Module Corporates+Banks (Bank A)

Sydney December 11, 2006 Seite 1

Lessons from implementations of Basel II and for Solvency II

- Credit Rating Models for the Banking Book of

Banks

Sydney December 11, 2006 | 24.04.23

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Module Corporates+Banks (Bank A)

• Scorecard-based rating systems • Estimation of individual PDs• Use of a masterscale to transform individual PDs into rating grades• Shadow-Rating, i. e. external ratings as benchmark• Estimation of PDs for each external Rating grade based on the default

history of the rating agencies• Corporates: Division into 12 submodules - six regions each with stock-

exchange quoted and non-quoted companies:1. Germany2. North-America3. Great Britain/Ireland4. Rest of the World - Developed Countries5. Rest of the World - Emerging Markets6. Rest of the World – Other Countries


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Development Data Corporates

Submodule Observations(with external rating)

Stock-Exchange Quoted(with external rating)

Non-Quoted(with external rating)

Total 10821(2119)

3071(1568)

7750(551)

Germany 6548(481)

1147(336)

5401(88)

North-America 1120(713)

625(515)

495(42)

UK /Ireland 654(256)

324(167)

330(60)

Rest of the WorldDeveloped Countries

1418(465)

667(345)

751(63)

Rest of the WorldEmerging Markets

935(148)

259(54)

676(42)

Rest of the WorldOther Countries

146(56)

49(32)

97(14)


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Scoring

Criteria used for Ranking• Quantitative Part of Score: Use of vendor models (Moody´s KMV)

◊ Use of RiskCalc- results based on balance sheet figures◊ For stock-exchange quoted companies in addition KMV-

EDF (expected default frequency) based on stock price • Qualitative Part of Score

◊ 5 to 6 qualitative factors depending on region◊ Discrete response categories◊ Transformation of responses into EDF using the estimated

PDs of the benchmark external Rating


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Calibration

General Approach• Determination of a PD(Score)-function• Benchmark: external ratings and their estimated PDs• Database as shown in slide 3: only data including external

rating can be used for calibration• First guess: Linear Regression between score and the

logarithmic PD of the external rating• Manual adjustments resulting in a stepwise linear function

PD(Score)


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Calibration

Representation of the calibration dataset in the institute´s documentation – submodule Germany, non stock-exchange quoted companies


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Score distribution shows that there is a region of data with low scores that is not represented by the dataused for calibration

Red: used for estimation Grey: whole dataset

Calibration


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If the figure shown in slide 6 is extended to the whole region of existing scores, it reveals the lack of calibration data for scores below -120. Thus the PD(score)-function is obtained via extrapolation Remark: the lower and upper bounds of

the x-axis correspond to the minimum and

maximum score values in the dataset

Yellow line: result of linear regression

Grey line: result of manual adjustment

Grey dotted line: maximum PD of the model

Calibration


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Calibration

Second Example:Submodule North America,non quotedcompaniesRemark: the lower and upper bounds of the x-

axis correspond to the minimum and

maximum score values in the dataset

Yellow line: result of linear regression

Grey line: result of manual adjustment

Grey dotted line: maximum PD of the model

Transparent circles: Airlines


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In this submodule extrapolation and restriction of the model PD to 20 % result in a rating distribution where nearly 15% of the obligors are assigned to the lowest rating grade “21”

Rating distribution submodule North America

non- quoted companies

Calibration


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Distribution of Scores belonging to the lowest Rating grade “21” may suggest that there is a differentiation of risk in the lowest rating grade which is not reflected by the rating system.For Comparison: the rating grades 1 to 20 cover a score range from -60 to 70.

Calibration


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Definition of Default

Point of View of the institute´s model validation team:• Definition of Default according to the Rating Agencies and

according to Basel II are almost identical• Argumentation:

• Similar verbal definition• Backtesting with internally observed defaults delivers no

statistical evidence of underestimating the PD (binomial test based on a sample containing 14 defaults) indirect argument that definitions are similar


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Point of view of the Audit team:• Definition of Default according to the Rating Agencies and

according to Basel II are different.• Argumentation:

• Rating agencies are not able to observe all criteria belonging to the Basel II definition of default (asymmetric information)

• There even exist differences between the default definition of Rating agencies, e.g. Moody´s refers primarily to rated bonds rather than to other liabilities as for example bank loans


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Analysis of the validation data: • 400 datasets carry a default flag

• 53 of these include an external rating• from these 53 the external rating reflects a default state in

only 14 cases The ratio 53/14 is an indication that there are differences

between the default definitions


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However, the ratio 53/14 overestimates the effect:• Rating agencies may react after the institute has observed a

default (time delay)• Credit officer does not neccessarily update the information

about the external rating for internally defaulted obligorsFurther analysis performed by the institute suggests a scaling factor of about 1.2 between internal and external default rates for this sample.


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Module Global Corporates & Banks (Bank B)

• Expert- Systems• Scorecard-based ratingsystem• Use of a Masterscale with fixed PDs• 26 rating grades (iAAA to iD), of which 7 default-grades (iCC+

and below)• Benchmark: Judgement of a group of Senior Credit Officers• Shadow-Rating with small development datasets


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Analysis of the data (Global Corporates)

Distribution of Obligors in Rating grades of iCC+ and below:• Not weighted

• Exposure-weighted (possible for data younger than 2004)

Conclusion: Ratingclasses don‘t separate defaulted from not defaulted obligors


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Migrationmatrices

Migrationmatrix 2003 – 2004

• Strange Migration out of class iAAA (and also into class iAAA, which is not shown in the above table)

• Further Analysis showed contamination with so called Facility Ratings, containing information about the transactions (collateral/cash-coverage)

• Severe in historical data, partially even in recent Ratings

Conclusion: a material amount of the ratings contains LGD-components


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Module Corporates (Bank C)

1. initial situation model developing process (MEP)2. design of rating system „Corporates“

2.1. pooling standards2.2. part of past (quantitative part)2.3. part of future (qualitative part)2.4. creditworthiness rating 2.5. support / burden and transfer stop

3. validation


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1. initial situation MEP

basic proceeding • pool project• data used: quantitative ratios out of annual balance sheet and

qualitative ratios (questionnaires), default information provided• data transformation on risk points between 0 and 100. Higher value

means higher risk.• determinating weights by means of which these risk points are

included in the total score (using logistical regressions and adjustment of experts)

• estimation of PD allocated to a score with logistical regression• classifying of these individual PD in a master scale


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data base for model development and validation


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1. initial situation MEP

poor data quality of ratios• ratios out of annual balance sheet are characterized by numerous and

extreme outliers• in approx. 30% of all observations at least one ratio is outside of the 1%

or 99% quantile • ratios of the qualitative section are in some cases significantly beyond

the respective range

- examples are given on the subsequent pages


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Equity capital rate 0,5% to 99,5% quantile


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• fixing five parameters (0,25,50,75,100) and the ranges of value allocated to these five parameters

• generation of clusters depending on regions and sectors• Clustering has a strong impact on model developping processes• Clustering is based on profound expert know-how (e.g. external consultancy)• especially for foreign clusters: external experts

regular check of clustering required

Transformation of the quantitative ratios in risk points


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equity capital rate according to clustering

high absolute frequency with 100 risk points for non-defaulted borrowers

region: Germanysector: capital-intensive


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Agenda

1. initial siutation model developing process (MEP)2. design of rating system „Corporates“


3. validation


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2.1. Pooling Standards

1. population• LRP: switching to gross and net liability according to economic

point of view• method of pool partner is unknown to LRP

2. completeness of data set• different definitions of input box in LRP and pool partner (optional

or compulsory entry) can result in different filling rate of pool input.• example: key figure „short-range supplier credit target“

obliging guidelines for an agreement on a consistent proceeding for all pool partners are meaningful


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2.2. part of past time (quantitative section)

Integration of new quantitative ratios

• The current inventory system „Vorgangsbearbeitung Kredit“ (VK) only contains annual balance sheet items which are an integral part of the current features

• Integration of new ratios basing on other annual balance sheet items is not possible offhand

• This means abandoning historization of the non-rating relevant items


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2.3. part of future

questions:

1. retracebility of the assignment of risk points for the qualitative ratios? 2. consistent rating of similar issues?

Analysis based on internal reports and systems

tendency to central values, no consistent rating, retracebility often not possible


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2.3. part of future

1. „Retraceability“:variable „Strength and weakness profile“ (weigth in total score: 30 %)• Set of six subcriteria with different lists of checkable items• Checklists shall provide retraceability but are not mandatory.

Often they are not filled.

Granularity of retraceability is limited


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2.3. part of future

1. „Retracebility“variable „Strategic planning“ (weigth in total score: 12 %)• over 20% of the data show assignment of 50 risk points (RP)• in most cases rationale for assignment of 50 RP is not clear:

• „real“ assessment based on information• special rules as existing for large corps and their subsidiaries• tendency to central values

• in most cases to all subcriteria 50 RP is assigned


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2.3. part of future

2. „ consistent rating of similar issues“:variable „Steatigic Planning“ (weight in total score: 12 %)• Analysis of data with lack of information• huge difference in resulting RP (see example)

Kundenname Risikopunkte Planung BeurteilungsgrundlageBitburger Getraenke Verwaltungsgesellschaft mbH 30 nicht nachvollziehbarPearson Plc London Gruppe 40 Planung wird unterstelltKnorr-Bremse AG-Teilkonzern 40 nicht nachvollziehbarSPAR Handels-AG 80 nicht nachvollziehbar


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2.4. creditworthiness rating

The experience of the credit experts influenced modelling in the follwoing aspects:• imitation of the existing model selection of model• selection of the analysed ratios selection of criteria • fixing of cluster and classification limits for assignment of risk points

data transformation• fixing of weights of quantitave and qualitative ratios determination

of score function• composition of qualitative key figures regarding content

characterization of qualitative key figures


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Analyses executed by regulators

• reconstruction of modelling and score computation on basis of the sample used for the development

• Analog model development and score computation using own estimation of parameters of logistical regression maintaining data transformation (risk points and according limits)

• analysis of impact on allocation of borrowers in rating grades and estimation of PD.


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retraceability of calculations

• estimation of parameters could be traced back by means of documentations and subsequent questionning (relative deviation under 0,1%)

• estimation of parameters for the quantitative ratios are sensitive with regard to different treatment of missing values (relative deviation of more than 20% using the substitution method applied for validation)

• estimation of parameters for the qualitative ratios are sensitive with regard to outliers, especially beyond the interval [0,100] (relative deviation of more than 15% for significant parameters, more than 50% for less significant ones)

• influence of individual extreme outliers on the coefficents used for the estimation of PD: 1,5% on the intercept, 2,5% on the slope (3544 observations, relative deviation)


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comparison LRP-Model with the purely statistical model


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Difference of rating grades

impacts on total borrowers in-sample:Expert-driven model assigns worser rating grades

impacts on defaulted borrowers: Expert-driven model assigns too optimistic rating grades


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Comparison of discriminatory power

variations of discriminatory power can be mainly observed in the lower areas for bad borrowers


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estimation of PD

impacts on the determined PD• estimation of parameter with logistical regression yields different

results different functional relation between score and PD: expert-driven model more conservative für low scores (good borrowers), to progressive for higher scores (bad borrowers)

• different distribution of scores different distribution of PD

• small variation in average, but strong impact on single borrowers


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conclusions

• Due to the high importance of qualitative ratios, quality assurance of inputs is treated with special importance.

• The influence of experience of credit experts on the different steps of modelling should be checked within validation.

• In-sample shows the expert-based model weaknesses especially with regard to the allocation of worse borrowers.

• Analog analysis should be executed out-of-sample and out-of-time .


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adjustment on the rule-conform default definition

adjusted default defintion (Annex 7 Part IV para 44-46), thus means higher default rate

• in the historical data these information are not available• determination of a scaling factor of 1,18 by means of few

observations (59) scaling of the average default rate with this factor

• in the long run a more differentiated consideration of the rule-conform default definition will be necessary

• other default definition (e.g. delay of payment) will cause probably the default of other borrowers

• new default definition is already established, with an increasing data set generated out of the new rating system, we are able to consider appropriately the rule-conform default defintion


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LRP-specific PD

adjustment on the own mean default rate: The LRP notices in their inhouse sample a lower default rate than on the pool sample and thus prefers to calibritate on its inhouse mean default rate.

• Pool: total default rate: 243/20773 = 1.17 % complete records: 99/8780 = 1.13 % mean Individual-PD: 1.13% mean PD after allocating to rating grades: 1.19%

• LRP: total default rate: 26/2737 = 0.95% complete records: 26/2734 = 0.95% mean Individual-PD : 1.32% mean PD after allocating to rating grades : 1.40%


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comparison of rating distributions


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Relevant para from the directive

• annex VII, part 4, para 57(a): If a credit institution uses data that is pooled across credit institutions it shall demonstrate that:(a) The rating systems and criteria of other credit institutions in

the pool are similar with its own;• annex VII, part 4, para 49:A credit institution’s own estimates of

the risk parameters PD, LGD, conversion factor and EL shall incorporate all relevant data, information and methods...

• modifications of relevant informations of pool data before use possible and necessary respectively, eg. different distributions

• to keep in mind: issue may change due to changement in default definition


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2.5. Support/Burden and Risk of Transferstop

Consideration of further risk drivers, which are not only dependent on the observed borrower:• Support/Burden: Modification of individual creditworthiness rating

when borrower is backed up (Support), or debited (Burden) by a company

• Transferstop: Modification of creditworthiness rating (after application of Support/Burden) at existing transferstop-risks


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Support/Burden (S/B)

• Rating-Agent determines, if there is a linkage between borrower and company in terms of support or debts

• Possible degree of linkage (30%, 50% or 70%) as well is determined by Rating-Agent

• The S/B-providers rating is set in the height of the determined percentage of the rating

• In case of rerating of a S/B-provider the ratings of all S/B-clients have to be checked. Information concerning S/B-clients can be provided not until the planned system-upgrade in 2006.

• Multiple-layered relations can emerge. The timeliness of the incorporated ratings has to be looked after.


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Agenda

1. initial siutation model developing process (MEP)2. design of rating system „Corporates“


3. validation


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3. Validation

Basic Approach• Analysis of the discriminative power• Binomialtestings, because of scarce data with huge exception-

areas• Determination of quality-measures (Brier-Score, Devianz etc.)• Determination of the significance of weights, new rating on trial

basis• Change of weighting in the scorefunction only for huge aberrations• Recalibration of the functional relation between score and loss

probability• Frequency: annual


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Validation in the Transition phase

• Partial use of the development-sample• Slow creation of database from the data of the new rating-

system• The keyfigure of “strong/weak points” from the qualitative area

has been changed in its compound. For reasons of compatibility to the historic inventory these keyfigures will be transformed back to their former composition. This figure‘s discriminative power as well as its influence on the scorefunctions composition can be statistically analysed not until a sufficiently enclosing database is built.

• Transitional solution with regard of the extended solution of losses (Annex 7 Part IV Nr. 44-46)

Module Corporates+Banks (Bank A)

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