Stress Testing the Impact of Group Dependence on Credit Portfolio Risk
Steven Vanduffel, Boštjan Aver, Andrew Chernih, Luc Henrard and Carmen Ribas
Integrating Stress-Testing Frameworks
Stress Tests, Market Risk Measures and Extremes: Bringing Stress Tests to the Forefront of Market Risk Management
Credit Cycle Stress Testing Using a Point-in-Time Rating System
Stress-Testing Credit Value-at-Risk: a Multiyear Approach
Stress Testing the Impact of Group Dependence on Credit Portfolio Risk
Hedge the Stress: Using Stress Tests to Design Hedges for Foreign Currency Loans
Survey of Retail Loan Portfolio Stress Testing
Stress Tests for Retail Loan Portfolios
Stress-Testing Banks’ Credit Risk Using Mixture Vector Autoregressive Models
Uncertainty, Credit Migration, Stressed Scenarios and Portfolio Losses
Worst-Case and Stressed Correlations in the Asymptotic Single Risk Factor Model
Risk Aggregation, Dependence Structure and Diversification Benefit
Stress-Testing Credit Distributions of Banks’ Portfolios: Risk Structure and Concentration Issues
Time-Varying Correlations for Credit Risk: Modelling, Estimating and Stress Testing
Macro Model-Based Stress Testing of Basel II Capital Requirements
Risk Tolerance Concepts and Scenario Analysis of Bank Capital
Basel II-Type Stress Testing of Credit Portfolios
For the past decade the financial industry has put models in place to assess the default risk of their various credit portfolios and Koyluoglu and Hickman (1998) have classified these as follows
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the “Merton-based” approach; see JP Morgan’s CreditMetrics (Gupton et al 1997) or MKMV’s PortfolioManager (Zeng and Zhang 2001);
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the “Econometric” approach; see McKinsey & Company’s Credit-PortfolioView (Wilson 1997a,b); and
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the “Actuarial” approach; see CreditRisk+ (Credit Suisse Financial Products 1997).
While the mathematical properties of these different approaches are now well understood it remains difficult to prove the accuracy of any of these models, especially when measuring upper tails of the portfolio loss distribution. This is essential because the portfolio will be most severely hit when several credit exposures default together. Unfortunately, since a default is a rare event it is difficult to predict default probabilities and even more so for joint default probabilities. At best financial institutions have a good view on the single and pairwise default probabilities, or equivalently, the default correlations but not on the likelihood that three or more
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