Transcript Internal Rating System for Basel II

Building an Internal
Rating System :
Conceptual Framework
Michael Peng
Agenda
1.
Key attributes of an Internal Rating System
2.
Expected Loss Framework
3.
Rating and PDs
4.
Exposure and Facility tracking
5.
Loss Given Default
6. Case Study – Rating Management System
7.
Concluding Comments
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What is an Internal Rating System ?
Credit Rating System consists of all of the methods, processes,
controls and data collection and IT systems that support the
assessment of credit risk, the assignment of internal risk ratings
and the quantification of default and loss estimates.
Internal rating system is the prerequisite for advanced credit
risk management, and each financial institution is expected to
develop its own internal rating system. Every institution faces a
different business environment, so each system should have its
own design. For example, a more simple framework might be
suitable for small institutions . There is no single answer for the
framework of internal rating systems, such as the number of
rating grades, a definition of each rating grade, and the method
of rating assignments. Financial institutions need to introduce
their own system depending on the characteristics of their loan
portfolios, their operations, the objectives of the rating system,
and other factors. Obviously, the institutions need to make
necessary adjustments flexibly due to changes in the business
environment.
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How is IR related to Basel II?
The New Basle Capital Accord – Consultative Document, April 2003
• Appropriate rating system for each asset class
• Multiple methodologies allowed within each asset class (large corporate , SME)
CORPORATE/ BANK/ SOVEREIGN EXPOSURES
•Two dimensional rating system
•Risk of borrower default
•Each borrower must be assigned a rating
•Transaction specific factors (For banks using advanced
approach, facility rating must exclusively reflect LGD)
•Minimum of seven borrower grades for non-defaulted
borrowers and one for those that have defaulted
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RETAIL EXPOSURES
•Each retail exposure must be assigned
to a particular pool
•The pools should provide for meaningful
differentiation of risk, grouping of
sufficiently homogenous exposures and
allow for accurate and consistent
estimation of loss characteristics at pool
level
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Why Building Internal
Rating System (1)?
When banks build their internal rating system, their
objective is twofold.
• First they want to assess the creditworthiness
of companies during the loan application process.
• Second they want to use rating information to
feed their portfolio management tools designed
to produce regulatory capital or economic capital
measures.
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The Use of Internal
Rating System
•
Setting upper credit limits based on rating grades: For example,
institutions can extend a smaller amount of loans to low-graded
borrowers and thereby avoid the risk of credit concentration in
them.
•
Setting authority ranks for loan approval by rating grade: For
example, loan officers at bank branches can make loan decisions
for only a limited amount of loans to low-graded borrowers.
•
Simplifying the loan review process for higher-graded borrowers:
Risk-based allocation of risk management resources can improve
efficiency of the overall loan review process.
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Foundation IRB Vs Advanced IRB Approach
Foundation IRB Approach
Advanced IRB
Approach
Values for Loss given default (LGD) and exposure at
default (EAD) are provided by the regulatory
authority.
Values for Loss given default (LGD) and
exposure at default (EAD) are determined by
each bank through internal modeling with a data
of 5-7 years.
Assessment of values of credit mitigants is done by
the regulatory authority.
Banks may assess the value of its credit
mitigants.
For retail exposure, there is no foundation IRB (only
advanced IRB where besides PD, the bank concerned
will have to estimate LGD & EAD.)
Advanced IRB is applicable to retail exposure
also.
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An Overview of credit risk measurement under
BIS II Framework
Internal Rating System
Qualitative
Evaluation
Quantitative
Evaluation
Internal
Rating
Reporting to the
Board
Financial Data
Loss Given Default
(LGD)
Exposure at Default
(EAD)
Stress Testing
Correlation
Source: BoJ Sep 2005
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Quantification of Credit Risk
Expected Loss (EL)
Unexpected Loss (UL)
Probability of Default (PD)
Calculation of Credit
Risk Amount
Risk Components
Migration Matrix
Portfolio
Monitoring
Provisioning
Pricing
Profit
Management
Capital
Allocation
Internal Use
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A Simple Look on Pillar 1 IRB Tasks
Architecture of an
Internal Rating System,
Estimation of Risk
Components
Quantitative Rating Model
Qualitative Evaluation
Risk estimates (i.e., PD,
LGD, EAD) predictive and
accurate?
Internal Use
“Use Test”*: Pricing,
Portfolio Monitoring,
Credit Risk Quantification?
Validation Work
* Use Test: IRB provision that requires ratings and default and loss estimates to “play an essential
role” in the Institution’s credit approval, risk management, internal capital allocations and corporate
governance functions.
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Source: BoJ Sep
2005
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Overview of a
Rating Management System
Audit Trail
Ratings summary
Facility/ Exposure
details
Collateral and
LGD details
Quantitative inputs
Qualitative inputs
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Internal ratings System (RMS):
User Interface
Bank’s
own
interna
l view
Rating
Templates
Qualitative
assessment
External
Ratings
Quantitative
Assessment
External
Models
Peer
comparison
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1. Key Attributes of an Effective
Internal Rating System
•
Consistent analytical approach to ratings and PDs – all asset classes
•
Transparency of methodology;
•
Visible audit trail;
•
Logical workflow, including sign-off and permissions;
•
Open architecture with a modular approach that is easily adaptable
and scalable;
•
Data access aligned with roles and responsibilities; and
•
Centralised information storage
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2. Expected Loss Framework
 Each prospective or existing loan facility must undergo three
consecutive stages to determine expected loss.
Stage 1
Stage 2
Stage 3
Rating (PD) x
Exposure x
at Default
Loss Given
=
Default
Corporates
Banks
Insurance
Project Finance
SME
Seniority
Maturity etc
Data
Collateral
Haircut Policy
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Expected
Loss
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3. Ratings and Pds
Across different asset classes
Typical Loan Book
Specialised
Finance
Public
Sector
Retail
High volume of data +
Low Exposure
Banks
Insurance
Low volume of data +
High Exposure
RATING TEMPLATES
ARE SUITABLE
Large
Corporates
SMEs
MODELS ARE
SUITABLE
The methodologies used for assessment of creditworthiness of different asset
classes should balance:
•
the volume and scope of data available, with
•
the relative exposure of the bank
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Large corporates and
specialised lending
Characteristics of these sectors
•
Relatively large exposures to individual obligors
•
Qualitative factors can account for more than 50% of the risk of obligors
•
Scarce number of defaulting companies
•
Limited historical track record from many banks in some sectors
Statistical models are NOT applicable in these sectors:
•
Models can severely underestimate the credit risk profile of obligors given the low
proportion of historical defaults in the sectors.
•
Statistical models fail to include and ponder qualitative factors.
•
Models’ results can be highly volatile and with low predictive power.
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Large corporates and specialised lending
Sample template – Insurance Companies
European Bank
Credit factors
Weights
Evaluation
of
Qualitative
Factors
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Large corporates and specialised lending
Sample template – Insurance Companies
Clear and
consistent
rating
criteria
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Large corporates and specialised lending
Sample template – Insurance Companies
European Bank
Evaluation of Quantitative
Factors
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Large corporates and specialised lending
Sample template – Insurance Companies
Quantitative Assessment Based on S&P’s Experience
All Combined
TAC/Total Assets
Pre-Tax Rtn on Assts
Gross Ex/GWP
Growth in gross premium
(%) Premium Income
Gross
(USD
Millions)
Net Inv
Yield
Inv Assets (Bonds+Cash)/TAC
Cash In/Cash Out
Short Term Assets +
Bonds / Total Assets
1
>45%
>8%
<5%
>20%
>900
>10.1%
<2%
>200%
>90%
2
20%-39%
2%-7%
5.1%-17%
10%-20%
500-900
5%-10.1%
2.1%-5%
99%-200%
75%-90%
3
4
5
5%-20%
2%-4%
<2%
(0.2)%-2% (2.1)%-(0.2)% <(2.1)%
17.1%39.0%-45.1% >45.1%
39.0%
1%-10%
(5)%-1%
<(5)%
30-500
30-10
<10
2%-5%
0.5%-2%
<0.5%
5%-8%
8%-12%
>12%
20%-99%
10%-20%
<10%
50%-75%
30%-50%
<30%
Benchmarks are provided per sector and market
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Large corporates and specialised lending
Sample template – Insurance Companies
Backtesting and Mapping to External Indicators of PD
Backtest model results versus S&P ratings or estimates
Compare results and
map the scales
Internal Rating Scale
S&P Scale
1
AAA
1
AA
3
A
2
3
2
1
5
1
BBB
BB
B
CCC
Identify areas of
inconsistency in
order to
improve an
internal ratings
process
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1
2
4
5
6
1
6
1
1
4
2
1
4
Satisfy board
regarding the
validity of an
internal rating
system
S&P
1-yr
PD
1
AAA
2
AA
0.02
3
A
0.02
4
BBB
0.19
5
BB
0.88
6
B
5.44
CCC
0
23.76
• Use of
external
default data
• Prepare for
CBO/CLO
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Rating Assignment Horizon—Relationship
with Business Cycle:
point-in-time vs. through-the-cycle
system
The time horizon of assessing the creditworthiness of
borrowers in assigning ratings is also important. Two
different approaches may be taken in considering the
effect of the business cycle in assigning ratings. One is a
point-in-time point-in-time system (PIT rating). In PIT
rating, risks are evaluated based on the current condition
of a firm regardless of the phase of the business cycle at
the time of evaluation. The other is a through-the-cycle
system (TTC rating). In TTC rating, risks are taken into
account on the assumption that a firm is experiencing the
bottom of the business cycle and is under
stress.
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PIT Rating vs TTC Rating
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4. Exposure and Facility Analysis
Exposure and Facility Analysis - Typically a corporate obligor will have a
number of facilities with a bank, including secured and unsecured loans
and overdraft facilities
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5. LGD and Definition of
default
 The definition of default is not the same in all countries,
often bank behaviour is linked to national legal specificities
US
BASEL II
UK
FRANCE
GERMANY
ITALY
Credit obligation
default
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90 days credit
obligation
default
Debt
restructuring
Bankruptcy
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5. LGD – Loss Given Default
-
LGD Behaviour in the US
 Average Overall Recovery By Industry, some differences
Industries with 9+ Observations
60
50
40
30
20
Food & Beverage
Television
Building Materials
Transportation
Real Estate
Textile & Apparel
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Services & Leasing
0
Gaming & Hotel
10
Retail Food & Drug
Recovery (%)
70
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LGD Behaviour
LGD Behaviour by debt Structure and Industry
subordinated
80%-100%
senior unsecured
60%-80%
senior subordinated
40%-60%
Senior secured
20%-40%
0%-20%
Aut
o/
c..
For
est
/ Bu
Con
il din
g
s um
er /
Ser
Ene
v ic e
rgy
s
/ Na
t. re
Ins u
s ou
...
ran
ce /
rea
l es
tate
tran
s po
rt at
ion
hea
l thc
are
Aer
os
pac
e/
Av e
r
age
Bank
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Overall - No Clear pattern!!
 Need More data
 Clear definitions
 Need to pool data
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Loss Given Default
Loss Given Default: LGD information is scarce and complicated
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Expected Loss
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Concluding Comments
To build an internal rating system for Basel II you need:
1. Consistent rating methodology across asset classes
2. Use an expected loss framework
3. Data to calibrate Pd and LGD inputs
4. Logical and transparent workflow desk-top application
5. Appropriate back-testing and validation.
Standard & Poor’s Risk Solutions
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From Pillar 1 to Pillar 2
Business Processes
From Expected Loss to Economic Capital
 Risk Appetite
 Capital Allocation
 Active Portfolio Mgmt.
 Mitigation Strategies
 Risk Averse Pricing
 RAPM & VaR limits
 EcoCap Optimisation
Portfolio
Approach
 Regulatory Capital
Requirement
 Risk-Adjusted Pricing
 Provisioning Policies
 Limits Based on EL
 Early Warnings
Internal Rating
Approach
 Regulatory Capital
Requirement
 Risk-Adjusted Pricing
 Provisioning Policies
 Limits Based on EL
 Early Warnings
 Regulatory Capital
Requirement
BIS II – IRB
Advanced
BIS II – IRB
Foundation
BIS II – Standard
Approach
Inputs
PD
 Internal Estimate PD Internal Estimate PD  IRB Parameters
 Supervisory LGD  Supervisory LGD
 Internal Estimate LGD  Macroeconomic Forecasts
 Supervisory EAD  Supervisory EAD
 Internal estimate EAD
 External
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Note1
•
•
•
•
: Expected Loss (EL)
Expected Loss is the bank’s cost of doing business. Expected loss has to be provided for.
The Expected Loss (in currency amounts)
EL = PD * EAD * LGD
If expressed as a percentage figure of the EAD
EL = PD * LGD.
The bank should also proactively incorporate an expected loss rate in the estimation
of the total spread to be charged on the loan.
Expected loss is not a measure of risk as it is anticipated.
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Note 2: Unexpected Loss (UL)
•
•
Regardless of how prudent a bank is in managing its day-to-day
business activities, there are market conditions that can cause
uncertainty in the amount of loss in portfolio value.
This uncertainty, or more appropriately the volatility of loss, is the
unexpected loss. Unexpected losses are triggered by the occurrence
of higher default rates as a result of unexpected credit migrations.
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Note 3:EL
Vs UL
Expected V/s Unexpected Losses
8.00%
7.53%
7.00%
Loan losses
6.00%
Unexpected loss
5.00%
4.58%
4.00%
3.52%
3.00%
2.21%
1.96%
2.00%
0.44%
0.00%
1
2
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2.30%
1.41% Expected loss
1.21%
1.00%
3.32%
3.93%
3
0.42%
0.27%
4
5
6
7
Time (Year)
8
9
10
11
12
13
0.56%
14
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