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Challenges in Validation: Taking the Study Findings Forward A Corporate Perspective. Advanced IRB Forum New York, June 19, 2003. Lyn McGowan RBC Financial Group. The Challenge of Validation for Corporate and Mid-Market Portfolios.

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challenges in validation taking the study findings forward a corporate perspective

Challenges in Validation: Taking the Study Findings ForwardA Corporate Perspective

Advanced IRB Forum

New York, June 19, 2003

Lyn McGowan

RBC Financial Group

the challenge of validation for corporate and mid market portfolios
The Challenge of Validation for Corporate and Mid-Market Portfolios
  • Internal rating validation approaches, methods, issues vary, depending on the types of rating models used
  • Rating system design and validation go hand in hand

Type of Rating ModelCORPORATEMID-MARKET

Statistical Models 7 4

External Vendor Models 7 2

Expert Judgement Models 15 11

Hybrid Models 10 7

the data challenge
The Data Challenge
  • Insufficient data to rely on purely statistical means of validation  must rely on other means
  • The Basel Research Task Force recognizes that quantitative statistical techniques should be performed, however should not drive the pass/fail decision for IRB validation
  • Supervisor will need to understand and be satisfied with:
      • The logic of the risk assessment process
      • The rating system’s design and operation
      • How the rating system has been calibrated
      • The internal validation process
      • The “feedback loop”
logic of the risk assessment process
Logic of the Risk Assessment Process
  • Conceptual clarity  Well-defined drivers/factors

 Dynamic properties,

significance of factors

  • Transparency Explicitly demonstrates

reasoning

 Constraints (such as

stipulated factor weightings)

 Assessment horizon

  • Replicability  “Gut feel” won’t do

 Criteria or thresholds for

factors

  • Well-documented  Process/procedures manual
rating system design and operation
Rating System Design and Operation
  • Conceptual clarity  Understandable output
  • Transparency  Not a Black Box
  • Replicability  Well-defined framework

and/or methodology

  • Consistency  Application across industry,

geography

  • Documentation  Rationale for design

 Conceptual meaning,

definition of grades

 Frequency of review

 Override authority, reporting

calibration of the rating system
Calibration of the Rating System
  • Conceptual clarity  Techniques have been

combined rationally

  • Transparency  Availability of data

across quality spectrum

 Method of mitigating

scarcity of data

 Basis for numerator and

denominator

  • Consistency  Potential sources of bias

 Relevance of external data

  • Documentation  Specific techniques used
internal validation process
Internal Validation Process
  • Conceptual clarity  Discriminative power vs accuracy of calibration

 Factor relevance vs factor

weights vs model strength

 Rationale for Triangulation

  • Transparency  Scope / frequency of work

 Mapping processes  Objective metrics

  • Consistency  Actual vs predicted

 Own to external loss

experience

 Role of loan review unit

  • Documentation  Clear, comprehensive, precise
the critical feedback loop
The Critical Feedback Loop

Calibration

Continuous Improvement Cycle

Validation