Stress Testing for Financial Institutions
First published:
ISBN: 9781906348113
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Survey of Retail Loan Portfolio Stress Testing
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
The concept of stress testing is straightforward. What is the expected portfolio performance given the current portfolio and a specific (usually severe) economic scenario? With Basel II there is an additional assumption that we are looking at total default losses over the next 12 months, but the modelling problem remains essentially unchanged.
Successful stress test models do not require that the analysis be done either on segment vintages or individual accounts. That decision is driven by the modelling framework chosen. Because a portfolio can be comprised of many disparate products and customer groups, we usually assume that better results will be obtained by segmenting the portfolio at least along product lines, assessing the shift in losses under economic stress for each segment, and then adding the individual loss estimates. Beyond this initial high-level segmentation, any further disaggregation of data is a modelling decision and effective models exist at multiple levels of data aggregation.
For a collection of accounts within a segment, the important dynamic is that of a shifting distribution across the probability of default (PD), see Figure 7.1. The models we describe
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