credit risk irb approach2

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Credit RiskInternal Rating Based Approach

Laurent Balthazar

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AgendaAgenda

1. Introduction to IRB2. Risk parameters in IRB

• PD: Minimum requirements and operational implementation• LGD: Minimum requirements and operational implementation• EAD: Minimum requirements and examples• Maturity: Minimum requirements and examples

3. Risk quantification• Understanding the Risk weighting function • Detailed issues of Risk quantification

4. Credit Risk Mitigation5. Securitization6. Application for supervisory approval

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AgendaAgenda





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1. Introduction to IRB: The 3 pillars

Basel Committee

• The BCBS was formed in 1974 by the Group of 10 central bank governors following the failure of West German bank BankhausHerstatt

• Committee members include representatives from Belgium, Canada, France, Germany, Italy, Japan, Luxembourg, the Netherlands, Spain, Sweden, Switzerland, the U.K. and the U.S.

• From its inception, its primary mission has been to promote stability in the global banking system in the pursuit of two guiding principles

• No foreign banking system should escape supervision• Supervision must be adequate for all banks operating internationally

• Its primary objective is to formulate standards, guidelines and best practices that individual authorities will implement

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The 3 pillars: mutual re-enforcement

Pillar 1 : menu of evolutionary approaches• Credit risk• Operational risk• Market risk

Pillar 2 : 4 principles• Internal assessment• Supervisory review• Capital > minimum• Supervisory intervention

Pillar 3 : disclosure requirements

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Scope of application

Holding => Basel 2 rules apply

International Bank => Basel 2 rules apply



Domestic Bank => Control of national supervisor

Investment Bank => Control of national supervisor CRD Directive

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Treatment of participations – Financial Companies

Financial companies (Insurance excluded)

Majority-owned / Controlled

DeductedSignificant investment(e.g. EU : 20%-50%)

Deducted or consolidated on a pro rata basis

Risk Weighted

Minority investments

Minor investment(e.g. EU < 20%)

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Treatment of participations – Insurance CompaniesInsurance companies

Majority-owned / Controlled

Deducted or other method (national

discretion)

Significant investment(e.g. EU : 20%-50%)

Deducted or other method (national

discretion)Risk Weighted

Minority investments

Minor investment(e.g. EU < 20%)

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Treatment of participations – Insurance CompaniesCommercial companies

Majority-owned / Controlled and Minority

investments

Amounts of participations up to 15% of banks capital (individual exposure) or 60% of banks

capital (aggregated exposure)

Deducted Risk Weighted

Amounts superior to those thresholds

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1. Introduction to IRB: Pillar 1

Pillar 1 : menu of evolutionary approachesCredit risk• STA• FIRBA• AIRBAOperational risk• BIA• STA• AMAMarket risk• STA• AMA

Economic Models

Rough weights

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1. Introduction to IRB: Pillar 1

Pillar 1 : solvency ratio

Total Eligible Capital

Credit Risk

- Standard Approach- IRBF Approach- IRBA Approach

Market Risk

- Standard Approach- Internal Model Approach

Operational Risk

- Basic Indicator Approach- Standardized Approach- Advanced Measurement Approach

≥ 8%

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1. Introduction to IRB: Credit risk

Credit risk

Standard approach• Continuation of Basel 88 • New: internal ratings (+ asset class): 0%, 20%, 50%, 100%, 150%

Foundation Internal Rating Based Approach• RWA= f (PD, LGD, EAD, M, Confidence level, Correlation)• Banks estimates only PD

Advanced Internal rating Based Approach• RWA= f (PD, LGD, EAD, M, Confidence level, Correlation)• Banks estimates PD, LGD, EAD, M

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1. Introduction to IRB: Why IRB ?

Why choosing IRB ?

Standard approach

• Simpler, less requirements• Not always more capital requirements

IRB• If there should be only one reason:

Banks should be managed in a economical way !

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Why choosing IRB ?

•Complex function integrating risk parameters• Derived from state of the art economical model

• Simple weights• No economical foundationPortfolio Risk

Assessment

• Internal ratings and PD• All portfolio might be covered• Internal LGD• Extended recognition of collateral

• Only external ratings• Implied PD given by regulator• Large part of portfolio unrated (externally)• No explicit LGD• Limited recognition of collateral

Individual Risk Assessment

IRBStandardSTD vs IRB

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Why choosing IRB ? => Macro view

% are contribution from portfolios to total capital changeAverage gain for advanced approaches

QIS 3 – G10 Banks Standard Approach FIRB approach AIRB approach Portfolio Group 1 Group 2 Group 1 Group 2 Group 1 Corporate 1% -1% -2% -4% -4% Sovereign 0% 0% 2% 0% 1%

Bank 2% 0% 2% -1% 0% Retail -5% -10% -9% -17% -9% SME -1% -2% -2% -4% -3%

Securitised assets 1% 0% 0% -1% 0% Other 2% 1% 4% 3% 2%

Overall Credit Risk 0% -11% -7% -27% -13% Operational risk 10% 15% 10% 7% 11% Overall Change 11% 3% 3% -19% -2%

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Why choosing IRB ? Macro view

View by individual portfolioRetail are the winners, Equity and sovereign the losers

QIS 3 – G10 Banks Standard Approach FIRB approach AIRB approach Portfolio Group 1 Group 2 Group 1 Group 2 Group 1 Corporate 1% -10% -9% -27% -14% Sovereign 19% 1% 47% 51% 28%

Bank 43% 15% 45% -5% 16% Retail (Total) -21% -19% -47% -54% -50% - Mortgage -20% -14% -56% -55% -60%

- Non-Mortgage -22% -19% -34% -27% -41% - Revolving -14% -8% -3% -33% 14%

SME -3% -5% -14% -17% -13% Specialised Lending 2% 2% n.a. n.a. n.a.

Equity 6% 8% 115% 81% 114% Trading book 12% 4% 5% 4% 2%

Securitised assets 86% 61% 103% 62% 129%

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Why choosing IRB ? Micro view

Min and Max impact at bank levelAverage variation low, but at individual bank level might be very important

QIS 3 – G10 Banks Standard Approach FIRB approach AIRB approach Group 1 Group 2 Group 1 Group 2 Group 1

Maximum 84% 81% 55% 41% 46% Minimum -15% -23% -32% -58% -36% Average 11% 3% 3% -19% -2%

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1. Introduction to IRB: From proposal to law

IRB in national laws

3-level structure

• Basel Committee : recommendations and best practices

• EU : directives

• Countries : local law

open options

open options

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1. Introduction to IRB: Exposure classes

Exposures class => Determine the risk weight function

1) Corporate, sovereign, banks: same function (>< Standard)• Corporate:

- SME specific treatment (adjustment of RWA)- Specialized Lending: SPV, reimbursement based on cash-flows of

financed asset.

Real estate with high volatility. To be defined by regulators.

High volatility Commercial real estate

High correlation between PD and cash flows generated by the property

Income producing real estate

e.g.: oil, metals, crops …Commodities financee.g.: ships, aircrafts, satellites …Object financee.g.: power plant, chemical plants, mines…Project finance

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• Sovereign:- Countries- Central banks- Multilateral Development Banks- Public Sector Entities (PSE) considered as sovereigns by regulators

(tax raising power)- International organizations RW 0% in standard (e.g.: BIS, IMF …)

• Banks:- Financial institutions- PSE not considered as sovereigns

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2) Retail: 3 functions (>< Standard)

• Mortgages:- Exposure covered by residential mortgage- No size limit- If rented must be limited

• Qualifying revolving exposures:- Exposures: revolving, unsecured, uncommitted- On private person- Max 100.000 EUR- Bank must demonstrate low volatility of loss rate

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• Other retail:- Loans on a group < 1Mio EUR- Large number of exposures in portfolio- Must be managed on a pool basis- Individual analysis does not prevent mass treatment

3) Equity: 1 function (but standard method allowed in IRB)

- Recovery only by selling or when bankruptcy occurs- No obligation form issuer - Obligation from issuer but: can be delayed, reimbursement can be

done in equity- Includes securities when income is function of equity- Includes securities eligible as Tier 1

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4) Eligible purchased receivables: function Corp or retail

- Claims purchased to another counterparty- can be retail or corporate in function of the nature of the claim

5) Securitisation exposure: special treatment: Standard or SF

- Not based on legal definition but economical one- Securitised exposure as soon as there are at least 2 tranches - Special attention as often used for regulatory capital arbitrage

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1. Introduction to IRB: Terminology

IRB ingredients

Symbol Name Comments

PD Probability of Default It is the probability that the counterparty will not meet its financial obligations

LGD Loss Given Default It is the expected amount of loss that will be

incurred on the exposure in case the counterparty defaults

EAD Exposure at Default

It is the expected amount of exposure at the time when a counterparty will default (the expected

drawn-down amount for revolving lines or the off-balance sheet exposure times its CCF)

M Maturity The average maturity of the exposure

ρ Asset correlation A measure of association between the evolution of assets returns of the various counterparties

CI Confidence Interval The degree of confidence used to compute the economic capital

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IRB : sources of parameters estimation

NB: ρ and CI always given by regulators

IRB Foundation IRB Advanced Exposure type Internal data Regulators data Internal data Regulators data

Corporate, Sovereigns, Banks, Eligible

purchased receivables Corp

PD LGD, EAD, M PD, LGD, EAD, M

Retail, Eligible Purchased receivables

retail Internal PD, LGD, EAD, M mandatory

Equity PD/LGD approach or market based approach

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IRB : for each exposure class, 3 components

1. Risk Components

2. Risk weight function

Capital requirements

3. Minimum Requirements

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1. Introduction to IRB: Roll out

Principle: if IRB, must apply to all asset classes.

Exceptions:

• Specialized Lending• Securitisation• Non-material exposures

For the rest:

• Roll out plan• Timing and resources• To be approved by regulators

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1. Introduction to IRB: Transition period

Transition period of 3 years• Beginning at implementation (31/12/2006 IRBF, 31/12/2007 IRBA)• Data requirements may be relaxed :

2000 2001 2002 2003 2004 2005 2006 2007 2008 2010

LG D : 7 years

E AD : 7 years

PD : 5 years(*)

R etail: 5 years (*)

R ating system : 3 years (*)

Relaxed: 2 years

Parallelrun

Transition period: 3 years

2009

(*): re laxed during transition period

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1. Introduction to IRB: Transition period

Transition period of 3 years

• But Capital Gains are limited

• 2009 … ?

Approach From Year-end 2005

From Year-end 2006

From Year-end 2007

From Year-end 2008

IRBF Parallel run 95% floor 90% floor 80% floor IRBA - AMA Parallel run or

impact studies Parallel run 90% floor 80% floor

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Agenda





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2. Credit risk in IRB: PD Minimum Requirements

Dimension of rating system

• Corporate, Sovereign and Banks

- 2 dimensions: PD and LGD (>< issue rating !)- 2 exposures on same borrower => same rating (except sover. Ceiling and

guarantees)- There must be policies to guide to classification in a rating grade

• Retail

- Exposures must be classified in homogenous pools that share risk characteristics- For each pool a PD must be estimated (and LGD and EAD)

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Rating structure

• Corporate, Sovereign and Banks

- Concentration issue (no more 30% in one grade)=> Concentrated portfolio ex: PSE at Dexia ?- Min 7 pass ratings and 1 for default

• Retail

- Number of pools and number of exposures in each pool sufficient to validate PD, LGD and EAD parameters

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Rating criteria

- There must be clear and coherent definition to classify exposures in rating grades, which means:

• Consistency across businesses, departments and geographical locations• Third parties (auditors, regulators…)should be able to reproduce the process

- Bank should integrate all available information- An external rating can be a basis but other info must be incorporated

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Rating horizon

- PD on 1 year- Ratings on longer horizon- Rating: reimbursement capacity under adverse economic conditions- PIT Vs. TTC issues (procyclicality…)

Today

Business Cycle

Time

PIT rating

Average TTC rating

Stress TTC rating

?

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Use of models: Current market practices

SME Portfolio

Corporate portfolio

Retail portfolio

Bank and sovereign portfolio

Weight of scoring model in final rating

Weight of human expert in final rating

Statistical Models

Constrained expert models

Expert Models

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Use of models:

Requirements

- May be used as a primary input for the rating- But only uses a subset of available info => cannot be the only tool- Human oversight necessary- The bank has to demonstrate the discriminatory power of the model- The bank must have procedures to check quality of inputs- Bank must demonstrate that the dataset used for construction isrepresentative of the bank borrower- Human judgment must integrate elements not taken into account in the model- Ongoing monitoring, validation and assessment of the quality of the model

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Documentation of rating system

- The bank must document extensively its IRB system- Documentation should highlight:

rating criteriarating responsibilitiesrating exceptionsreview frequency (in principles min 1 per year)management overview

- Documentation of default definition- If a model is used:

Mathematical hypothesisValidation out of the time and out of the sampleModel weaknesses

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Coverage

- Each Corp/ sovereign/ Bank exposure should have a facility rating- Each retail exposure should be classified in a pool- Each guarantor should be rated

Integrity

- Independency of the rater (FO / RM separation)- Yearly review (may be done by sampling for retail)- Efficient procedures to collect new info on a borrower

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Overrides

- Overrides = change to the model rating - The bank must define in which cases it is allowed- Overrides must be identified and followed separately- Each guarantor should be rated

Data maintenance

- The bank must collect and historize all the data necessary for re-rating - Corporate, Sovereign, Banks: Store rater name, rating data, Default Rates, Migrations- Retail: Store data that allows to classify in pools, observed DR

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Corporate governance and oversight

- The board must validate main aspects of the rating system and must understand it - There must be regular discussion about the rating system quality between management and risk control.- Ratings included in reporting to senior management- The bank must have an independent risk control unit: model development, monitoring, policies …- Independent review at least once a year by audit or other similar independent function

⇒ Regulators will look at reports of meetings (Board, Risk Control, audit…), and they will question them !

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Use of internal rating systems

Ratings and PD must be used for

- Credit Approval- Risk Management- Economic Capital Allocation- Corporate Governance

⇒ PD’s of Basel 2 and Internal PD’s might be different (e.g.:pricing…) but coherency must be demonstrated.

IRB Compliant systems must be used at least 3 years before implementation

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Overall requirements

- PD must be long term average of 1 year default rates (except for retail)- Pooling authorized but data must be comparable- Estimations must be grounded in experience- Economic climate at the time of collection must be taken into account- Usually a margin of error is included => conservatism

⇒ Often not enough data internally: conservative bias that might not be used for internal risk management

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Default definition

90 days delayUnlikely to pay

- Default indicators:Non accrued statusProvisionsSelling at material lossRestructuration with lower NPVFilled for bankruptcy

- Retail: default might be at facility or obligor level (no contagion mandatory)

Calibration issue !

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Re-ageing policy

Regulators do not want the bank to play with rules:

There must be a clear policy for past due credits:

Who can approve them ?Minimum age of facility to be eligible for re-ageingNumber of re-ageing authorized per facilityNew risk assessment for re-aged facilities

Use test (treatment of re-aged exposures >< defaulted exposures)

Overdrafts

If new limit, must be communicated to the client ! (no internal game)

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Requirements specific to PD estimation

Corporate, sovereigns, banks:

- Internal long run experience must be incorporated- Judgment must be taken into account- If mapping to external data: default definition and rating criteria must be documented - Minimum 5 years of data

Retail

- Internal data should be primary source- It should integrate seasoning effects in case of portfolio growth

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Requirements specific to qualifying purchased receivables

- Legal certainty: the bank must ensure it has control on reimbursements- Monitoring of the quality of the pools:

correlation between seller and receivablessellers/ servicers monitoringfraud detectionhistorical data on the poolconcentration monitoring

- The bank must be able to verify that the seller/ servicer respect the contract covenants

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Validation of internal estimates

- The bank must have clear policies and pre-defined triggers to determine when the deviation between expectations and observed values should lead to a review of the parameters

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2. Credit risk in IRB: PD operational implementation

How to estimate a PD on each exposure and fulfilling those requirements ?

Operational development of a rating framework

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1st issue: do we need a model ?

• Not mandatory in Basel 2 text

• Even, in CP3 warning about the use of models

⇒ But: how to fulfill requirements otherwise ?

• Uses only a part of the info• Is correct in 80% of the cases• Cannot treat outliers• If fundamental underlying characteristics of the population changes, the model does not integrate it

• Systematic in the rating process

• Can be reproduced by third parties

• Statistical tests can prove discriminatory power

Models weaknessesModel advantages

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Solution: take the best of the 2 worlds

⇒ Construct a rating framework that combines the use of models with human oversight and adjustment (constrained expert model)

Basel 2 Credit Analyst

Automated

Scoring Model

Traditional

Expert analysis

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Which quantitative technique to use ?

1) Regression techniques

- Multivariate Discriminant Analysis

- Ordinary Least Square

- Logistic regressione.g. Logistic regression

0

0.2

0.4

0.6

0.8

1

1.2

0 2 4 6 8 10 12 14 16 18

Financial ratios

defa

ult (

1) /

not d

efau

lt (0

)

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2) Structural Models: e.g. KMV

Current asset value

Current value of debts

Potential value of assets in the future

1 year

PD= f (asset value, asset volatility, debt value)

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3) Expert systems: e.g. Decision trees

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4) Inductive learning: e.g. Neural Networks

Inputs

Results

TRIAL

MODIFY

Wi

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Comparison

Criteria Statistical techniques

Inductive learning techniques Structural models

Applicability + + - (Limited to listed companies)

Empirical validation (out of the sample and out of the time

tests)

+ + +

Statistical validation + - (No weights that can be statistically

tested)

n.a. (Parameters must not be statistically tested as they are derived from an

underlying financial theory)

Economical validation

+ (We can see if the weights of the various ratios

corresponds to the weight expected by

specialists)

+ (The impact of the ratios can be

estimated using sensitivity analysis)

++ (Structural models are the only

derived from a financial theory)

Market reference

++ (Riskcalc of Moodys, Fitch IBCA

scoring models, various models used by central banks of

France, Italy, UK…)

+ (No model directly based on neural networks to the extend of our

knowledge, but a model of S&P is based on Support

Vector Machines that is derived from NN)

+ KMV model

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Finding data: 3 sources

• Default data- Most objective

- But often scarce data

- Basel 2 definition ?

• External ratings- Especially for certain portfolios (Corp, Sovereign, Banks)

- Indirect way to modelize defaults

• Internal ratings- When no other data available

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Regression techniques

• Default data- Binary issue (default=1, not default=0)

- e.g. Binary logistic regression

• Ratings- Multi-class issue

- e.g. Ordered logistic regression

- Rating coding: AAA=1, AA+=2, AA=3, … B-=16, CCC=17

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How many models to construct ?

• Should integrate- Number of type of counterparties (retail, Corp, banks…)

- Model by sector, region … => Data availability issue !

- Model use outside its development population (Generic Vs

Specific model)

• 2003 Study- US banks: 5 non retail and 3 retail on average

- European banks

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Sector issue ?

• A specific model may be constructed on a given sector, or sector variable may be included (1/0)

• But pay attention !- More overfitting risk

- Variation in sector fundamentals not anticipated

- e.g.: utilities sector and deregulation

- e.g.: sensitivity to the stage of business cycle if few data

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Modelization steps1. Data collection and cleaning

5. Validation

2. Univaried analysis

3. Ratio transformation

4. Regression analysis

6. Calibration

7. Test phase

8. Production

Regular

Feed-Back

to Credit

analysts

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1. Data collection and cleaning

- Which data sources are available ? => Internal, external DB, data pooling …

- What kind of data do they contain ? => potential explanatory variable (fin. statements, age …)

- What is the quality of the data ?=> Missing values, extreme values …=> Potential bias (survivor bias, size bias …)

- How to clean the data ?=> rating or default date vs fin. Statement availability => replacing or excluding missing values=> polluted data (Shareholders, country ceiling …)

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- Rating distribution issue

Which distribution ?=> Bell shaped ? => Flat ?=> keeping concentration ?

Classical distribution

02468

101214

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

rating

freq

uenc

y

Concentrations

02468

101214

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

rating

freq

uenc

y

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- Fraction issue

Which ratio is the best ?- denominator negative values ! - Ordering !

=> Those values have to be treated

Profit / Equity Equity/ LT fin debt Profit Equity Result Equity LT fin Debt Result

10 100 10% - 10 100 -10% -10 10 -100% -10 -100 10% -100 10 -1000%

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- Treatment of size variable

=> To get good results in regression analysis, variable distributions should not be too far from the bell shape

=> Some variable may need to be transformed

Frequency of total assets

0

200

400

600

800

1000

1200

1400

1600

734

420,7

7584

0,816

1,260

,858

1,680

,899

2,100

,940

2,520

,981

2,941

,022

3,361

,063

3,781

,105

4,201

,146

4,621

,187

5,041

,228

Assets in 000 EUR

Freq

uenc

y

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- Studying outliers

• “Quick and dirty” model to identify outliers

• May we find a reason for that ?

• if it is the case, can we exclude those observations ?

=> Pay attention to keep objectivity and to document choices

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- Analysts feed-back

=> OK with potential variable ?

=> OK with treatment of missing values ?

=> OK with treatment of extreme values ?

=> OK with sample composition ?

=> ratio definition ?

=> Outliers excluded ?

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2. Univaried analysis

- Study of the relationship between each variable and the average PD / the average rating

Graph 11.5 ROA - default dataset

0%

10%

20%

30%

40%

50%

60%

-20% -10% 0% 10% 20% 30%

ROA

Ave

rage

Def

ault

rate

Graph 11.4 ROA - rating dataset

02468

1012141618

-15.0% -10.0% -5.0% 0.0% 5.0% 10.0% 15.0% 20.0% 25.0%

ROA

Rat

ing

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- Univariate predictive power

- Shape of the relationship (monotonic or not)

- What is the range of efficiency ?

- Does the relationship make sense ?

0

2

4

6

8

10

12

14

16

18

0.0% 50.0% 100.0% 150.0% 200.0% 250.0%

Cash/ ST debts

Rat

ing

e.g. Liquidity ratio on large corporate rating dataset

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=> Does the relationship makes sense ?

=> OK with rejected ratios ?

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3. Ratio transformation

- Can be done in many ways:

Polynomial function (e.g. Moody’s uses DR)Cap: minimum and maximum values

- Mandatory for non monotonic ratios, usefull for other

- Univaried analysis can be a basis for Caps

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=> Does the function makes sense (no overfitting) ?

=> Does min and max values seems reasonable ?

72


4. Regression analysis

- e.g. Logistic regression

- Divide sample in construction and backtesting sample

- Selection process:

ForwardBackwardStepwiseManual

- Choose a performance measure: economic vs statistical

[ ])...(exp11

2211 cxbxb +++−+=π

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=> OK with selected variables ?

=> Replacing ratios with correlated one even if less performing ?

=> Model acceptance is key issue

74


5. Validation

- On construction sample and on backtesting sample, ideally out of the sample and out of the time.

- Performance measures:

- Accuracy ratio (default)- Cumulative notch difference (ratings)- Cost function

Economic tests- p. value- Log likelihood- Wald test- R squared- Goodness of fit

Statistical tests

75


- Main tools

CND

0%10%20%30%40%50%60%70%80%90%

0 1 2 3

Perfect model

Tested model

100%

0%

% of defaults

ScoreDefault rate of the sample

Naive model

AR

76


- Benchmarking performances:

• Publicly available models (Altman, gloubos grammatikos …)• External commercial tools• Performance level published by other banks• Best ratio• Blind tests

=> but performance depends on particular sample characteristics !

77



=> opinion on model performance

=> opinion on zone where model is under performing

=> rating stability versus reactivity

=> weight of the different variables (e.g.: size for Corp at Dexia)

78


6. Calibration

- If default dataset is used:

• Does it correspond to Basel 2 definition• If not, which adjustments (constant over rating scale) ?

- If rating dataset:

• Which source of info on default (e.g. public statistics of rating agencies) ?• Coherence of reference dataset and default definition

=> Calibration function of application (pricing, Basel 2 …)

79


7. Test phase

- Double run of some month for the analysts- Feed back from analysts (validation)- Detection for underperformance

8. Production

- Building definitive IT tools (feed back from analyst on user-friendliness)

80


Qualitative assessment

- Scoring tool delivers a first rating, but only based on financial assessment

- Qualitative elements have to be integrated

=> in analyst freedom ?

=> or scorecard approach ? +-

-+SubjectivityFlexibility

81


Qualitative scorecard

- Develop list of questions with credit analysts- Expert weighting of questions- Rate a sample of counterparties- Study link between qualitative score and rating- If ok, combines qualitative score and financial score

Qualitative score

Very good Good Neutral Bad Very BadAAA 0 0 0 -6 -7AA 1 0 0 -5 -6A 2 1 0 -4 -5

BBB 3 2 0 -3 -4BB 4 3 0 -2 -3B 5 4 0 -1 -2Fi

nanc

ial s

core

Impact in steps

82


Typical rating sheet

83


Typical rating system structureScoring module based on financial ratios=> Financial rating

Overruling of the analyst=> Final rating before sov ceiling

Qualitative scorcard basedOn a list of questions=> Qualitative score

Potential second rating after sovereign ceiling

Combination of both=> Model rating

84


Pre-defining the overrulings

- Shareholders- New credit- Exceptional year- …

=> Allows a more formal follow up of them

85


Typical timing (15 months)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Data collection and cleaning

Univariate analysis

Model Construction

Test phase

IT tools definition

Training and

production

Final validation and first

backtesting

86


Organizational issue

- We have a model to systematize the rating process- But there is still freedom for the analyst- Statistical development is not complicated, but will the model be used correctly ?

=> operational implementation in organizational structure is the key

Model developers Credit analysts

Quality control

87


Quality control: role

- Function appearing in a lot of banks

- Main roles:

• Control that models are applied correctly• Quality of the inputs• Coherency of answers to questions• Support model use and testing

=> Main role: analysis of overrulings

=> This function is key for regulators (reports)

88


Quality control: functioning

Credit Analysts

Model developers

Independent review (senior analyst)

Fill ratios and qualitative scorecards

Model rating

Give the final rating to the

borrower

Analysts rating

Difference is within accepted

degree of freedom (e.g. 1 step)

Both analysts and model agrees on the rating – no additional control needed

No agreement on overruling:

discussion (rating

committee)

Agreement on overruling: report to developers

Regular joint back-testing of the model

89


Organization at Dexia

- Who is involved in rating models ?

=> Risk= too much control …

Model developer

Credit analysts

Quality Control

Validation department

Rating Committee

AuditGroup of rating follow up

Rating system

90


Conclusions on setting up rating models

Key issues for success

- Data, Data, Data …

- Model acceptance by users => Integration of experts in the whole development process

- Support from senior management => resistance to change (micro view >< macro view)

- Role of quality control => also for model acceptance

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Agenda





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2. Credit risk in IRB: LGD Minimum requirements

Basic requirements for LGD estimation in IRBA

- History of recovery data in databases- Conservative bias- Business cycle of collected data taken into account- Compliance with Basel 2 default definition- A LGD must be estimated on each facility- Min = long run default weighted average loss rate

Corporate, Bank, Sovereign

- Min 7 years of history

Retail


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2. Credit risk in IRB: LGD measures

How to estimate an LGD on each exposure and fulfilling those requirements ?

Operational measurement of LGD

94


Introduction

- Requirements are less numerous than for PD- Might seems easier at a first sight

But …

- less research than on PD- More country specific issues- When going in the details, complexity increases importantly

Challenge…

- PD is based on fixed event at a given date- LGD is a dynamic issue that may last several years

95


Source of LGD data

Market LGD

- Secondary bonds prices- Objective as assets can effectively be sold

Implied LGD

- Derived from asset pricing model- e.g. Bond spreads

Workout LGD

- Effective recoveries after workout process- Discounted at time of default

96


Quality of LGD data

Market LGD

- limited to listed bonds- Secondary prices OK if it is the policy of the bank- If default weighted, might be bias because of demand / supply

Implied LGD

- no standard asset pricing models- e.g. Bond spreads: part of PD, LGD, Liquidity, influence of interest rates …

Workout LGD

- Internal data, objective- few historical data available in most cases

97


Workout LGD

- Economical loss => including costs and discount effects

EADr

tseriesre

LGDt

tt∑ +−

−=)1(

coscov

1

EAD is directly linked to LGD

=> Both should be treated at the same time

98


Example

- A default occurs on a facility of 1 Mio EUR- At the time of default, the facility is used for 500.000 EUR- There is a cost for a legal procedure of 1.000 euro one year after the default- After 2 years the bankruptcy is pronounced and the bank is paid back 0.2 Mio EUR-The discount rate is 5%

%64000.500

05.1000.200

05.11000

12

=+

−

−=LGD

99


Issues around LGD Computation

1. Costs

2. Null LGD

3. Default duration

4. Discount rate

100


1. Costs

- All costs (direct + indirect) should be integrated

- Scope: legal department, credit analysis, risk monitoring … limit ?

- Allocation key for indirect cost: fix, by exposure amount, …

=> Not important at portfolio level, but might be important at facility level (pricing …)

101


2. Null LGD

- LGD might be bull or negative (when contract penalties, or settlement through physical collateral)

• A/ Integrating negative values in average LGD ?• B/ Censoring data (min 0%) ?• C/ Censoring LGD + PD for coherency ?

⇒ B preferred by regulators⇒ C coherent but changes Basel 2 default definition⇒ A …

102


3. Default duration

- If credit is called by the bank at default => OK- But if it is not the case (intensive care), what is the treatment of …

• Lines at default that do not exist at the end ?• Drawn dawn amounts after default (LGD or EAD) ?• Changes of collateral

Default Credit is called

End of workout process

- Lines paid back- New lines- New collateral

- Lines arefrozen

- Recovery process

103


4. Discount rate

- One of the more fundamental questions- Basel 2 text not clear on this issue

‘Firms should use the same rate as that used for an asset of similar risk. They should not use the risk free rate or the firm’s hurdle rate…’ (CP 189, FSA)

‘A bank must establish a discount rate that reflects the time value of money and the opportunity cost of funds to apply to recoveries and costs. The discount rate must be no less than the contract interest rate on new originations of a type similar to the transaction in question, for the lowest-quality grade in which a bank originates such transactions. Where possible, the rate should reflect the fixed rate on newly originated exposures with term corresponding to the average resolution period of defaulting assets’ (pub 8/4/03, Federal Reserve).

104


- Then, in principle market rates for such assets

• But how to estimate ? By comparing secondary market prices of bonds with future cash-flows from worked out recoveries

• Basel 2 default definition >< market (rating agencies)

• Assets without secondary markets (e.g. Mortgages) ?

• Historical rates or implied future ones (Rf 70’s 10%, 90’s 4%)?

105


- Impact might be important: Study of Moral and Oz (2002) on Spanish mortgages

- Applied 3 scenarios

• Rate specific to each facility (rate before default)• Average discount rate (ranging from 2% to 6%)• Forward rates on several periods

⇒ Discount rate +1% = increase of LGD by 8%

⇒ Using different forward rates (rolling period 900 days) results in a maximum difference of 20%

106


Public studies

- Often banks do not have sufficient internal data

- Public studies usually rely on market LGD

Author Period Sample LGD Type Statistics Altman and

Vellore 1982-2001 1.300 Corporate bonds Market - Average 62.8%

- PD and LGD correlated

Araten, Michael and

Peeyush 1982-1999

3.761 large Corporate loans of

JP Morgan Workout

- Average 39.8% - Stand. Dev. 35.4% - Min/ Max (on single loan) –10%/173%

Ciochetti 1986-1995 2.013 commercial mortgages Workout

- Average 30.6% - Min/ Max (annual) 20%/38%

Eales and Edmund 1992-1995

5.782 customers (large consumer loans and small business loans) from Westpac Banking Corp

(Australia), 94% secured loans

Workout

- Average business loan 31%, Median 22% - Average Consumer loans 27%, Median 20% - Distribution of LGD on secured loans is unimodal and skewed towards low LGD - Distribution of LGD on unsecured loans is bimodal

Gupton and Stein 1981-2002

1800 defaulted loans, bonds and preferred stocks

Market

- Beta distribution fits recovery - Small number of LGD<0

Hamilton, Parveen,

Sharon and Cantor

1982-2002 2.678 bonds and

loans (310 secured)

Market

- Beta distribution skewed toward high recoveries - Average LGD 62.8%, Median 70% - Average LGD secured 38.4%, Median 33% - PD and LGD correlated

107


Main findings

- High volatility of LGD

- LGD distributions far from normal

Bimodal distribution

01020304050607080

0% 10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

LGD

Freq

uenc

y

Unimodal (Beta ?) distribution

010203040506070

0% 10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

LGD

Freq

uenc

y

108


LGD drivers

- Moody’s launched a model Losscalc that try to predict LGD

- What influences the LGD level

• Seniority• Secured/ unsecured

• The rating• The country

• The industry• Macro economic drivers• Capital structure

Clear, no doubt

Seems confirmed by several studies

More data needed to confirm

109


PD/ LGD correlation

- One of the most crucial and controversial issue in the industry- Often modeled as a fix parameter in risk management- Most recent studies tend to show that it is correlated- But based on market LGD … supply / demand bias ?

=> Study of Altman: fixed LGD against moderate correlation with PD. 30% of difference for capital at 99.9%

30.1%

29.6%

Diff fix/ correl

1053

564

Correlated LGD

814

437

Volatile LGD

809

435

Fixed LGD

99.9% VAR

99% VAR

Statistic

110


Stressed LGD

‘A bank must estimate an LGD for each facility that aims to reflect economic downturn conditions where necessary to capture the relevant risks…In addition, a bank must take into account the potential for the LGD of the facility to be higher than the default-weighted average during a period when credit losses are substantially higher than average... For this purpose, banks may use averages of loss severities observed during periods of high credit losses, forecasts based on appropriately conservative assumptions, or other similar methods.’

(see article 468 of ICCMCS)

=> Link to PD / LGD correlation issue

111


Arguments against stressed LGD

- The industry had an important reaction against this issue- LGD is the more sensitive parameters in capital requirements, they argued:

• Already conservative bias in other parameters (PD, CCF,…)• Add-on of 6% (Madrid compromise)• Correlation hypothesis high against market standards• Double default effect limited• Conservatism in collateral valuation• Diversification of different portfolios (sectors, geographical locations …)• Diversification of product types

=> An additional stress on LGD is not necessary, and correlation at global level has still to be proved

112


Advanced Basel IIcompliance

Model complexity

LGD RecoveryLGD Basel II(transitions)

LGD Basel II(costs

add-ons)Basic

Complex

Step 1 Step 2… 3

::

113


Assess Loss Given Default and...Basic stepping stones

• First step• rely on the most observable data, on a best effort basis• start from a legal fixed point, namely the recovery world• take the obtained results as a Basel II conservative point of view

• Second step• translate the “recovery” results to the Basel II world

• Third step• enhance the results by costs estimation

Increase afterwards the modelling complexity

… solve the EAD issues at the same timeAs similar data sources addressed

114


...PUK

NLL

Country dimension

B

Advanced Basel IIcompliance

Model complexity

Step 1 Step 2… 3

Complex::

Basic

115


The country dimension• brings a lot of issues related to

• the processes (e.g. legal framework)• the data chains (less developed), and the necessity of,• and… the inherent scarcity of the data

• and therefore forces us to retain a pragmatic approachIt is our aim

• to privilege the developments where this can be ensured on a larger data scale,)• to derive from the Loss Given Default rates developments rates for the other

countries (= second direction), by applying an expert country correction coefficient function of:

• the legal framework• internal or external benchmarks

116


Conclusions on LGD measures

- Fewer focus received from the industry up to now

- But has the greater influence on capital level

- Principles are easy but in practice we face a lot of questions

- Most banks will start with “recovery LGD” as a prudent first guess

- LGD very dependant on local regulation => limited international data pooling

- Stressed LGD required by regulators will be discussed

117

Agenda





118

2. Credit risk in IRB: EAD requirements and examples

Basic requirements for EAD estimation in IRBA

- History of exposure data in databases

- Conservative bias

- Business cycle of collected data taken into account

- Compliance with Basel 2 default definition

- A CCF must be estimated on each facility

- On balance min EAD= exposure

- Off-balance: EAD must estimate possible drawn dawn amount at defaultand after default

- Long run average default weighted, with safety margin if correlation between PD and EAD

119


Corporate, Bank, Sovereign


Retail


2 types of EAD

- The one linked to the behavior of the client: will he draw its line, will it use its guarantee

- The one linked to derivatives instruments, they are function of the evolution of risk parameters (interest rates, exchange rates,…)

120


EAD linked to client behavior

- CCF have to be estimated on each product type (e.g. double CCF for guarantee lines ?)

- E.g. CCF of foundation approach

0% - Commitments unconditionally cancellable without prior notice.

20% -Short term self-liquidating trade-related contingencies (e.g.: documentary credit collateralized by the underlying goods) - Undrawn commitments with an original maturity of maximum 1 year

50% - Transaction related contingencies (e.g.: performance bonds) - Undrawn commitments with an original maturity greater than 1 year

100%

- Direct credit substitutes (e.g.: general guarantees of indebtedness…) - Sale and repurchase agreements - Forward purchased assets - Securities lending

121


EAD= Current exposure + CCF x undrawn part of credit line

- For LGD we need to look at default time and after- For EAD we need to look at default time, after, and before

⇒Is the CCF 100% ? Bank penalized for a good management of risky clients ?

Line

Exposure

Default1 year before

122


Current Mark-to-Market

Add-on = Potential Mark-to-Market or

OTC Derivatives contract residual maturity

Credit Risk

Maturity

Total Credit Risk = Current Credit Risk + Add-On

EAD linked to market parametersOTC Derivatives Credit Risk is a function of two major drivers :

• The Current Credit Risk: equal to current Mark-to-Market• The Potential Future Credit Risk or Add-On: due to potential future evolution of

Mark-to-Market

123


Forward BuyNov 2002

The current value of a transaction: assessed by calculating the value this transaction would have if it were immediately hedged in the financial markets.

Default

Default

Negative Mark-to-Market

Positive Mark-to-Market

Negative MtM: The value of the transaction is negative. The transaction is ‘out of the money’. The counterparty has a credit exposure on Fortis Bank.

Positive MtM: The value of the transaction is positive. The transaction is ‘in the money’. Fortis Bank has a credit exposure on the counterparty.

124


Maturity

Add-On

OTC Derivatives contract residual maturity

Reflect the evolution of the OTC Derivatives Credit Risk in the future

?

• Modelled as depending on the future volatility of the market risk factors underlying the derivative contract

• Statistically build by MB Risk Management: comparable methodology to assessment of market Risk

125


Regulatory add-on

For instance, if a bank has concluded a 3 years interest rate swap with another bank, on a notional amount of 1000 EUR whose market value is currently 10 EUR, the credit-equivalent would be

10 EUR (MTM value) + 1000 EUR × 0.5% (PFE) = 15 EUR

Residual

Maturity

Interest

rate

Exchange rate

and gold Equity

Precious

metal

Other

commodities

≤ 1 year 0.0% 1.0% 6.0% 7.0% 10.0%

1 – 5 years 0.5% 5.0% 8.0% 7.0% 12.0%

≥ 5 years 1.5% 7.5% 10.0% 8.0% 15.0%

126


In order to reduce Economic Derivatives Credit Risk, it is possible to apply the following Credit Risk Mitigation Factors:

• derivatives collateral management: securities posted to cover net MtM above a specified trigger

• derivatives close-out netting: Offsetting of receivables and liabilities in case of an event of default of the counterparty (only with MtM deals)

• break clause: bilateral contingent option to early cancellation at pre-fixed dates

127


Definition: Offsetting of receivables and liabilities in case of an event of default of the counterparty (only with MtM deals)

Conditions requested by Regulator for each counterparty to apply Close-Out Netting :

• Signed Master Agreement with the counterparty.• Favourable legal opinion on the Close-Out Netting in the country of incorporation of

the counterparty • Legal opinion define the products and type of counterparty (corporate, bank, financial

institution, etc...) that can be accepted in a Close-Out Netting process. Offsetting takes place within Netting Pools : set of transactions that are eligible

for Close-Out NettingCountries allowing corporates to apply for Close-Out Netting: Australia, Austria,

Canada, Denmark, Finland, Hong-Kong, Ireland, Italy, Japan, The Netherlands, Norway, Portugal, Singapore, UK, USA, South Africa, Sweden, Switzerland

128


Exposure calculation in case of netting

The PFE is adapted with the following formula:

0.4+0.6×NGR

with NGR being the ratio of the netted MTM value (set to zero if negative) to the gross positive MTM values.

=> Offset of collateral agreement partially recognized at the add-on level

129


Example

Notional CCF MTMContract 1 1000 EUR 1.0% +100 EURContract 2 2000 EUR 5.0% -30 EURContract 3 3000 EUR 6.0% -40 EUR

NGR = 30 EUR (netted MTM of 100 – 30 – 40) / 100 EUR (sum of positive MTM)=0.3

PFE (without netting)=(1000 × 1% + 2000 × 5%+ 3000 × 6%)= 290 EUR

PFE (corrected for netting) = (0.4 + 0.6 × 0.3) × 290 EUR = 168.2 EUR

Credit-equivalent = 30 EUR (net current exposure)+168.2EUR= 198.2 EUR

130


Derivatives collateral management implies • Daily Mtm value calculation• Daily Margin calls if the Net Current Mark-to-Market is bigger than a predefined

threshold• Threshold often equal to zero in order to eliminate the capital consumption• Only Cash and OECD government bonds are accepted as collateral• Internal Add-ons are less severe (one month add-on is used)• Documented by a Credit Support Annex to ISDA AgreementsExample : FX deal collateralised - Residual maturity : 4 years

1Y 2Y 3Y 4Y

Maturity

Add-on value [% of the Notional Amount]

6m1m

14.0%

11.5%

9.2%

7.0%

4.9%3.5%

One month Add-on due to collaterisation

131


• Total maturity is considered to select add-ons curve.• Break-clause date reflects date at which the credit risk exposures stops

Maturity

Add-on value [% of the Notional Amount]

Break-Clause Date Deal Maturity Date

Credit Risk Exposures

132

Agenda





133

2. Credit risk in IRB: Maturity requirements and examples

Maturity calculation

- In IRBF, fixed at 2,5 years (6 months for repo style transactions)

- In IRBA calculated as

- Min 1 year, Max 5 years

- If the bank cannot apply the formula => Final maturity

- Netting: average maturity

- National discretion: break of the 1 year min (e.g.: Repo, Forex, Securities Lending…)

∑∑

CFtCF

134

Agenda





135

3. Understanding RWA function

Goal of part 3: understanding the formula …

136


1. The RWA function

2. What is a risk ?

3. Correlation issue

4. The Merton process

5. The formula: stressed default rate

6. The formula: Confidence Interval

7. The formula: expected loss deduction

8. The formula: Maturity adjustment

9. The formula: Madrid issue

10. Weaknesses of the approach

137


1. The RWA function

PD

LGD

EAD

Maturity

Correlation

Confidence interval

IRB

FIR

BA

Regulators

Full model recognition:

Basel 3 ?

F(x) = Capital requirements

Capital requirements / 8%= RWA

138


2. What is a risk ?

- Formula delivers capital to cover credit risk

- But what is a risk ?

A/ Next year salaries of employees

B/ Next year credit loss on large retail portfolio

C/ Next year amortized value of the building

D/ Next year revenue on corporate business line

=> Risk is the Unexpected

139


Expected versus unexpected loss

• Expected loss • Anticipated average annual loss rate• Foreseeable cost of doing business• Differentiated cost of risk recovered through pricing

• Unexpected loss• Unforeseeable • Unanticipated Losses • Requires balance sheet cushion of capital• Differentiated capital sustained with appropriate return

140


3. Correlation issue

- What creates the risk is the correlation of defaults (simul 3% PD)

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

4.00%

4.50%

100 500 1,000 10,000 50,000

Portfo lio size

defa

ult r

ate

Year 1Year 2Year 3Year 4Year 5Year 6Year 7Year 8Year 9Year 10

141


- S&P historical default rates

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

Year

Def

ault

rate

Average

Deviation ? = Basel 2

issue

142


4. The Merton process

- Default generating process- E.g.: assets=100, expected return=10%, volatility of asset value 20%,

debt=80

0%2%4%6%8%

10%12%14%16%18%20%

0.0

20.0

40.0

60.0

80.0

100.

0

120.

0

140.

0

160.

0

180.

0

200.

0

Assets and debts value

Freq

uenc

y

Assets value Debts

143


- Correlation is introduced in Basel 2 as:

R(A) = w1 R(E) + w2 R(a) R(B) = w1 R(E) + w2 R(b)

Total Return of

companies

Correlated

Return of economy

Systemic risk

Stand alone Return of

companies

Idiosyncratic risk

144


5. The formula: stressed default rate

⎟⎟⎠

⎞⎜⎜⎝

⎛

−

+ −−

ρφρφ

φ1

)()( 11 CIPD

Asset correlation(Inverse)Normal

distribution

Probability of default

Confidence interval

With some statistical developments …

145


- The formula allows to estimate the loss distribution

0.0010.0020.0030.0040.0050.0060.0070.0080.0090.00

100.00

0.15

%0.

33%

0.40

%0.

46%

0.51

%0.

56%

0.61

%0.

66%

0.70

%0.

75%

0.80

%0.

85%

0.90

%0.

96%

1.02

%1.

08%

1.15

%1.

23%

1.32

%1.

43%

1.57

%1.

76%

2.06

%2.

93%

Default rate

Freq

uenc

y

Corp: PD 1%, Correl 20%

=> Basel 2 capital= 14.6%

Qual. Rev. Expo: PD 1%, Correl 4%=> Basel 2 capital= 4.1%

0.0020.0040.0060.0080.00

100.00120.00140.00160.00180.00200.00

0.00

%0.

03%

0.05

%0.

08%

0.11

%0.

14%

0.17

%0.

21%

0.26

%0.

31%

0.36

%0.

42%

0.50

%0.

58%

0.68

%0.

79%

0.93

%1.

10%

1.32

%1.

60%

1.99

%2.

58%

3.69

%7.

81%

Default rate

Freq

uenc

y

146


- The different risk classes are associated to different risk weighting function, the only difference is correlation

SME

Retail

Large CorporatesUL

UL

UL

147


6. The formula: Confidence Interval

⎟⎟⎠

⎞⎜⎜⎝

⎛

−

+ −−

ρφρφ

φ1

)()( 11 CIPD

Confidence interval ?

- Used for all statistical measures

- Signification: not enough capital =>

Accepted Bankruptcy

- Calibration: Desired PD ?

148


- Used for all statistical measures

1 Year PD CIAAA 0.01% 99.99%AA+ 0.02% 99.98%AA 0.03% 99.97%AA- 0.04% 99.96%A+ 0.05% 99.95%A 0.06% 99.94%A- 0.07% 99.93%

BBB+ 0.18% 99.82%BBB 0.34% 99.66%BBB- 0.72% 99.28%BB+ 0.91% 99.09%BB 1.15% 98.85%BB- 2.68% 97.32%B+ 3.95% 96.05%B 9.07% 90.93%B- 13.84% 86.16%

⎟⎟⎠

⎞⎜⎜⎝

⎛

−

+ −−

ρφρφ

φ1

)999.0()( 11 PD

⇒ Basel 2 calibrated for A- / BBB+ bank

149


7. The formula: expected loss deduction

Risk= Unexpected

⇒ Expected loss is a cost that should be integrated in the pricing

⇒ Up to know only PD was considered, we introduce LGD

PDPD

−⎟⎟⎠

⎞⎜⎜⎝

⎛

−

+ −−

ρφρφ

φ1

)999.0()( 11

xLGDPDPD

⎥⎥⎦

⎤

⎢⎢⎣

⎡−⎟

⎟⎠

⎞⎜⎜⎝

⎛

−

+ −−

ρφρφ

φ1

)999.0()( 11

150


• Likelihood that losses will exceed the sum of Expected Loss (EL) and Unexpected Loss (UL)

• 100% minus this likelihood = confidence level• Capital set according to the gap between EL and VaR, and EL is covered by

provisions or revenues, likelihood that the bank will remain solvent over a one-year horizon = confidence level.Under Basel II, capital is set to maintain a supervisory fixed confidence level

151


8. The formula: Maturity adjustment

- Input = PD 1 year => output = capital 1 year

- Why not 5 years ? Why not until maturity of the credit ?

We can work on 1 year horizon because we suppose that we can sell all the bank assets …

Assets

100 EUR credit BBB(e.g. 20Bp EL

5% at 99.9%

5 years)

Liabilities

5 EUR capital

95 EUR debt

Enough ?

152


Stressed year !

- 5 EUR loss because of default => covered by capital

- 95 EUR of credit still at risk and no more capital => we sell them

- But …

Assets

100 EUR credit BBB (20 Bp EL

5% at 99.9%

5 years)

Liabilities

5 EUR capital

95 EUR debt

New rating year end

% of portfolio Spread New value

AAA 0% 0.05% 0.0AA 5% 0.10% 4.8A 10% 0.20% 9.5

BBB BBB 50% 0.30% 47.5Spread BB 20% 1.00% 18.50.30% B 10% 2.00% 8.9

Default 5%Intitial Value Final Value

95 89Delta

-6

153


- Regulators used MTM credit VAR models to calibrate Basel 2 formula

- And smoothed results through regression

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

1 2 3 4 5

Maturity

Reg

ulat

ory

capi

tal

0.1% PD0.5% PD1.0% PD1.5% PD

154


- The formula:

))5.2(1()5.11(

11

)999.0()( 11

xbMxxb

xLGDxPDPD

−+−⎥

⎥⎦

⎤

⎢⎢⎣

⎡−⎟

⎟⎠

⎞⎜⎜⎝

⎛

−

+ −−

ρφρφ

φ

))²ln(05478.011852.0( PDxb −=

155


9. The formula: Madrid issue

- Madrid compromise: regulators accepted EL deduction but introduced a

corrective factor (6% add-on)

With ))²ln(05478.011852.0( PDxb −=

06.1))5.2(1()5.11(

11

)999.0()( 11

xxbMxxb

xLGDxPDPD

−+−⎥

⎥⎦

⎤

⎢⎢⎣

⎡−⎟

⎟⎠

⎞⎜⎜⎝

⎛

−

+ −−

ρφρφ

φ

May be reviewed based on QIS 5 results

156


SUMMARY

06.1))5.2(1()5.11(

11

)999.0()( 11

xxbMxxb

xLGDxPDPD

−+−⎥

⎥⎦

⎤

⎢⎢⎣

⎡−⎟

⎟⎠

⎞⎜⎜⎝

⎛

−

+ −−

ρφρφ

φ

Stressed DR RecoveriesEL

deduction

Maturity adjustment

(MTM)

Safety margin

ProbaDefault

Asset correl

(Merton) CI for BBB+ / A- rating

157


10. Weaknesses of the approach

- Critics of the industry

• Correlation structure too simplistic

• 5 year cap for Maturity adjustment

• Unique confidence interval

• Concentration risk not recognized (single name and industry)

• Fixed LGD

• Single approach (Merton type) kills research

=> Internal credit VAR models ? Future of Basel 3 ?

158

Agenda





159

3. Detailed issues of Risk quantification

1. Calibration of RW functions

2. Correlation level

3. Example of RWA

4. Specialized lending

5. Equity exposures

6. Purchased receivables

160


1. Calibration of RW functions

The calibration intended in CP 2 (January 2002)

QIS2 results (Spring 2002)G10 average, after incorporating someof the working paper proposals

0102030405060708090

100110120130

Cur

rent

Acc

ord

Stan

dard

ised

App

roac

h

Foun

datio

nIR

B

Operationalrisk

Creditrisk

0102030405060708090

100110120130

Cur

rent

Acc

ord

Stan

dard

ised

App

roac

h

Foun

datio

nIR

B

Source: CBF

Operationalrisk

Creditrisk

The calibration intended in CP 2 (January 2002)

QIS2 results (Spring 2002)G10 average, after incorporating someof the working paper proposals

0102030405060708090

100110120130

Cur

rent

Acc

ord

Stan

dard

ised

App

roac

h

Foun

datio

nIR

B

Operationalrisk

Creditrisk

Operationalrisk

Creditrisk

0102030405060708090

100110120130

Cur

rent

Acc

ord

Stan

dard

ised

App

roac

h

Foun

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B

Source: CBF

Operationalrisk

Creditrisk

Operationalrisk

Creditrisk

161


Source: CBF

The calibration aimed at for in CP3

0102030405060708090

100110120130

Cur

rent

Acc

ord

Stan

dard

ised

App

roac

h

Foun

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B

Review of the Standardised Approach calibration

QIS2.5(Nov.2001)

Further review of the IRB calibration

QIS3(Oct.2002)

G10 average, after incorporating someof the working paper proposals

0102030405060708090

100110120130

Cur

rent

Acc

ord

Stan

dard

ised

App

roac

h

Foun

datio

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B

QIS2 results (Spring 2002)

Operat.risk

Creditrisk

Operat.risk

Creditrisk

Source: CBF

The calibration aimed at for in CP3

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100110120130

Cur

rent

Acc

ord

Stan

dard

ised

App

roac

h

Foun

datio

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B

Review of the Standardised Approach calibration

QIS2.5(Nov.2001)

Further review of the IRB calibration

QIS3(Oct.2002)

G10 average, after incorporating someof the working paper proposals

0102030405060708090

100110120130

Cur

rent

Acc

ord

Stan

dard

ised

App

roac

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Foun

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B

QIS2 results (Spring 2002)

Operat.risk

Creditrisk

Operat.risk

Creditrisk

Operat.risk

Creditrisk

Operat.risk

Creditrisk

162


2. Correlation level

SME

Retail

Large CorporatesUL

ULUL

4%QRE

15%

16%-3%(function of PD)

8% - 20%(function of Turnover

and PD)

12%-24%(function of PD)

Mortgages

Other retail

SME

Corp, Sovereign, Banks

High because of maturity

European pressure

163


Retail Correlation

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

0.00% 2.00% 4.00% 6.00% 8.00% 10.00% 12.00% 14.00% 16.00%

PD

Cor

rela

tion

Asset correlation for corporate portfolios

10%

12%

14%

16%

18%

20%

22%

24%

26%

0.00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% 7.00%

PD

Ass

et c

orre

l

Corporate Other retail

164


SME

0.01

%

0.80

%

1.70

%

2.60

%

3.50

%

4.40

%

5.30

%

6.20

%

7.10

%

8.00

%

8.90

%5

25

455.0%

10.0%

15.0%

20.0%

25.0%

Correl

PD

Turnover Mios EUR

165


Impact on SME: a revised pricing ? Customer Condition

SharedFixed Costs

Marginal Costs

Transfer price

Options:• Reservation• Max. Price• Early Term.

Market price

Provision(Expected Loss)

ALM

Cré

dits

Profit margin

Bus

ines

s Li

ne

Capital(Unexpected Loss)

CostsProduct

Cost BL

YES

NO

?

Funding

166


Capital Required For €100 Unsecured Loan Basel I €100 X 100% X 8% = €8.00 Corporate Retail

20% €1.60 50% €4.00 100% €8.00

Basel II Standardised Approach

€100 X

150%

X 8% =

€12.00

€100 X 75% X 8% = €6.00

€5m €50m

*11.3% - 14.4% €0.90 to €1.16 4.45% €0.36 X

€100 X 8% = X

Basel II IRB Approach (PD 0.03% to 20%; LGD 45%)

188% to 238%

X 8% =

€15.07 to €19.06

€100 X

100.3% €8.02

From Basel II Annex 3 *Shows Firm –size adjustment effect for Sales of €5m and €50m

167


3. Example of RWA

PD (%) BRW PD (%) BRW140.03190.05290.1450.2700.4810.51000.7

1 125192224633315482105881562520

Original calibration !

168


Risk Weight IRBF / Standard

0,00%

50,00%

100,00%

150,00%

200,00%

250,00%

300,00%

0,00%

0,05%

0,14%

0,33%

0,75%

1,30%

2,00%

3,00%

5,00%

10,00

%

Risk Weight IRBFCorporateRisk weight IRBF SME

Risk Weight StandardECAI RATEDRisk Weight StandardECAI UNRATED

BBB+ BB+ BB- B+

169


Maturity : 2,5y Sovereign Retail Other RetailLGD : 45% Corporate Residential Retail Qualifiying

PD Bank Mortgage Revolving0.03% 14.44% 4.15% 45.00% 0.98%0.05% 19.65% 6.23% 6.63% 1.51%0.10% 29.65% 10.69% 11.16% 2.71%0.25% 49.47% 21.30% 21.15% 5.76%0.40% 62.72% 29.94% 28.42% 8.41%0.50% 69.71% 35.08% 32.36% 10.04%0.75% 82.78% 46.46% 40.10% 13.80%1.00% 92.32% 56.40% 45.77% 17.22%1.30% 100.95% 67.00% 50.80% 21.02%1.50% 105.59% 73.45% 53.37% 23.40%2.00% 114.86% 97.94% 57.99% 28.92%2.50% 122.16% 100.64% 60.90% 33.98%3.00% 128.44% 111.99% 62.79% 38.66%4.00% 139.58% 131.63% 65.01% 47.16%5.00% 149.86% 148.22% 66.42% 54.75%6.00% 159.61% 162.52% 67.43% 61.61%10.00% 193.09% 204.41% 75.54% 83.89%15.00% 221.54% 235.72% 88.60% 103.89%20.00% 238.23% 253.12% 100.28% 117.99%

THE 4 CURVES

RISK WEIGHTED ASSET DERIVATION for Corporate, Sovereign, Bank & Retail Exposures

June 2004Without 1.06 conservative factor

170


4. Specialized lending

250%140%120%95%HVCRE (Basel II not CAD III)

B- C-BB- or B+BB+ or BBBBB- or better

More than 2,5 years

250%115%70%50%Less than 2,5 years

PF,OF,CF,IPRE

WeakSatisfactoryGoodStrong

If NO PD/LGD Corporate Criteria Approach ⇒Supervisory Risk Weights Estimates („Slotting Grid Approach“):

171


5. Equity exposures

Approaches:

• PD/LGD based approach (same as Corporate (LGD 90%), 5 years maturity)

• a methodology based on market risk and stress testing (target: equities heldmainly for capital gains purposes)

• Simple risk weight• Internal models (VAR)

172


Equity exposures subject to PD/LGD Method• PD determined according to methods for corporate exposures.

minimum PD’s :• 0.09% for exchange traded equity exposures where investment is part of long-term

customer relationship• 0.09% for non-exchange traded equity exposures where returns on the investment

based on regular and periodic cash flows not derived from capital gains• 0.40% for exchange traded equity exposures including other short positions • 1.25% for all other equity exposures including other short positions

• LGDPrivate equity exposures in sufficiently diversified portfolios 65%All other exposures 90%

• MaturityM assigned to all exposures shall be 5 years

173


Simple Risk Weight Approach• Risk weight (RW) = 190% for private equity exposures in sufficiently diversified

portfolios.• Risk weight (RW) = 290% for exchange traded equity exposures.• Risk weight (RW) = 370% for all other equity exposures.• Risk-weighted exposure amount = RW * exposure value

Internal Models Approach• Potential loss on the institution’s equity exposures using internal value-at-risk

models subject to the 99th percentile, one-tailed confidence interval of the difference between quarterly returns and an appropriate risk-free rate computed over a long-term sample period, multiplied by 12.5.

• Risk weighted exposure amounts at individual level not less than the sum of minimum risk weighted exposure amounts required under the PD/LGD Approach and the corresponding expected loss amounts multiplied by 12.5.

174


6. Purchased receivables

Retail – “top-down” approach

Corporate:• If such receivables are purchased, banks are expected to estimate the risk

of their default (“bottom-up” approach). • The alternative "top-down“ approach is possible as long as:

• receivables are purchased from third parties,• must not be subject to netting agreements,• if not secured by collateral, their maturity must not exceed 1 year,• concentration limits must be kept.

Note.: „top-down“ means that claims are viewed as one package, each separate claim is not analyzed

175


• Within the IRB approach bank is expected to calculated not only the capital requirement for default risk (like for any other exposures) but also capital requirement for so called „dilution risk“

• The essence of dilution risk is the possible existence of a counter-claim of the debtor against the original creditor.

• For the purpose of calculating the RW for such risk, into the corporate risk function is set LGD = 100 % and the portion of expected loss from the exposure will be equal to PD.

• When the receivable is secured by a guarantee, the procedure is the same as for IRB approach for corporate exposures, regardless whether the guarantee covers the risk of default, dilution risk or both.

176

Agenda





177

4. Credit risk mitigation

1. General issues

2. Funded Versus Unfunded

3. Funded

a. Financial collateral eligibleb. Calculationc. Other collateral eligibled. Calculation

4. Unfunded

a. Eligible instrumentsb. Calculation

178


1. General issues

• Institutions using both the standardized approach, or foundation IRB, may take into consideration the effect of Credit Risk Mitigation (CRM) during RW calculation.

• All possible credit risk mitigation methods must be legally enforceable in all relevant countries.

• In addition, institutions must ensure the efficiency of the entire process and address risks connected therewith.

• Institutions must always ensure compliance with minimum requirements for CRM procedures.

• If all defined requirements are met, the institution may decrease the value of RW or EL in compliance with CAD.

• No exposure with a CRM instrument may have a RW higher than an identical, but unprotected exposure.

• In cases when CRM has been incorporated in the calculation of RWA, the provision on CRM is not further applied.

179


2. Funded Versus Unfunded

Credit Risk Mitigation instruments may be funded or unfunded

For funded instruments the following is required:• Sufficient liquidity of the underlying assets and their low volatility• The institution has the right to keep or sell the assets in case of default or

other contractually determined credit event occurrence• The degree of correlation between the value of the asset and creditworthiness

of the debtor may not be inappropriately high

For unfunded instruments the following is required:• The providing entity is sufficiently trustworthy (CAD sets binding list of eligible

providers)

180


3. Funded: a) Financial collateral eligibleCollateral approach Simple approach Comprehensive approach

Impact on RWA

Covered exposure receives the risk weight of the collateral

with a minimum of 20%

Exposures are reduced by the value of collateral and the net result is risk weighted

as unsecured • Cash on deposits at the issuing banks • Gold • Debt securities rated by ECAI at least: BB- for sovereigns (and

assimilated PSE), BBB- for other, A-3/P3 for short term • Unrated debt securities if they are: issued by a bank, senior, liquid,

listed on recognized exchange • Equities (including convertibles bonds) included in a main index • UCITS (Undertakings for Collective Investments in transferable

Securities) and mutual funds if: quoted daily and invest only in the instruments above

Eligible collateral

• Equities (including convertibles bonds) not included in a main index but listed on a recognized exchange

• UCITS and mutual funds which include such equities

181


Eligibility

• Low correlation between the creditworthiness of the debtor and the value of the collateral.

• Operational requirements – sufficient documentation, risk management processes connected with collateral, at least semi-annual frequency of re-pricing and in cases when the collateral is held by a third party such party may not report it among its assets.

• In addition, under the simple approach with respect to financial collateral it is required that residual maturity of the collateral is not shorter than the residual maturity of the exposure.

182


3. Funded: b) Calculation for financial collateral

• In order to mitigate the capital requirement, banks may use the simple approach replacing the risk weight of the counterparty by the risk weight of the collateral. However simple approach is NOT allowed within IRB.

• Comprehensive (FCCM) approach:• allows a greater ratio of offsetting the exposure by collateral through decreasing

the value of the exposure by the value of the collateral. • The principle of this approach is that the market value of the collateral is adjusted

for the haircut representing the estimate of volatility of such collateral. A different haircut is allocated to the exposure itself. The level of such haircut may be determined:

• by the regulator,• through an estimate made by the bank (VaR model).

• In case the collateral is denominated in a different currency than the exposure itself, another haircut considering currency risk is applied

183


Comprehensive approach

Adjusted value of exposure (E*) is calculated as follows:

E* = max{0,[E × (1+HE) – C × (1-HC-HFX)]}

where:E = is the exposure value according to the standardized or IRB approachHE = discount (haircut) applicable to the given exposureC = present value of the collateral receivedHC = discount (haircut) applicable to the collateralHFX = discount (haircut) for the currency mismatch between collateral and exposure

The relevant haircuts can be calculated in two ways:• use of regulatory estimates,• bank’s own estimates.

184


Haircuts proposed by regulators

Collateral Residual Maturity Sovereign (and assimilated) issuers Other issuers

AAA to AA- and A1 securities

≤ 1 year > 1 year, ≤ 5 years

> 5 years

0.5% 2.0% 4.0%

1.0% 4.0% 8.0%

A+ to BBB- and A2 / A3 / P-3 and unrated

bank securities

≤ 1 year > 1 year, ≤ 5 years

> 5 years

1.0% 3.0% 6.0%

2.0% 6.0%

12.0% BB+ to BB- All 15.0% Not eligible

Main index equities and gold 15.0% Other equities listed on a recognised

exchange 25.0%

UCITS / Mutual funds Highest haircut applicable to any security in which the fund can invest

Cash in the same currency 0.0% Collateral and exposures in different

currencies 8.0%

185


Haircuts adjustment

- Those haircuts are for a 10 day holding period and should be adjusted for other effective holding period with

186


The following minimum holding period should be used

• If there is no daily remargining or revaluation, the minimum holding period has to be adapted upward.

• To transform the supervisory haircuts for the ten days holding period to haircuts adapted for the transaction-holding period, banks have to use the square root of time formula.

20 daysSecured loans10 daysOther capital market transactions5 daysREPO type transactions

187


Worked out example

Example :a bank has a three years BBB bond as collateral to cover a three years secured lending operation. The bond is marked to market every week. The bond issuer is a corporate and the face value is 100 EUR. The haircut is calculated as follow:

• The supervisory haircut for a three years BBB bond issued by a corporate is 6% • The minimum holding period for secured lending is 20 business days• As the bond is not revaluated daily but weekly (every five business days), the

minimum holding period must be adapted to 24 (as there are five days instead of one between revaluations)

• Then the supervisory haircut that is based on a ten days holding period is scaled up using the square root of time formula

• The value of the bond is then 100 EUR x (1-9.3%)= 90.7 EUR.

%3.91024%0.6 == xHaircut

188


Internal measurement of haircuts

- Internal VAR models may be used to estimate haircuts but subject to following requirements

Qualitative criteria Quantitative criteria - Estimated haircuts must be used in

day to day risk management - Risk measurement system must be

documented and used in conjunction with internal exposures limits

- At least annual review by the audit of the risk measurement framework

- Use of the 99th percentile, one-tailed confidence interval

- Use of minimum holding periods as for supervisory haircuts

- Liquidity of the collateral taken into account when determining the minimum holding period

- Minimum one year of historical data, updated at least every three months

189


Zero haircuts for Repo transaction

At national discretion, some collateral can receive zero haircuts when used in REPO-style transactions

- if exposures and collaterals are cash or sovereign

- in the same currency

- there is a daily remargining

- the maximum liquidation period is four days

- with core market participants (sovereigns, central banks, banks…).

190


Netting agreements

AE = max [0; (Σ E - Σ C + Σ (Es x Hs) + Σ (Efx x Hfx))]

With AE = Adjusted Exposure

Σ E = sum of exposures (positive and negative)

Σ C = sum of the values of received collaterals

Es = absolute values of net positions in a given security

Hs = haircut appropriate to Es

Efx = absolute value of the net position in a currency different from the settlement currency

Hfx = haircut appropriate for currency mismatch

191


3. Funded: c) Other collateral eligible

Real estate collateral (CRE / RRE)• Monitoring the market price of the real estate - with at least an annual

frequency. In case of loans above 3 mill. € or above 5 % of own capital, an independent appraisal must be performed every 3 years.

• Documentation for all approved types of real estate.• The real estate must be properly insured.

Financial receivables • Claims <1y, repayment will occur through the commercial or financial flows

related to the underlying assets of the borrower.• Legal certainty: the bank can take control of it• Risk management requirements (collection cost, concentration risk, …)

192


Other collateral

Regulators should ensure that:

• Liquid markets• Public prices

Banks requirements:

• First claim• Loan agreements should describe valuation procedure• For inventories: physical inspection• Collateral accepted, valuation methods should be described in banks

procedures

193


3. Funded: d) Other collateral calculation

IRBF

1/ Minimum collateralization test2/ collateral haircut3/ New LGD

Collateral type Minimum collateralisation

Collateral Haircut Final LGD

Receivables 0% 125% 35%

CRE / RRE 30% 140% 35%

Other physical collateral 30% 140% 40%

194


IRBF Worked out example

Exposure of 100 EUR secured by a commercial real estate of 40 EUR would be valued as follows

• 40 EUR / 100 EUR = 40% which is superior to the 30% minimum collateralisation level.

• haircuted by 140%, 40 EUR/140%= 28.6 EUR. • LGD applied on the part of the exposure corresponding to the haircuted value

would be 35%.

⇒The LGD on the 100 EUR exposure would then be 45% (assuming senior corporate exposure) on 71.4 EUR and 35 % on 28.6 EUR.

195


IRBA

• the rules are less strict as any kind of collateral can be recognized

• deduction from the exposure to compute the capital requirements

• as long as the bank has historical data to support its valuation (at least 7 years of data on average recovery value on the various types of collaterals it plans to use).

⇒ Collateral effect in IRBA is much more important

196


Maturity mismatch

Maturity mismatch is a situation when the residual maturity of the credit protection instrument is shorter than the one of the given exposure.

Maturity of exposure is defined as the longest possible residual period until the debtor meets its obligations (maximum 5 years).

Maturity of protection is defined as the shortest possible delay when the credit protection can be cancelled.

Calculation:• In general, the value of CRM instrument is multiplied by:

• where:• t … is maturity of protection (in years)• T … is maturity of exposure (in years)

,25,025,0

−−

Tt

197


4. Unfunded a) Eligible instruments

Recognized unfunded CRM: only the following credit protection providers of unfunded protection may be recognized in IRBF:

• central banks and sovereigns,• local governments and PSE,• multilateral development banks,• international organizations (with a 0 % RW with the standardized

approach),• Financial institutions,• corporate with A- minimum• Must cover capital and interest

IRBA:

• No limit

198


Beside above mentioned instruments some types of credit derivativesmay also be used.

Some of them are treated as unfunded (=guarantees)

• Credit Default Swaps: if protection is similar to guarantees• Total Return Swaps: except if premiums included in P&L and fluctuation of asset

values not in MTM

Some of them as funded (=collateral)

• Cash funded CLN: considered as collateralized transaction

199


Requirements: Guarantees

- Guarantor should be rated (annual review)- Effect of guarantor can be recognized in PD or LGD (IRBA) - Cannot result in RWA inferior to a direct exposure to guarantor- Guarantee should be written, unconditional, non-cancellable, up to maturity

Requirements: Credit derivatives

- Credit events should cover: default, bankruptcy, restructuration with lower NPV (or partial recognition)

- The one who determines default cannot be protection seller alone- If a reference asset is used: should be at least pari passu, same issuer

200


4. Unfunded b) Calculation

Base case: substitution

• For the secured part of exposure (coverage adjusted for currency and maturity haircut), PD of the credit protection provider is used instead of PD of debtor (and LGD if better).

• The uncovered part of the exposure still has a PD of the debtor and LGD according the underlying exposure.

• The value of Guarantee must always be adjusted in case of a currency mismatch between the exposure and protection instrument of a currency haircut HFX (the haircut is calculated in the same way as under the financial collateral comprehensive method):

)H(1GG FX* −×=

201


Critics of the industry

• No recognition of double default effect (e.g. AA covered by AA no effect) => No incentive for those efficient risk management tools

• But simple PD product supposes independence => again a correlation issue

• July 2005: proposal of Basel 2

• But limited to professional protection providers

202

Agenda





203

5. Securitization

• Banks must apply the securitisation framework to determine the capital requirements for exposures resulting from traditional, synthetic and other types of securitisations.

• As securitisation can be structured in several ways, the method how it is treated for capital adequacy purposes must be based rather on its economic substance than on its various legal forms.

• The economic substance of transactions should indicate whether or not they should be considered as securitisation.

204

5. Securitization

Traditional securitisation• A structure when the cash flow from the underlying pool of exposures relates at least to

two different exposures or tranches with different degrees of credit risk.• The payment to the investor depends on the development of the underlying exposure.• Categorized structure (in tranches) characterizing securitisation differs from regular debt

instruments in that the “junior” tranches may absorb losses without affecting more senior tranches.

Synthetic securitisations• A structure of at least two categorized (stratified) risk positions or tranches taking into

consideration a different degree of credit risk while the credit risk of the underlying pool is transferred through guarantees or credit derivatives (funded credit linked notes or unfunded credit default swap).

• The risk of investor again depends on the development of the underlying exposure.

205

5. Securitization

Originator means either of the following:• an entity which, either itself or through related entities, directly or indirectly,

was involved in the original agreement which created the obligations or potential obligations of the debtor or potential debtor giving rise to exposure being securitised;

• an entity which purchases a third party’s exposures onto its balance sheet and then securitises them;

Investor:• institution which is not the originator, sponsor or provider of services and

carries the economic risk of the securitisation exposure.

206

5. Securitization

Capital requirements:

Standardized approach (may be used in IRB)

- Banks that invest in exposures that they originate themselves that receive an external rating below BBB- must deduct them form their capital base

- For off-balance sheet exposures, CCF are used (if they are externally rated, the CCF is 100%).

LT rating (ST

rating)

AAA to AA-

(A-1/P-1)

A+ to A- (A2/P-2)

BBB+ to BBB-

(A-3/P-3)

BB+ to BB-

Other ratings and unrated

RWA 20% 50% 100% 350% Deducted

207

5. Securitization

IRB Approach

The Rating Based Approach (RBA) that must be applied when the securitisedtranche has external or internal ratings.

The Supervisory Formula (SF) is used when there are no available ratings.

The Internal Assessment Approach (IAA) can also be used when there are no available ratings but only for exposures extended to ABCP programmes.

208

5. Securitization

Rating Based Approach

• a risk weight is assigned in function of an external or internal inferred rating (that can be assigned in reference to an external rating already given to another tranche that is of equal seniority or more junior and of equal or shorter maturity)

• the granularity of the pool calculated with N=

• the seniority of the position.

∑∑

²)²(

EADEAD

209

5. Securitization

RW Rating Senior tranche, N≥6

Not senior tranches and

N≥6 N<6

AAA 7% 12% 20% AA 8% 15% 25% A+ 10% 18% A 12% 20% A- 20% 35%

35%

BBB+ 35% 50% BBB 60% 75% BBB- 100% BB+ 250% BB 425% BB- 650%

Long Term ratings

Unrated and < BB- Deduction

A1/P-1 7% 12% 20% A2/P-2 12% 20% 35% A3/P-3 60% 75% 75% Short Term

Ratings Other and unrated Deduction

210

5. Securitization

Internal Assessment Approach

• Only applies to ABCP programmes. Banks can use their internal ratings if they meet some operational requirements, mainly:

- The ABCP must be externally rated (the underlying, not the securitisedtranche).

- The internal assessment of the tranche must be based on ECAI criteria and used in the bank’s internal risk management systems.

- A credit analysis of the asset seller’s risk profile must be performed.

⇒Then, the risk-weight associated to the internal rating is the same as in the RBA

211

5. Securitization

The Supervisory Formula

-is used when there is no external rating, no inferred internal rating and no internal rating given to an ABCP programme.

-The capital requirement is a function of:

- The IRB capital charge had the underlying exposures not been securitised (KIRB)

- The tranche’s credit enhancement level (L)

-The tranche’s thickness (T)

- The pool’s effective number of exposures (N)

- The pool’s exposure weighted average loss-given-default (LGD).

212

5. Securitization

-The tranche’s IRB capital charge is the greater of 0.0056xT or S[L+T]-S[L]. S[L] is the supervisory formula defined as

213

5. Securitization

- Until the sum of the subordinated tranches and tranche for which the capital is calculated is inferior to the regulatory capital had the exposures not been securitised, the capital rate is 100%.

- Then it decreases sharply until the marginal capital rate becomes close to zero, as illustrated on the following graphs (in this example the capital had the exposures not been securitised would be 8.14 EUR, and the credit enhancement equals 5 EUR)

Securitised assets: 100 EUR

K irb= 8.14 EURFirst loss 5 EUR

Bank investment

X EUR

Senior Tranche

(100 – 3 – X) EUR

Bank buys

214

5. Securitization

0

1

2

3

4

5

6

0 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5

S i z e o f t h e t r a n c h e

Cap

ital

S i z e o f t h e t r a n c h e 3 . 1 4C a p i t a l 3 . 1 4

0 %

2 0 %

4 0 %

6 0 %

8 0 %

1 0 0 %

1 2 0 %

0 5 1 0 1 5 2 0 2 5 3 0 3 5 4 0 4 5

T r a n c h e s i z e

Cap

ital /

tran

che

size

S i z e o f t h e t r a n c h e 3 . 1 4C a p i t a l r a t e 1 0 0 %

215

5. Securitization

Comparison between Corporate and securitised exposures

• Ratings of external agencies mainly based on EL measures

• UL may be very different, which is reflected in RWA

0%

200%

400%

600%

800%

1000%

1200%

1400%

AAA AA A+ A A- BBB+ BBB BBB- BB+ BB BB- B+ B B-

Securitisation exposure Corporate Bond exposure

216

Agenda





217

6. Application for supervisory approval

1960 1970 1980 1990 2000 2001 2002 2003 2005 20062004

3-6-3 principle

Liberalisation

1974: G10 launch Basel Committee

1988: Basel I Accord

1990: 100 countries apply Basel I

1996: Market riskJune/1999: CP1

Jan/2001: CP2

Apr&May: QIS1,2

Nov: QIS2,5

Oct: QIS3

May: Release of CP3

July: QIS3 resultspublished

Dec: Finalised Version of the new Accord

Jan: start parallel run

Jan: Basel II comes in force

2007

218


Meetingall concerned supervisors

Local supervisors locallegal entities

Local solvency reporting

CRD local implementation

Current legal requirements

Consolidating supervisor

College of supervisors

ConsolidatedBank

Consolidated solvency reporting

Supervisory review

Single entry pointValidation & permission Act as

ONEcompany

Home-host issue

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Key success and quiet revolution in supervisory world

Framework• Overall responsibilities (going concern & emergency situations)

• Assessment of compliance with the requirements for FIRBA, AIRBA and AMA

• Coordination of gathering & dissemination of information• Planning and coordination of supervisory activities, including Pillar 2• Other specific provisions

• Permission to go FIRBA, AIRBA and AMA• Reach a joint decision within six months• In the absence of joint decision, make its own decision• The decision of the consolidating supervisor will be recognised and

applied by the local supervisors

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To get supervisory approval

- Regulators resources limited => regulators will rely extensively on internal validation

- More important than models is use test => key issue is communication- 4 key dimensions investigated:

Rating system (PD and LGD)

Data management system

Quantification process

Oversight and control mechanisms

Calibration

Monitor

Support

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Statistical validation (non-availability of specific test to be justified)

• Model power statistics• Build in and out sample• Model power statistics for a 5 years’ period before default• Rank ordering comparison with external & internal rating tool • Justification of calibration

Business and Credit Experts validation• Business and credit experts blind test• Business and credit experts pilot phase

Rating system (PD and LGD)

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- Estimation of PD: internal / external ?

- Coherence with bank portfolio ?

- Conservative bias ?

- Business cycle state at time of collection ?

- Correlation between various parameters ?

Quantification process

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- Corporate governance issue ?

- Control of a coherent use of the models ?

- Organization for model approval / review ?

- Follow up of overrulings ?

- Reporting of model results ?

Oversight and control mechanisms

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Example: Dexia Control of rating quality

Rating tool

Quality Control

AuditHead of analysts

Model manager Credit analysts

Rating Committee

Rating follow up group

Validation Department

Validation Committee

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Data management system


-Are the rating tools secured ?

-Are all the data historized (bank able to apply backward model changes) ?

- Do data management systems allows to verify that guidelines arerespected ?

- Integrity of the data used for regulatory capital calculation ?

- Do data management support use test ?

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Primary Validation responsibility = the bank NOT supervisors

AIG subgroup : 6 principles – January 2005

Assessing the predictive ability & use• historical experience, forward looking, discriminating power, reassessment when

divergence from expected results, ….RAM EXPERT Function

Iterative process• changing market and operating conditions• on-going process

No single method• statistical tools• other methods (back testing, benchmarking, …internalisation of ECAI’s)• combination of methods

Qualitative and quantitative• not only a mathematical exercise• must cover structures, procedures, controls, ...

Subject to independent review• validation ≠ audit

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Duration 3 monthsRegulators 4 2 Quantitative Specialists

2 Process SpecialistsReview documentation, interviews ( credit management,

analytics, chief risk officer, business)Report CEO, CCO, Chief Auditor, External Auditors

Point of attention

Outliers detectionCoherency of default definitionDocumentation of methodological choices (test)Central Documentation RepositoryOverruling process

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Agenda

Conclusions

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7. Conclusion

• A permanently strong Credit Risk framework is the aim and key driver, Basel 2 gives the needed leverage

• Model development is not a one shot exercise but a continuous process based on input from users, refined data and new insights

• USE the models and use them respecting the rules

• A lot of work has been done but still a lot needs to be done : we know we can rely on your dedication and professionalism to succeed

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• Management empowerment is crucial for validation

• Model validation not an exact science : a model might assess relative quality of the counterparty but it cannot capture all elements as it is based on portfolio analysis : this means : on average over time

• Expert judgement is of critical importance : for modelling and for communication

• Data issues centre around quantity not quality

• Regional difference in culture and modelling e.g. equity model versus debt model

• Use test of critical importance: do you believe your model for day to day financial decision ?

7. Conclusion

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• Hold on funds in excess of 8 %

• Reinforce arrangements and strategies

• Apply specific provisioning policy

• Restrict/limit business, operations…

• Reduce risk in activities, product, systems

7. Conclusion: not meet requirements of directive?(art. 136)

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Consortium Partners

Under the Auspices of

Local Training Facilitator

credit risk irb approach2

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