Credit Cycle Stress Testing Using a Point-in-Time Rating System
Sean Keenan, David Li, Stefano Santilli, Andrew Barnes, Kete Chalermkraivuth and Radu Neagu
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
Managers and supervisors of financial institutions recognise two different approaches to internal credit rating systems: through-the-cycle (TTC) and point-in-time (PIT). In simple terms, a PIT system is one in which rating grades are associated with fixed probabilities of default (PD) that should obtain, approximately, in every period. Credit cycles should not affect the observed default rate’s rating grades. Changes in the credit quality of obligors should be captured entirely through rating changes, not through variances in observed default rates by grade. Through-the-cycle systems seek to assign ratings that are stable over changing credit cycle conditions, preserving the relative rank ordering of risk across obligors and allowing the absolute level of default risk to vary (see Lewis 2004). While many articles debate the relative merits of these approaches (see, for example, Heitfield (2004) or Gordy and Howells (2004)) there is little doubt that a commitment to a PIT system presents institutions with some specific challenges and benefits.
One significant challenge stems from the fact that PIT PD models are voracious consumers of timely data. Where daily stock prices are
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