Russian banks sovereign ratings: a comparative study

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Russian banks sovereign ratings a comparative
study
S.Smirnov, A. Kosyanenko, V. Naumenko, V.
Lapshin, E. BogatyrevaHigher School of
Economics, Moscow
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Introduction

• The aim of the research was to assess credit
  ratings quality for the purpose of Bank
  Counterparties Credit Risk Assessment, in order
  to use them in credit risk models under IRB
  approach.
• Presentation plan
• General requirements to external credit ratings
• Properties of migration matrices
• Assessment of conditional intervals for PD
• Entropy measures
• Mapping to model-based PD
• Conclusions

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Using credit ratings in models

• Probability of default (PD) is one of the major
  building blocks in credit risk management.
• External credit rating can be used as an input
  variable in PD-models. Using credit ratings in
  addition to other sources of information about
  borrower's credit risk (e.g. financial ratios,
  market-based information) may improve the
  prediction power of credit risk models (see
  Kealhofer, 2003, Loffler, 2007)
• By using credit ratings as input to credit risk
  models one should assess the uncertainty of these
  variables (see Basel II).

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Basel II requirements for ratings
Under the IRB approach different exposures should
be treated separately, e.g. corporate, sovereign,
bank, retail, and equity (Basel II,
215). Irrespective of whether a bank is using
external, internal, or pooled data sources, or a
combination of the three, for its PD estimation,
the length of the underlying historical
observation period used must be at least five
years for at least one source. If the available
observation period spans a longer period for any
source, and this data are relevant and material,
this longer period must be used (Basel II,
463) Ratings assigned by for external credit
assessment institution (ECAI) should be
recognized by regulator and satisfy 6 eligibility
criteria Objectivity, Independence,
International access/Transparency, Disclosure,
Resources, Credibility (Basel II, 91).
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National rating agencies in Russia

• There are four largest national rating agencies
  recognized by the Russian Central Bank
  RusRating, Expert RA, National rating agency
  (NRA) and AKM.
• Historical data for the purpose of the research
  was provided by RusRating and AKM. Information
  about ratings assigned by NRA and Expert RA was
  taken from their web-sites.
• Rating data contains monthly information about
  rating assigned to Russian banks from January,
  2001 to May, 2010.

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Dynamics of rating assignment
As of May 1, 2010 there were the following
numbers of efficient credit ratings in the
banking sector RusRating 51 AKM 33 NRA
65 Expert RA - 113

• In October 2008 Russian Central Bank recognized
  ratings assigned by national rating agencies
  (RusRating, Expert RA, NRA and AKM) for the
  purpose of granting unsecured loans.

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Rating Transitions

• Rating agencies are not likely to revise their
  ratings since 2001 there were only few rating
  downgrades. The number of rating upgrades is much
  more substantial.

Total Total Total Since 01.01.2008 Since 01.01.2008 Since 01.01.2008
Down grades Up grades Obs No. Down grades Up grades Obs No.
RusRating 11 85 3375 2 34 1315
Expert RA 13 18 2365 13 18 2261
NRA 0 19 922 0 19 888
AKM 1 4 611 1 4 601
There are several cases when ratings withdrawals
due to termination of the contract followed
rating downgrades in a month or two. In the
beginning of 2010 Expert RA had 5 such facts.
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Rating philosophies

• Ratings Point in Time indicate the current
  probability of issuers default. They are likely
  to change significantly during the bad times.
• Ratings Through the Cycle indicate the average
  probability of default during the long period of
  time. They are not likely to change during the
  economic cycle. However its likely that default
  probabilities associated with ratings grades do
  change.

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Transition matrix computation

• Cohort approach takes into account only the
  initial and terminal states of the institution in
  question.
• Duration approach takes into account time spent
  in every rating grade.
• Under conditions of first order Markov process,
  time homogenous matrix structure for Russian
  agencies these approaches coincide.

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Migration matrices the case of SP

• Typical SP migration matrix (expressed in
  monthly transition probabilities for the purpose
  of comparison with Russian rating agencies)

Source SP 2008 Global Corporate Default Study
and Rating Transitions

• Key features are
• Distinct diagonal line (taking into account
  aggregation of rating classes).
• Existence of non-diagonal elements which gives
  evidence of rating upgrades and downgrades.

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Migration matrices RusRating
Data on rating transitions had monthly frequency.
Each number in a diagonal cell of a migration
matrix shows probability of the fact that rating
will not change in a month period of time.
Numbers below the diagonal line show the
probability of rating upgrades, above
downgrades.
RusRating migration matrix demonstrates
sufficient amount of both upgrades and
downgrades.
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Migration matrices Expert RA, AKM
Migration matrices for Expert RA and AKM have
much less non-diagonal elements than migration
matrix for RusRating.
Expert RA
AKM
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Migration matrices NRA
Rating history of NRA has almost no downgrades.

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Confidence intervals

• Confidence interval is an interval with lower (L)
  and upper (U) bounds that covers the unknown true
  parameter, i.e. L lt p lt U with some predefined
  probability
• ProbL lt p lt U 1 - a.
• Confidence intervals is a standard industry tool
  to assess uncertainty of PD estimations (see, for
  example OeNB, 2004).
• One of the major factors that influence the
  length of confidence intervals for PD is the
  amount of data available. There are research
  papers that show that to some extent it is
  impossible to statistically distinguish
  investment grade rating classes (see Lawrenz,
  2008).

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Confidence interval methodology

• To calculate confidence intervals for PD one
  should

• Fit a priori unconditional PD distribution from
  external data (Russian Deposit Insurance Agency
  PD model) as Beta distribution very good
  agreement. Estimated parameters (a,b) (1,16.3).
• Regard each month for each bank with given rating
  as a trial success if no default, failure if
  default. Form posterior distribution for PD Beta
  (number of defaults 1, number of non-defaults
  16.3).
• Find 95 confidence interval for Beta
  distribution with estimated parameters and plot
  together with (number of observations)-1.

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Confidence intervals RusRating
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Confidence intervals Expert RA
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Confidence intervals NRA
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Confidence intervals AKM
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Conditional entropy

• Conditional entropy measures new information (in
  bits) contained in each successive rating value
  (randomly selected).

Given migration matrix pi,j and unconditional
probabilities pi (expected) conditional entropy
is
To understand what happened to credit quality of
the rating object (3 possibilities whether it
improved, deteriorated or remained the same) it
is necessary to obtain data over the period of
(months) RusRating 9 NRA 11 Expert RA 13
AKM 21.
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Mapping to model assessed PD

• Ratings were mapped to PD estimates derived from
  econometric model based on balance sheet data.
  This model is used by Deposit Insurance Agency to
  assess PD of banks participants of Deposit
  Insurance System.
• The following measures were calculated in order
  to estimate the accuracy of ratings
• average PD for each rating grade
• confidence intervals for PD according to each
  rating grade
• probability that PD associated with different
  rating grades will coincide.

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Rating grades comparison methodology

• Given PD samples for 2 different rating values,
  test a hypothesis these 2 samples really come
  from the same PD distribution.
• Non-parametric Kolmogorov-Smirnov test using
  
• as test statistics.
• Enter the p-value for each pair of rating values
  (including general population) in a table.

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Mapping RusRating
Probability of PD coincidence
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Mapping Expert RA
Probability of PD coincidence
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Mapping NRA
Probability of PD coincidence
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Mapping AKM
Probability of PD coincidence
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Conclusions

• It is reasonable to use external credit rating as
  an input parameter in credit risk models.
  Accuracy of these rating assessment should be
  taken into account.
• However according to our findings we can not
  recommend to use ratings assigned by national
  rating agencies in credit risk models as the only
  source of information due to the lack of
  credibility
• rating are not likely to be downgraded
• sometimes there is no uniform dependence between
  rating grades and PD
• in most cases we can not differentiate between
  rating grades.
• When new data will be accumulated it will be
  possible to estimate rating accuracy once more
  and probably use ratings as an alternative source
  of credit quality information.

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References

1. Basel Committee on Banking Supervision.
   International Convergence of Capital Measurement
   and Capital Standards. A Revised Framework. Bank
   for International Settlements. June 2006 (Basel
   II).
2. Kealhofer, 2003. Quantifying Credit Risk I
   Default Prediction. Finandal Analysts Journal,
   59, pp. 30-44.
3. Loffler, 2007. The Complementary Nature of
   Ratings and Market-Based Measures of Default
   Risk. The Journal of Fixed income, pp. 38-47.
4. OeNB (Oesterreichische Nationalbank), 2004.
   Rating Models and Validation in Guidelines on
   Credit Risk Management.
5. Lawrenz J. Assessing the estimation uncertainty
   of default probabilities.// Kredit und Kapital.
   -2008.-Vol. 41 (2). pp. 217-238.

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Thank you for your attention!