May 2006
Discussions on Advancing Credit Risk Management through Internal Rating
Systems (1)―――Summary of Discussions at the “Study Group on the
Advancement of Credit Risk Management”
I. Introduction
In October 2005, the Study Group on the Advancement of Credit Risk Management(hereafter, the Group)1 began discussions among experienced practitioners of credit risk
management to discuss various approaches necessary for advancing credit risk
management through the use of internal rating systems. The Group took into
consideration, discussions abroad and approaches actually taken by foreign banks withadvanced credit risk management expertise.
This paper presents the highlights of the discussions at the past five sessions, the very
last of which was held in February 2006.2 The Group aims to widely disclose its
discussions to financial circles in Japan so that each financial institution may refer tothem in advancing credit risk management. This paper is mainly written for financial
institutions that aim to establish and strengthen credit risk management in line with top
international practices. Discussions at the sixth and the following sessions arescheduled to be published in the future.
This paper does not intend to draw conclusions for each of the issues discussed. It
simply aims at presenting issues and discussions concerning the advancement of credit
risk management. Opinions are those of members and do not represent those of theorganization each member belongs to.
II. Discussions at the Second Session (held on November 10, 2005)
A. Problems in PD Estimation due to Limited Default Data
1. Potential Issues
A portion of portfolios held by financial institutions have a low number of or no default
samples (so-called LDPs).3
1 Center for Advanced Financial Technology in Financial Systems and Bank Examination

Department of the Bank of Japan serves as the secretariat of the Group.
2
At the first session (held on October 21, 2005), members confirmed the Group’s management
policy and issues for discussion. Actual discussions started from the second session.
3
LDP stands for low-default portfolio. Points to be kept in mind in estimating and validating risk
1

LDPs include loans to highly rated large firms, sovereigns, and financial institutions, aswell as loans in a relatively new market (e.g., non-recourse mortgage loans in Japan).
The following elements or a combination of them explain why default data is limited forsome portfolios. First, the quality of loans in a certain portfolio is very high. Second,
the number of loans in a certain portfolio is small. Third, transactions in a certain
portfolio have a short history either because the market itself is new or because afinancial institution is a recent market entrant.
Due to limited default data, estimation error tends to expand for LDPs and hence theincreased possibility of underestimation of PD. Meanwhile, it is difficult in practice tosupplement the lack of data using external data, mapping PD to external rating grades,and other measures in the estimation and validation of PD and other risk parameters.
2. Participants’ Views
Four approaches below may be taken to deal with the LDP problem. Methods for
estimating risk parameters are not yet established for LDPs and various techniques are
expected to be developed in the future.
Approaches

Techniques

A. Using internal data of Appropriately map (linking of data) LDPs to other internal
portfolios and apply PD of other portfolios after making
financial institutions
necessary adjustments.
Combine data of the adjacent ratings and subportfolios withsimilar characteristics to increase the size of data sample for
LDPs.
Utilize information on the migration of assets categorized as
LDPs in PD estimation, to incorporate the possibility ofsuccessive downgrades leading to default, together with thepossibility of direct transitions to default.
B. Using external data
sources (including

Map LDPs to external data source that have data covering
longer time periods or have a larger data sample to increase

components for LDPs are mentioned in “Validation of low-default portfolios in the Basel II
Framework” Basel Committee Newsletter No.6, September 2005.
2

pooled data of externalorganizations andoverseas market
information)4

default samples.
Extract PD of LDPs implied in market information (e.g., credit
spread) using some pricing models.

C. Using the outcome of Apply PD estimated with default prediction models that use
default prediction models explanatory variables assumed to affect the level of PD (riskdriver) instead of directly applying the actual default data (thetechnique in approach D. below may be used to determine risk
drivers).
D. Using expert
judgement (qualitative
information)5

Estimate PD levels based on the empirical knowledge ofexperts in LDP transactions paying due attention to objectivityof qualitative judgement.

It is necessary to use several methods, which may involve some of the four approaches
above, in the process of estimating and validating risk components. The difficulties intheir estimation also highlight the difficulties in their validations. Nevertheless, theefforts to enhance quality are expected by, for example, comparing estimates throughmultiple approaches or confirming similarity in the grading standards of internal andexternal ratings.
Financial institutions should establish estimation and validation procedures as part ofcredit policy, and document internal discussions and examinations to maintaintransparency and consistency. The options currently available for each type of LDP
are limited. It may, therefore, be inappropriate to limit estimation and validation
techniques or set a specific operational rule that, for example, requires the use of themost severe results.
Evaluated risk of LDPs should be reflected directly or used as a reference indicator in
the pricing of loans. In this way, financial institutions will be able to maintain a certain
consistency between risk evaluation and lending practices.
B. Taking into Consideration the Effect of Business Cycles on PD Estimation
4

Data from external organizations may be used when they, for example, have longer time-series data,wider range of data covering more firms including foreign ones, and long-term historical data ofcredit related markets.
5
Currently, approach D. is mainly used for LGD estimation though it is not necessarily exclusive to
LGD and sometimes also used for PD estimation.
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1. Potential Issues
Business cycles are considered to affect the quality of credit portfolios and to cause
fluctuations in capital adequacy ratios. It is, therefore, necessary to understand theirimpacts (how they affect rating migration and default rates) and to prepare adequate
buffers for stability in financial institution management. Such understanding helps
adequate validation of PD estimation and of internal rating models.
Various technical problems, however, exist in grasping the impact of business cycles.
They include the problem of (1) securing long-term time-series data, (2) specifying
cyclical factors, and (3) evaluating the way of portfolio management, which may bepassive or active against business cycles.
2. Participants’ Views
Procedures necessary for identifying the impact of business cycles include thefollowing.
A. Securing data overbusiness cycles

Secure long-term time-series data covering at least twoconsecutive business cycles (preferably more than two cycles).
If it is difficult to satisfy the above conditions, supplement
internal data with external data.

B. Analyzing businesscyclesSpecify factors that explain business cycles.

C. Analyzing thesensitivity of the qualityof credit portfolios tobusiness cycles

Understand a bank’s own “rating assignment horizon” (whether
banks focus only on the current or medium- to long-term
conditions of borrowers). In other words, confirm whether
banks’ rating assignment is based on PIT6 (changes in businesscycles are reflected in the form of rating migration) or TTC(changes in business cycles are reflected in the form of changes
in the actual default rate for each grade).
Analyze the relationship between factors that explain business6

In PIT (point-in-time) rating, risks are evaluated based on the current condition of a firm regardlessof the phase of the business cycle at the time of evaluation. In TTC (through-the-cycle) rating,risks are taken into account on the assumption that a firm is experiencing the bottom of the businesscycle and is under stress.
4

cycles and changes in (1) the pattern of rating migration, (2) the
actual default rates for each rating, and (3) the financial
condition of debtor firms and subsequent rating changes.
Concepts of PIT and TTC have not fully taken hold in the risk management process offinancial institutions. Also, lack of long-term time-series data still makes it difficult tovalidate the impact of business cycles on rating migration and time-series changes ofdefault rates.
Regardless of whether an institution is using the idea of PIT and TTC or not, it isimportant to find out how business cycles affect rating migration and default rates foreach grade (as mentioned in C. above) because it helps assess the magnitude of the
impact of business cycles as well as the functioning of internal rating systems. Even if
strict application of a quantitative approach is difficult, banks should consider
recognizing explicitly the impact of business cycles in their risk management processes,
using available tools and techniques. For example, economic impacts may bevalidated through continuous monitoring of the orders and stability of default andmigration rates. Also, stress tests may also be applied for migration and default rates
to understand how portfolios are affected by business cycles.
III. Discussions at the Third Session (held on December 15, 2005)
A. Problems Related to the Definition of Default
1. Potential Issues
The definition of default under the new regulatory capital framework may be interpreted
as identical to borrowers that need “special mention” or those in lower categories under
the self-assessment framework. This definition is stricter than that commonly usedinternally by Japanese financial institutions and is expected to encourage banks to
expedite their actions against troubled borrowers and thereby enhance the stability offinancial system.
However, the difference between the definition of default used for regulatory purposes
and the ones that have been used by Japanese financial institutions for internal
operations could pose a challenge of how to maintain consistency between the two inadvancing risk management.
2. Participants’ Views

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There is no wide difference in the definition of borrowers that need “special mention”
used by financial institutions, due to the efforts by financial institutions to follow the
self-assessment guideline and by the authorities to clarify the categorization criteria
through examinations and other measures. Still, banks would need to make on-going
efforts to maintain transparency and consistency in applying their definition of default,
and thereby presenting to third parties including authorities that banks are not arbitrarily
applying the definition of defaults for the purpose of decreasing the amount of required
capital.
As for the problems arising from the differences in the definition of default used byfinancial institutions and the one applied for regulatory purposes, all concerned parties
would need to recognize them and continue to make efforts to address such differences
within the process of seeking to ensure appropriate risk management.
It is not expected that the levels of PD and LGD estimated by various financial
institutions using the new definition of default should converge to a certain narrow
range. Estimated results of these risk components are expected to change, depending
on business strategies, geographical regions, and other factors. An important point isthat financial institutions need to be capable of providing objective explanations toshow that the levels of PD and LGD are estimated without arbitrariness, for example, byusing tools such as benchmarking with external ratings.
Even at individual financial institutions, PD levels may differ among portfolios (e.g.,
between domestic and overseas portfolios, and different products in retail banking).
Such disparities do not necessarily deny the consistency of default definition as long as
financial institutions are able to give rational explanations of the factors behind thedisparities (e.g., business strategies and special factors concerning regional andsovereign characteristics).7
Definition of default (or risk components based on this definition) used for internal
operations, such as risk management and extension of credit, does not necessarily haveto be equivalent to that for regulatory capital (or risk components based on thisdefinition). Regardless of the choice of definition, however, financial institutions need7

For example, if PD of portfolio consisting of credit in the United States clearly differs from that inJapan, the level of the former should be explainable through benchmarking with external ratings andothers.
6

to indicate that they are based on the consistent concepts (e.g. using the same data or
estimation methods) and thus they themselves are consistent with each other.
B. Problem of Credit Concentration to a Few Large Borrowers in Estimating Risk
Components
1. Potential Issues
A problem of credit concentration to a few large borrowers is often recognized as theso-called granularity problem, the one that affects the risk amount of the overall
portfolio. This type of credit concentration could also cause some irregularities in
estimating risk components due to the differences in the performance of ratings of large
and small borrowers over various phases of business cycles. For example, this credit
concentration could entail the situation where average PD weighted by loss amounts
constantly exceeds average PD weighted by number of defaults.
Unless borrower ratings are constantly biased in favor of larger size, large disparities
between the default rates based on the loss amount and those based on the number ofdefaulted obligors may be caused by the credit concentration to a few large borrowers.
If so, estimation of PD will require some kind of alteration. Japanese default data inthe past few years, however, is very likely to also reflect structural changes in the macro
economy in addition to cyclical aspects. Consensus has not yet been formed, at thispoint, on how to identify the effects of these factors.
2. Participants’ Views
One effective way of confirming the effect of credit concentration to a few large
borrowers is to compare default rates based on the loss amount and those based on thenumber of defaulted obligors, and then examine the level of disparities and the durationof the existence of such disparities. It goes without saying that this comparison
becomes meaningful only if borrower ratings are not biased by the size of borrowers.8
For this, if external ratings and others are available for benchmarking, they should be
used for validating PD levels of large borrowers.
Even if disparities existed on a continuous basis, this does not necessarily mean thatinternal ratings and PD estimation methods should be altered. In order to deal withdisparities that are mainly caused by credit concentration to a few large borrowers, one

8

Measures should be taken so that firm sizes will not bring about systematic bias in rating.
7

could also consider measures where the level of LGD and EAD may be adjusted or
stress test type tools may be applied to prepare for the risk materialization.
IV. Discussions at the Fourth Session (held on January 11, 2006)
A. Problems Related to Internal Rating Systems and Models
1. Potential Issues
Approaches vary among internal rating systems and models. The same risk profile
may, therefore, have different quantitative results.
Backgrounds behind various approaches and some examples
Internal
ratingsystems

Different risk assessment approach toward portfolios with different risk
characteristics (by country, by asset type, and by industry) may encourage
financial institutions to adopt different rating systems for each portfolio.
Rating structure and pool categorization required from the perspective ofsecuring homogeneity of risks may encourage financial institutions to adopt
different ranges of creditworthiness and level of concentration of borrowers ineach grade.
Choice of methods for facility rating, i.e., one-dimensional vs two-dimensional
ratings.

Internal
ratingmodelsA. Modeling
Models that estimate the order of borrowers (facilities) by creditworthiness vs.
models that also estimate PD levels of each borrower.
Models that are to some extent based on expert judgement vs. models that focus
only on the results of default and data on the defaulted borrower without
assuming any structural relationship about causality between the two.
Choice of a single model vs. multiple models for internal rating system.
B. Data to be input into the model
Choice of minimum data that a model should incorporate according to a priori
knowledge.
Method of incorporating qualitative and substantial financial information (e.g.,8whether to convert it to data which can be input directly into internal rating
models or use it to make qualitative adjustments to the results of the model).
C. Accuracy of the model
Evaluation of model performance using quantitative indicators (e.g., accuracy
ratio).
2. Participants’ Views
Details of internal rating systems and internal rating models vary, reflecting thedifferences in the business models of financial institutions. This is why the sameborrower may be evaluated differently by each financial institution.
In view of the above, financial institutions are expected to design internal rating systems
that suit their business models. In this process, they are also expected to pay attention
to the following points, which may be valid regardless of their business models. First,
if different rating systems are assigned to different portfolios, this assignment should bedone from the perspective of accurately capturing the risk structure of portfolio as awhole and for each rating grade. Second, rating system structures should not be
intentionally altered to underestimate required capital. Financial institutions should set
a policy on rating systems and maintain consistency in their operations.
When choosing single or multiple rating systems, for example, there are several merits
and possible demerits for each choice. The important point is that financial institutions
need to be in a position to explain how they had evaluated them and selected a specificrating system.
Single rating system
MeritsSimple and clear.
Easy to compare different type ofcredits.
Easy to maintain consistency in risk
measurement.
Provides more data sample for risk
quantification.

9

Multiple rating systems
Consistent with business practices(e.g., rating approval and loan
origination)
Enables more precise risk evaluationin view of credit characteristics.

Points
requiringattention

Are differences in loan transactions
captured clearly?

Are the costs of losing the merits ofa single rating system too much?

Is the adoption of a single rating
system hindering the use of ratingsby business lines?

Is the mapping in terms of risk
amounts possible to some extent
among different rating systems?

Is the rating system designed toovercome differences in industry,
region, and other characteristics?
In general, a rating structure needs to be checked by confirming that (1) there is noexcessive concentration of borrowers or facilities in a specific rating grade (unless there
is a clear reason) and (2) that the balance between the range of creditworthiness and thenumber of borrowers in each rating grade is appropriate for an adequate estimation ofrisk components. In some cases, borrowers may be concentrated in a specific rating
grade if the range of creditworthiness of the borrowers concerned is extremely narrow.
Internal rating models may be built with a focus only on default results and data on
defaulted borrowers without necessarily paying attention to the causal structure, in order
to increase model performance. Such models, however, may face difficulties in (1)addressing aged deterioration of performance of models to be continuously used byfine-tuning and (2) explaining the reasons for the specific rating in a way appealing tobusiness lines. If estimation models are used to obtain PD instead of using long-term
average of actual default, the difference between the two results should be explained
rationally.
Regarding input data used for building an internal rating model, it is necessary toconfirm that all important factors (e.g. country and industry factors) are included. Ifsome are excluded, there should be rational reasons to do so. It is also important tounderstand how the model responds to the factors that may be critical, regardless ofwhether they will actually be used or not. This process helps increase the transparencyof the selection of these factors. There are many possible ways to incorporate
qualitative information and substantial financial information that are not captured by10accounting data.9 Financial institutions should choose the methodology, which would
produce the least biases in the rating process.
Ex ante and ex post evaluation of internal rating models should be implemented fromvarious perspectives by factoring in the medium- to long-term stability and robustnessof the models. For example, it is important to carry out comprehensive and continuous
evaluation using statistical evaluation techniques such as the AR test. In the process ofsuch evaluation, it will be effective to compare the model in use with other models tounderstand its characteristics.
It is important for financial institutions to be in a position to explain the adequacy of
their internal rating systems and internal rating models to third parties including theauthorities. Meanwhile, the authorities should prepare tools to compare different
rating systems and models in order to (1) understand the characteristics of internalrating systems and models used by each institution, and also to (2) grasp the situation of
the financial system from a macro perspective. This benchmarking work, however,would not be suggesting the use of any specific rating systems or models by financial
institutions.

B. Problems Related to Incorporating Forward-Looking Components in
Estimating Risk Components
1. Potential Issues
Estimation of risk components tends to depend heavily on historical data to ensure
objectivity. Meanwhile, forward-looking components could often play an importantrole in developing effective risk management and business strategy.
If risks are assumed to materialize at a certain point in the future by simply following ahistorical pattern, no wide difference would be observed between estimation based
mainly on historical data and that on forward-looking components. This assumption,
however, does not hold in cases where the economy undergoes a long-term structuralstress, when financial institutions drastically change their business models such as loan

9

Qualitative information and financial data may, for example, be used (1) as direct input into therating model after converting them into quantitative information, (2) as information to makeadjustments of preliminary rating, and (3) as information to help judge loan originations and support
other front businesses.
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origination policies and rating approval criteria, or create a new type of portfolio. In
these cases, estimation based merely on historical data may hinder financial innovationand decrease motivation for improving the rating approval process.
If there are large disparities between data used for risk management (estimation based
mainly on historical data) and that used for planning medium- to long-term businessstrategies (estimation based mainly on forward-looking components), financial
institutions will need the capability to logically explain such disparities.
2. Participants’ Views
Financial institutions may choose to incorporate forward-looking components (e.g., adrastic change of a business model) in the estimation of risk components, which departfrom the historical pattern as indicated in the above.
This choice should only be accepted, however, if risk management sections are able topersuasively and objectively explain, to the management and third parties outside, why
disparities exist between the estimates mainly based on historical data and those on
forward-looking components. Objective reasoning may be given using historical data
adjusted for a factor specified to have caused changes in the historical pattern, market
information and results of market surveys, developments in leading indicators, results ofmacroeconomic models etc.
Similar persuasive arguments would be necessary if risk management and businessstrategies use risk components with different weights of forward looking components.
V. Discussions at the Fifth Session (held on February 9, 2006)
A. Problems Related to the Use of External Data and Models
1. Potential Issues
Use of external data and models could be effective for building internal rating models
and estimating PD and other risk components when, for example, (1) the number ofsamples in internal data is limited, (2) there is a need to use know-how of vendors andother external institutions, (3) there is a need to cut costs for developing internal models,and (4) data and models developed at a key institution within a financial group are
applied consistently to other entities in the group.
The use of external data may lead to inaccurate understanding of risk profile if applied

12without confirming consistency with internal data. Also, the use of external models
may hinder financial institutions from carrying out effective risk management andvalidation work, if the external models, which do not yet have an established reputationin the market, become “black boxes” due to contractual conditions or due to the lack ofexperts within a firm who are familiar with the model. Also, external models based on
external data may not be well-suited for the business environment of the financial
institutions using them.
It should also be well understood that the use of external data and models may lead tounintended changes in a firm’s risk management framework depending on situations atthe vendors and external institution, and the details of the contract.
2. Participants’ Views
The purpose of using external data varies and ranges from estimating risk components,
to obtaining sample data for building a model, or validating internal ratings. It is
important for financial institutions to recognize this purpose clearly. Generally, themore influential the external data on the estimation of risk components become, themore precise the validation of its adequacy should be.
In confirming consistency between external and internal data, it is necessary tounderstand the differences in various attributions between the two data. For example,
if the definition of default and borrower characteristics (e.g., size, industry, and region)differs clearly, adequacy of the mapping process of external data to internal one should
be examined considering such differences and in this process attributions important inmaintaining consistency between internal and external data should be specified. Even
if internal and external data, which are extracted based on a specified attribution, may
seem to show consistency, it is preferable to use a model other than internal ones toconfirm consistency because the outcome greatly depends on the model output and thus
on the structure of internal models. If definitions of default differ for internal andexternal data, strict adjustments of the definition may be difficult. In this case,external data should be adjusted conservatively given this imperfect adjustment.
Internal rating models that are built using external data may be validated as follows.
First, select borrowers who are included both in internal and external data. Thencompare rating results of the internal model with that of other models. If large
discrepancies exist between the two results, the factors behind them should be13

analyzed. 10 It will also be effective to compare default distribution obtained by
inputting only external data in the internal model with that obtained from internal data.
Quality of external data, such as ratings by rating agencies, could be ensured to someextent through market discipline if used widely in the markets. External ratings,
however, may have different criteria for evaluating firms from the one for businessmodels used by each financial institution. For example, rating agencies may evaluate
firms from a long-term perspective, while a financial institution’s business model may
have a more short-term perspective. In using external data, such differences should betaken into consideration as much as possible. How much attention should be paid tothe differences depends on the purposes of using external data (e.g., to be used for
estimating risk components or for validating internal ratings).
A system should be established so that the above assessment is carried out in aconsistent way within an organization regardless of sections or timing. How strictlysuch discrepancies should be addressed depends again on the purpose of using external
data. For example, when external data is used to estimate risk components such as PD,
strict analysis of the consistency between internal and external data should be madebecause such estimation will directly affect the amount of required capital.
As in the case of external data, external models should be used with clear purpose. For
example, it should be decided whether an external model will be used as a main model
for internal ratings or as a model to validate a main model.
Data used for building a model or information about the logic of the model are
important for risk management purposes, but in some models they are not available tomodel users, in other words, have become “black boxes”. In this case, particularly
when the models do not yet have established reputation in the market, financial
institutions should, in principle, demand disclosure of information to vendors or
external institutions that built the models to the utmost extent. Certain information onmodels are reasonably “black boxed” because vendors or external institutions want toprotect their intellectual property rights and thus retain the commercial merits ofdeveloping models. At the same time, however, users of external models should be
10This validation method is effective because external vendors or other institutions are likely to havebuilt their models not only by using external quantitative data but also by supplementing qualitative
information and others in view of the limitations of external data.
14

aware that vendors or external institutions may not disclose information for different
reasons, for example, in order to hide potential weaknesses of the model.
If some information remain “black boxed” even after financial institutions’ requests ofdisclosure, financial institutions should clarify exactly what information are unknown.
It is also necessary to indicate how and to what extent such information will cause
problems in the validation of the model and how such problems may be overcome.
Examples of “black boxes,” problems caused by them, and possible measures to deal
with them are given below.

“Black boxes”

Assumed problems

Possible measures

Sample data used for building Model performance willa model.
decline if sample data andportfolio held by financial
institutions differ greatly.

Validate the model using
internal and external data.
Obtain information on theattribution of sample datafrom vendors and externalorganizations.
Request vendors to validate
the model using data
provided by the user
institution.

Model structure and logic.
Method of building a model(estimation method andexpert judgement).
Parameters of a model.
Initial output of the model(e.g., scoring).

Unable to identify Factors
behind deterioration of
portfolio suggested by amodel is not necessarily
identified.
Unable to specify factors
behind differences in therating results of borrowers ofsimilar creditworthiness.

15

Broadly specify factors byobserving differences in theperformances of portfolios
pooled by obligors’
attributions such as ratings,
industry, and firm size.
Take various approaches invalidation by using third party
information in addition to that

held by institutions
themselves.
Validation results of vendors
and external organizations.

Unable to precisely validate
model performance.

Request disclosure ofvalidation results if model
performance verified byinternal and external data
show significant
deterioration.

Choice of measures, in cases where problems due to “black boxes” cannot be dismissed,
depends on how external models are used. If external models are to be used as a mainmodel, the problems could be more serious and commensurate measures would neet tobe taken.

B. Problems Related to LGD Estimation
1. Potential Issues
While PD is an indicator of the probable frequency of loss events, LGD is an indicatorof severity of loss against principals for each loss event. Thus, LGD as well as PD are
significant indicators used for risk management and quantification of risk based on
rating and pooling. There is not necessarily a broad consensus, however, on thedefinition and estimation method of LGD partly due to lack of data and the existence oftechnical problems. In the case where accuracy of LGD is significantly low reflecting
lack of data and unclear concepts but is not compensated by conservative adjustments
nor by conservative estimation of other risk components, required capital calculated byeach financial institution may vary greatly.
Reasons behind the difficulties in collecting data of LGD and in estimating LGD can besummarized as follows.
Reasons for Need extensive information (cash flow of collection over many years, sourceof collection and its type, cost of collection, and discount rates reflecting riskthedifficulty in premium of the collection).
collecting
Often need long time periods to fix data points (i.e. until collection is
datacompleted after default).

16

Difficult to trace back historical data to reflect changes in the definition ofdefault (e.g., from borrowers in danger of bankruptcy to borrowers that need
“special mention”).
Difficult to obtain the LGD (1) of default cases that could return to non-default
status (e.g. when a broader definition of default such as being equal toborrowers that need “special mention” or those categorized lower) and thus (2)would become difficult to fix loss data.
Difficult to obtain information on LGD of housing loans and other retailproducts from guarantee companies.
Lack of data (see the above)
Treatment of discount rates is not yet established.
Treatment of the cost of collection is not yet established.
Treatment and estimation methods are not yet established for (1) downturn
LGD that takes into account the period of recession and (2) correlation
between PD and LGD.
Reasons for
Methods are not yet established for treating portfolios that have recovered tothenon-default status from default status.
difficulty inestimating
On the LGD estimation method, some including the following points are not
LGD
yet established.
--- Model structure (scoring model based on a priori knowledge vs. rating
model based on statistical method, rating model vs. direct estimation of
LGD).
--- Specification of factors affecting LGD (rate of coverage by collateral,
relationship between appraised value of collateral and its actual value
after liquidation, volatility and trend of collateral value, seniority, lengthof workout period, characteristics of borrowers <type of industry, rating
just before default>, amount of credit <large vs. small>, whether alending financial institution is the main bank for a concerned obligor or17not, macro factors such as economic conditions, and others).
Approaches for validating estimated values are not yet established.
--- Difficult to validate estimated LGD, of which uses for business purposes
are not yet established.
2. Participants’ Views
Financial institutions need to clarify the following points when collecting LGD data.
Point at issue and possible approaches
Definition ofdefault

Use the same definition of default for PD and LGD.

Workout period

Decide the maximum workout period to be counted into LGD
estimation in view of difficulty in tracking the amount of collection
over longer time periods.
--- Only include the amount collected within the predefined
maximum period decided or make estimations on the amountof collection for years that exceed the maximum period.
--- Conservative treatment is required for the latter.

Treatment ofdownturn LGD

Determine downturn LGD by taking into account the correlation
between PD and LGD, in view of the fact that LGD tends to increaseat times of economic downturn as PD increases.
Use LGD at the bottom of the economic cycle or default weighted
LGD.

Discount rates(DRs)

In theory, DR should be equal to risk free rate plus risk premium on
defaulted assets.
--- Consider DR as “rates based on contract interest rates on loans(if risk premium is reflected in contract interest rates)”--- Risk premiums should be adjusted to avoid double-countingof risk, if a certain amount of risk is already reflected inestimation of the amount of collection.

Collection cost

Variable expenses including personnel expenses needed forcollection work will be calculated by multiplying the standard unit
price (derived by actual costs) by the period of collection. Other
costs (e.g., servicing fees) will be added as necessary.

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Treatment ofdefaulted assetsthat recovered to
non-default status

EAD discounted by accumulated discount rate over the periodrequired for the asset to become non-default status will be treated ascollection.

Financial institutions should clarify the above points as much as possible and thus
collect LGD data in a consistent manner. It is preferable to establish a system that
automatically detects aberrant values in the process of data gathering.
In LGD estimation and facility ratings assignment, factors affecting LGD (see the chart
above), relationship between these factors and LGD, and sensitivity of LGD to thesefactors should be taken into consideration. The following process may be modeled forthis purpose: (1) the process of defaulted assets returning to non-default status, (2) theprocess of collection of collateral provided for defaulted assets, and (3) the process ofcollection of asset portions uncovered by collateral in view of seniority andcharacteristic of industry.
Financial institutions should also consider the issues related to (1) methods to secure anadequate level of risk homogeneity from the perspective of LGD for each rating
categories or pools (e.g. if distribution of LGD for each rating category does not follow
normal distribution, financial institutions are expected to explain why) and (2)validation of LGD by backtesting or other tools (in the case where the LGD follows
non-normal distribution, expert judgement could also be used for evaluating
performance of a model).

19

Attachment
List of Study Group Members
Mizuho Financial Group
Risk Management Division
Tadahiko Onoyama
Risk Management Division
Hiroshi Taguchi
Credit Risk Management Division
Yoshiyuki Okubo
Credit Risk Management Division
Yusuke Oyama
Sumitomo Mitsui Banking Corporation
Credit Planning Department
Toshiaki Nakai
Credit Planning Department
Hirokazu Shindo
Credit Planning Department
Takeshi Abiko
Corporate Risk Management Department Hiroo Sugai
Corporate Risk Management Department Akiya Watanabe
Corporate Risk Management Department Yoshitaka Ando
Bank of Tokyo-Mitsubishi UFJ
Credit Policy & Planning Division
Credit Policy & Planning Division
Credit Policy & Planning Division
Credit Policy & Planning Division
Credit Policy & Planning Division

Hiroshi Nagasawa
Takashi Mamizuka
Koujirou Nakazawa
Senshin Masui
Tomoya Mizogami

Bank of Japan
Center for Advanced Financial Technology
Center for Advanced Financial Technology
Center for Advanced Financial Technology
Center for Advanced Financial Technology
Center for Advanced Financial Technology
Center for Advanced Financial Technology

Tsuyoshi Oyama
Nobuyasu Obata
Masao Yoneyama
Jun Muranaga
Hidehiko Umeda
Yasunari Inamura

Observing members
Financial Services Agency
Planning and Coordination Bureau (Supervisory Bureau)

20

Shunsuke Shirakawa

Planning and Coordination Bureau (Supervisory Bureau)Supervisory Bureau

21

Yasushi Shiina
Futoshi Ueno