City University of Hong Kong
Professional Seminar on
Latest Perspective on Basel II
Simon Topping
Hong Kong Monetary Authority
19 July 2004
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Outline
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Timetable for implementation in Hong Kong
Range of approaches to be offered
Transitioning from Basel I to Basel II
Qualifying criteria for use of basic & IRB approaches
IRB validation – qualitative & quantitative aspects / use of
benchmarking
• Determining minimum CARs under Basel II
• Changes to HKMA’s supervisory approach
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Timetable for implementation in Hong Kong
• Implementation targeted for end-2006
• Following consultation with LegCo FA Panel, preparing draft
Banking Amendment Bill 2005 featuring “rule-making power”
for HKMA in relation to capital adequacy & financial disclosure
• Consultation on implementation approach, proposals for
implementing IRB, draft Bill to commence shortly
• Basel II Consultation Group established
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Range of approaches to be offered
• Clearly desirable for all AIs to adopt Basel II, but some smaller
AIs have concerns about cost/benefits
• “Default option” of standardised approach for credit risk (+
operational risk, Pillar 2 & Pillar 3)
• AIs meeting qualifying criteria can apply for approval to use
either basic approach or IRB approach
• Therefore for credit risk there will be 4 options: basic approach;
standardised; foundation IRB; & advanced IRB
• While for operational risk there will be 2 options: basic indicator
approach & standardised approach (not AMA)
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Transitioning from Basel I to Basel II
• While the regulatory regime will be “Basel II-ready” by end2006, some AIs, particularly those implementing the more
advanced approaches, will require an extended period to make
the necessary adjustments
• Therefore proposing a 3-year implementation period from end2006 to end-2009 for IRB
• During this period “transitional arrangements” will apply
• Generally speaking, all AIs will adopt standardised approach at
end-2006 unless they opt for basic approach or indicate their
intention to adopt IRB
• Pillars 2 & 3 will apply to all AIs from end-2006
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Qualifying criteria for basic approach
• Comprises “Basel I” treatment of credit & market risks plus
operational risk plus Pillars 2 & 3
• Will be available to all AIs (primarily RLBs & DTCs, also some
smaller banks) which are small (total assets less than HK$10bn)
& whose business is simple
• Not available for subsidiaries of larger banks
• Also available as an interim measure for AIs planning to adopt
IRB within the transitional period
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Qualifying criteria for IRB
• Available to all AIs that can meet rigorous qualitative &
quantitative qualifying criteria
• AIs’ rating systems need to rank order & quantify risk in a
consistent, reliable & valid manner
• Must provide for a meaningful differentiation of borrower &
transaction characteristics, a meaningful differentiation of credit
risk, & reasonably accurate & consistent quantitative estimates of
risk
• Must have been in “use” for 2 years; minimum of 2 years of data
(within transitional period)
• Must cover all material exposures (phased rollout allowable, but
IRB coverage must reach a target level before transition to IRB
allowed)
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IRB validation (qualitative aspect)
Qualitative Aspect
Scope
 Coverage of asset classes
 Appropriate rating system design for AI’s exposures
 Credible rating operations and process
 Adequate corporate governance and audit
 Adequate use of the rating system
HKMA’s validation methodologies
 Questionnaire for AI’s self-assessment
 Checklist for on-site examination
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IRB validation (quantitative aspect)
Quantitative Aspect
I. Data quality
• Data maintenance
• Use of external
data
- sample data
checking
- data storage
process
II. AI’s internal stress
tests used in
assessment of
capital adequacy
• Benchmarking
against HKMA’s
internal stress-testing
parameters
III. AI’s internal
validation of
PD/LGD estimates
& internal statistical
tests on
discriminative
power of its credit
scoring models
IV. HKMA’s
validation
methodologies
for PD/LGD
estimates
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IRB validation process
A. HKMA’s benchmarking models for identifying
underestimated PD/LGD:
IV.
HKMA’s
validation
methodologies for
PD/LGD
estimates
• Listed companies (empirical testing a PD term-structure model)
• Private companies including SMEs (model based on financial
statements)
• Retail exposures:
 RML (empirical testing a model based on expected-loss measures)
 Credit cards, small SMEs, personal loans (scoring systems)
• Bank and sovereign exposures based on their external credit ratings
• Standard VaR validation for equities
B. Benchmarking among AIs
• Comparing PD/LGD of same/similar exposures to identify “outlier” with
“underestimated” PD/LGD measures
• Results depend on individual AIs’ rating approaches
C. Back-testing
• Statistical tests (e.g. Gini coefficient)
• A sufficiently long period of actual default history is necessary for
meaningful tests
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Validating PD estimates (corporates)
• Among IRB AIs, we could compare PD of same/similar exposures
to identify “outlier” with “underestimated” PD measures
• However, it is possible that the use of benchmarking among AIs
may be constrained by the widespread adoption of the same
vendors, e.g. Moody’s KMV
• AIs would not have enough actual default data for meaningful
back-testing during the initial period
• We therefore need to develop some benchmarking models which
can be used to identify “underestimated” PD measures (N.B. not
developing “super” credit risk models)
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Benchmarking of PD estimation (listed company)
Input market
parameter:
Listed company’s
leverage ratio & its
volatility
Model Engine
Generate PD
term structure of
company
Mapping with S&P’s
default rates
Map model PD term
structure of company
to S&P’s default-rate
term structures of
different ratings (static
pools cumulative average
default rates)
Empirical tests
PD term-structure model based on
133 listed companies with credit
ratings (BBB+ & below) in US with
1,337 data samples at different time
Assigning
model “S&P’s”
rating
Based on
mapping result,
a rating is
assigned to the
company
Implied 1-year
benchmarking
PD of company
Based on actual
1-year average
default rate of
assigned rating
Compare 1-year benchmark PD with AI’s 1year PD of company based on its IRB
system. Based on comparisons for a number
of companies, the results will indicate any
inconsistencies / systematic underestimation
in the AI’s PD estimates.
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Determining minimum CARs under Basel II
• Charge for credit risk under standardised approach is likely on
average to be slightly lower than under Basel I
• However, this will be more than offset by the charge for operational
risk
• Charge for credit risk under IRB less certain, but unlikely to be
significantly lower
• Under Pillar 2, AIs will assess their target overall level of CAR by
means of a capital adequacy assessment programme (CAAP); this
will be subject to supervisory review
• Possible that less “buffer” or “cushion” above the regulatory CAR
will be maintained – so CARs may fall over time
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Changes to HKMA’s supervisory approach
• For AIs on standardised approach, capital adequacy requirements
o/a credit risk will continue to be set by the regulator
• For AIs on IRB approach, however, cap ad requirements o/a credit
risk will be more internally set (N.B. closer to economic capital)
• For all AIs, setting the target CAR (under Pillar 2) will in the first
instance be the responsibility of the AI itself, rather than the
regulator – although the regulator will conduct its own assessment,
at least initially (N.B. a development of the risk-based approach)
• Also for all AIs, market discipline (through Pillar 3 disclosures) will
play an increasing role
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