric potential
is limited
No settled approaches to rating scales comparison
Conclusion: required considering all restrictions on arrangement,
data accessibility, etc.
Karminsky, Sosyurko, Vasilyuk - Comparison of Bank Credit Ratings Assigned by Rating Agencies
EBES 2011 CONFERENCE - ISTANBUL
4
Rating agencies in Russia
7 agencies = 3 international & 4 national
Number of bank ratings
596 ratings at the end of 2010
259 358 454 604 596
2006 2007 2008 2009 2010
Karminsky, Sosyurko, Vasilyuk - Comparison of Bank Credit Ratings Assigned by Rating Agencies
EBES 2011 CONFERENCE - ISTANBUL
5
Concept of multiple mapping

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Increase of comparison reliability of scales mapping by using all
available statistical information (in time, on agencies, scales and
structures)
Development of the database that includes ratings, financial and macroindicators
Econometric exposure of the most significant publicly accessible
explanatory variables that have an influence on ratings
Creation of a base scale for mapping transformation of all compared
agency-ratings
Building up a criteria of scales correspondence, considering the
peculiarities of explained component
Determination of mapping parameters using the optimization
procedures. Carrying out the comparison of rating scales
Verification of criteria and estimation of parameters for scales
conformity. Analysis of time dynamics and trends
Forming the methodical and practical basis for regular monitoring,
modeling and verification of rating models
Karminsky, Sosyurko, Vasilyuk - Comparison of Bank Credit Ratings Assigned by Rating Agencies
EBES 2011 CONFERENCE - ISTANBUL
6
Comparison methods and base scale

Comparison methods for rating scales include:


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
Methodology and principles of mapping of rating scales
Criteria for comparison of rating scales (Mathematics)
Econometric models for scales’ comparison
Audit of the “conformity table” and the coordination of its structure
Comparison methods are concluded to have:




Choice of a base rating scale
Mapping system for displaying external and internal ratings into a base
scale
Application to each class of rating entities (banks, companies, etc.)
Allowing simultaneous use of all independent rating estimations
RS1
Rating
Scales
NS1
RSi
NSi
RSN
NSN
F1(α1)
Numeric
Scales
Fi(αi)
BS
Base Scale
FN(αN)
Karminsky, Sosyurko, Vasilyuk - Comparison of Bank Credit Ratings Assigned by Rating Agencies
EBES 2011 CONFERENCE - ISTANBUL
7
Rating modeling (Ordered probit models)
yi*  xi   i ,
where xi – is a set of independent variables
y i*
P( yi  0)  F (c1  xi ),

P( yi  r )  F (cr 1  xt )  F (cr  xi ), 2  r  k  1,
P( y  k )  1  F (c  x ).
i
k 1
i

 yi  1 if yi*  c1 ,

*
 yi  r if cr 1  yi  cr , 2  r  k  1,

*
y

k
if
y
i  c k 1 ,
 i



Rating is a depended variable y
Less values of y are connected with higher agency-ratings
Ratings are represented as a numeric scale: 12+ grades
Karminsky, Sosyurko, Vasilyuk - Comparison of Bank Credit Ratings Assigned by Rating Agencies
EBES 2011 CONFERENCE - ISTANBUL
8
Research data

10 rating scales:





4 national rating agencies
3 international agencies (3+3)
Time period: 1q2006 – 4q2010
(20 quarters)
370 Russian banks with at least 1 rating
during this period
Total 7400 observations of banks
Karminsky, Sosyurko, Vasilyuk - Comparison of Bank Credit Ratings Assigned by Rating Agencies
EBES 2011 CONFERENCE - ISTANBUL
9
Numerical scales
S&P
S&P (rus)
Moody’s
M
Moody’s
(rus)
M_ru
Aaa.ru 1
Aa1.ru 2
Aa2.ru 3
Aa3.ru 4
A1.ru
5
A2.ru
6
A3.ru
7
Baa1.ru 8
Baa2.ru 9
Baa3.ru 10
Ba1.ru 11
Ba2.ru 12
Ba3.ru 13
B1.ru
14
B2.ru
15
B3.ru
16
Fitch
SP
SP_ru
AAA 1 ruAAA 1
AA+ 2 ruAA+ 2
AA
3 ruAA
3
AA4 ruAA- 4
A+
5 ruA+
5
A
6 ruA
6
A7 ruA7
BBB+ 8 ruBBB+ 8
BBB 9 ruBBB 9
BBB- 10 ruBBB- 10
BB+ 11 ruBB+ 11
BB
12 ruBB
12
BB- 13 ruBB- 13
B+
14 ruB+
14
B
15 ruB
15
B16 ruB16
Aaa
Aa1
Aa2
Aa3
A1
A2
A3
Baa1
Baa2
Baa3
Ba1
Ba2
Ba3
B1
B2
B3
CCC+ 17 ruCCC+ 17
Caa1 17 Caa1.ru 17
CCC+ 17
CCC 18 ruCCC 18
CCC- 19 ruCCC- 19
CC
20 ruC
20
Caa2 18 Caa2.ru 18
Caa3 19 Caa3.ru 19
C
20 C.ru
20
CCC 18
CCC- 19
C
20
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
F
AAA
AA+
AA
AAA+
A
ABBB+
BBB
BBBBB+
BB
BBB+
B
B-
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Fitch (rus)
F_ru
AAA(rus)
AA+(rus)
AA(rus)
AA-(rus)
A+(rus)
A(rus)
A-(rus)
BBB+(rus)
BBB(rus)
BBB-(rus)
BB+(rus)
BB(rus)
BB-(rus)
B+(rus)
B(rus)
B-(rus)
CCC+(rus
)
CCC(rus)
CCC-(rus)
C(rus)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Expert
RA
ERA
A++ 1
A+
2
A
3
B++ 4
B+
5
B
6
C++ 7
C+
8
C
9
NRA
NRA
AAA 1
AA+ 2
AA 3
AA- 4
A+
5
A
6
A7
BBB+ 8
BBB 9
BBB- 10
BB+ 11
BB 12
BB- 13
АК&М
AKM
A++
1
A+
2
A
3
B++
4
B+
5
B
6
C++
7
C+
8
C
9
RusRating
RR
AAA
1
AA+
2
AA
3
AA4
A+
5
A
6
A7
BBB+ 8
BBB
9
BBB- 10
BB+
11
BB
12
BB13
B+
14
B
15
B16
17
CCC+
17
18
19
20
CCC
CCCC
18
19
20
Karminsky, Sosyurko, Vasilyuk - Comparison of Bank Credit Ratings Assigned by Rating Agencies
EBES 2011 CONFERENCE - ISTANBUL
10
Criteria for choosing the mapping function
min
{ i ,i 1,..,N }
2
(
F
(
R
,

)

F
(
R
,

))
 i 1 i 1jt i 1 i 2 i 2 jt i 2
Q
Q – set of all observations {t – period of time, j – bank,
Ri1jt – Moody’s rating (base scale),
Ri2jt – rating of another agency }
t = 1, … , T
j = 1, …., K
Fi1 : Ri → Rbase
Fi = αi1 ∙ fi (Ri) + αi2
fi – linear, polynomial, logarithmic function
that transforms rating into a base scale
Karminsky, Sosyurko, Vasilyuk - Comparison of Bank Credit Ratings Assigned by Rating Agencies
EBES 2011 CONFERENCE - ISTANBUL
11
Mapping function
Multiple mapping into the
base scale:



linear
logarithmic
polynomial (up to 5th
power)
Moody’s – S&P
Moody’s – Moody’s (rus)
Karminsky, Sosyurko, Vasilyuk - Comparison of Bank Credit Ratings Assigned by Rating Agencies
EBES 2011 CONFERENCE - ISTANBUL
12
Logarithmic model of multiple mapping
Moody’s credit ratings (R) and
default probabilities (PD) of banks
are
approximated
by
a
logarithmic dependence during the
years 1980-2008
PD = 0,000218×R3,8
PD 25
(%)
20
15
10
5
0
R
1
3
5
7
9
11
13
15
17
19
Logarithmic model for 2006-2010 years:
M = const∙Ra
↔ Ln(M) = a∙Ln(R)+b
Variable
LOG(M_RU)*D_M_RU
D_M_RU
LOG(SP)*D_SP
D_SP
LOG(SP_RU)*D_SP_RU
D_SP_RU
LOG(F)*D_F
D_F
LOG(F_RU)*D_F_RU
D_F_RU
LOG(AKM)*D_AKM
D_AKM
FD
LOG(ERA)*D_ERA
Model
D_ERA
LOG(RR)*D_RR
D_RR
LOG(NRA)*D_NRA
D_NRA
Number of Observations
R2
Karminsky, Sosyurko, Vasilyuk - Comparison of Bank Credit Ratings Assigned by Rating Agencies
EBES 2011 CONFERENCE - ISTANBUL
Coefficients a,b
0,254
2,202
0,916
0,146
0,265
2,113
0,749
0,594
0,213
2,162
0,269
2,491
0,373
2,329
0,674
1,016
0,163
2,474
3432
0,902
p
0,000
0,000
0,000
0,029
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
0,000
13
Rating comparison (logarithmic scales)
NRA
Rus-Rating
Expert RA
AK&M
Fitch (rus)
Fitch
S&P (rus)
S&P
Moody’s (rus)
Moody’s
Karminsky, Sosyurko, Vasilyuk - Comparison of Bank Credit Ratings Assigned by Rating Agencies
EBES 2011 CONFERENCE - ISTANBUL
14
Comparison of international banks



3639 pairs (Moody’s – another agency)
Bank data 1995 – 2010
290 different banks
Fitch
S&P
Moody’s
Credit rating’ comparison for scales of international agencies
(logarithmic model)
Karminsky, Sosyurko, Vasilyuk - Comparison of Bank Credit Ratings Assigned by Rating Agencies
EBES 2011 CONFERENCE - ISTANBUL
15
Conclusion








Econometric models for ratings play significant role due to IRB
Approach and other Basel II recommendations and should be
developed
Scientific and practical basis of using econometric rating models for
bank risk management is discussed
Comparison method of ratings of different agencies lies in the basis
of Unified Rating Space modeling system
Scales Mapping Concept and methods are built
Including the criteria for choosing the function of transformation of
rating value into the base scale
Comparison of credit ratings has been performed
Models were verified by international bank data and other mapping
approaches
The main problems are DATA, MONITORING and VERIFICATION
of models
Karminsky, Sosyurko, Vasilyuk - Comparison of Bank Credit Ratings Assigned by Rating Agencies
EBES 2011 CONFERENCE - ISTANBUL
16
Q&A
Thank you for your attention!
Alexander Karminsky, Prof., Dr.
AKarminsky@hse.ru
karminsky@mail.ru
Vladimir Sosyurko
vsosyurko@mail.ru
Alexander Vasilyuk
a.a.vasilyuk@gmail.com
Higher School of Economics (HSE)
Moscow, Russia
Karminsky, Sosyurko, Vasilyuk - Comparison of Bank Credit Ratings Assigned by Rating Agencies
EBES 2011 CONFERENCE - ISTANBUL
17