3.6 CONCEPTUAL APPROACHES FOR MODELING CREDIT RISK
3.6.1 Transaction and Portfolio Management Transaction and portfolio management serve complementary objectives
• Transaction managementpursues value creation.
• Portfolio managementpursues value preservation.
Transaction management is based on individual transaction opti- mization and added value through the use of appropriate risk and pricing models, methods for structuring loan instruments, and the like for individual positions. Relationships to other segments and markets are not included in this view. The portfolio management approach considers all factors (such as correlations, volatilities, etc.) for a port- folio in order to optimize and preserve the existing risk-return level in a portfolio.
Depending on the credit risk management setup, different activities (such as securitization, credit derivatives, syndicated loans, etc.) are possi- ble with the given infrastructure and management know-how. Figure 3-2 gives an overview of the impact of the loan structure on credit risk man- agement decisions.
Depending on the credit risk management type, the focus is on dif- ferent objectives. At the transaction level, the focus is on:
• Measuring credit risk using risk ratings and default and loss probability calculations
• Developing and using migration models
• Integrating risk ratings into the credit process.
The key input elements are risk ratings.Risk-rating methodology is developed with three objectives in mind:
• Measure credit risk (default and loss probability or potential) using good discrimination, separation, and accuracy across the full spectrum of credit risk.
• Provide management with accurate and meaningful information for decision making.
• Produce supportable and accurate information for regulatory and financial statement reporting purposes.
3.6.1.1 Primary Approaches to Risk Rating Table 3-1 gives an overview of the primary approaches.
3.6.1.2 Migration Models
Migration models can help to calibrate the risk rating and the predicted losses by default frequency and amount of loss. The objectives are as follows:
• Adjust the internal rating process to external rating systems.
• Support provisioning requirements.
• Determine the risk cost by rating.
• Support risk-based pricing.
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"Front end"
"Back end"
Transaction management Portfolio management
Credit structure and embedded optionalities impact is significant on approach to credit risk management Credit
portfolio
Credit derivatives
Securitization (CMO, ABS,
etc.) Syndicate
hold / sell Underwrite /
originate new loans
F I G U R E 3-2
Impact of Loan Structure on Credit Risk Management Approach.
Judgmental approach/ Relationship managers Subjective; ratings affected In-place common, Most bank systems expert systems and/or credit officers by fads or politics; historical track record
assign ratings based on vulnerable to relativity may be available; allows financial ratios, opinions and anchoring; no for nonquantitative factors;
on management quality, numerical estimates of promotes identification of and other data collected risk term structures risk factors.
in due diligence review; except by reference to expert system codifies past experience.
rules of successful loan officers.
Discriminant analysis Classifies companies or Oriented to the history; Simple to apply to all Altman’s Z score facilities into several often calibrated over an borrowers, allows testing
categories based on arbitrary period; usually no of many variables and financial ratios measuring term structure; prone to functional forms.
leverage, debt-service overfitting.
coverage, volatility; uses historical default data to calibrate model.
Contingent claims Capital owners hold a Oriented to the future; Hard to apply and KMV’s CreditMonitor (options model) default option, which parsimonious; determines probably less reliable for
they exercise when default term structure; private firms; unbundling advantageous; associated measures sensitivity to of volatility of structured with migration model; changing business and securitized products predicts cumulative default conditions. to identify risk factors.
probabilities as a function of market leverage, term, and volatility.
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The average loss rate modelmeasures transactional lossesby risk rating.
The cumulative loss over several periods is calculated on the basis of indi- vidual positions allocated to credit development curves, which are based on average loss rates based on historical experience. Figure 3-3 shows the development curves for two positions.
Applying a Markov approach, the model measures rating transitions through various “credit” stages(see Figure 3-4). The true Markov process probabilities are based on the following assumptions:
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1 2 3 4
Doubtful
Time horizon
Cumulative losses, % Substandard
1 Period average loss rate 4 Period average loss rate
3 Period average loss rate
2 Period average loss rate
45%
10%
F I G U R E 3-3
Average Loss Rate Approach for Measuring Rating Transitions.
Grade 1
Grade 3 Grade 2 Grade 1
Default
Loss %
Grade 3 Grade 2 Grade 1
Default
Loss %
Transitional state
Absorbing state F I G U R E 3-4
Markov Process for Measuring Rating Transitions.
• The model is independent of its prior grade history.
• The model is constant over time.
• The probabilities are the same for all credits in a given category regardless of the specific credit characteristics.
• The probabilities are independent of the movements of other credits.
The results from the Markov process have to be mapped against standard ratings (e.g., from S&P) to review and calibrate the internal process to external rating information (see Figure 3-5).
The EDF is an essential input for many analytical methodologies and models, including risk-based pricing, RAROC, and quantitative models (e.g., CreditMetrics). EDFs also allow financial institutions to test the align- ment of their own grading systems across different business units and against third-party data. (See comments on EDF in Section 3.7.3.2.)
3.6.2 Measuring Transaction