Measuring and Managing Credit Risk
Arnaud De Servigny, Olivier Renault
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Today's most complete, up-to-date reference for controlling credit risk exposure of all types, in every environment
Measuring and Managing Credit Risk takes you far beyond the Basel guidelines to detail a powerful, proven program for understanding and controlling your firm's credit risk.
Providing hands-on answers on practical topics from capital management to correlations, and supporting its theories with up-to-the-minute data and insights, this authoritative book examines every key aspect of credit risk, including:
• Determinants of credit risk and pricing/spread implications
• Quantitative models for moving beyond Altman's Z score to separate "good" borrowers from "bad"
• Key determinants of loss given default, and potential links between recovery rates and probabilities of default
• Measures of dependency including linear correlation, and the impact of correlation on portfolio losses
• A detailed review of five of today's most popular portfolio models—CreditMetrics, CreditPortfolioView, Portfolio Risk Tracker, CreditRisk+, and Portfolio Manager
• How credit risk is reflected in the prices and yields of individual securities
• How derivatives and securitization instruments can be used to transfer and repackage credit risk
Today's credit risk measurement and management tools and techniques provide organizations with dramatically improved strength and flexibility, not only in mitigating risk but also in improving overall financial performance. Measuring and Managing Credit Risk introduces and explores each of these tools, along with the rapidly evolving global credit environment, to provide bankers and other financial decision-makers with the know-how to avoid excessive credit risk where possible—and mitigate it when necessary.
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minimum for investment-grade securities, while 5 years should be enough to back-test non-investment-grade issues. Figure 2-9 is calculated from a sample of defaulted firms and reports the average time it took for firms in a given rating grade to drift down to default.21 In many banks we are still far from ex post statistical testing, because the rating history is in general too short.22 In the future many banks will probably discover that their internal rating system is weaker than they expected.
to incorporate early warning predictive power. The task related to internal ratings assigned to banks in the Basel II accord is very challenging. Banks have to rate a very large universe corresponding to most of the asset classes they are dealing with. For most banks it is a new task that they have to perform. They suffer from a lack of data history, and it will take years before they have sufficient results to back-test their methodologies. Many are at the stage of choosing their approaches for
prebankruptcy negotiation around 70 percent. l94 percent in 1994 according to Kaiser (1996). mBlazy and Combier (1997). nSaint-Alary (1990). aFranks and Sussman (2002). bBlazy and Combier (1997) cWith the new code, imminent insolvency can also trigger bankruptcy. See Elsas and Krahnen (2002). dAdministration for large companies. e Company voluntary arrangement. f Very rarely used in practice. gAround 80 percent of firms move directly to liquidation. hKeenan, Hamilton, and Berthault (2000).
11:15 PM Page 173 Default Dependencies FIGURE 173 5-4 Optimal Allocation versus Correlation Proportion invested in X (w* ) 60% 40% 20% 0% –20% –40% –60% –80% –100% 68% 80% 92% 68% 80% 92% 56% 44% 32% 20% 8% –4% –16% –28% –40% –52% –64% –76% –88% –100% –120% Correlation between X and Y FIGURE 5-5 Minimum Portfolio Variance versus Correlation 2.0% 1.5% 1.0% 0.5% Correlation between X and Y 56% 44% 32% 20% 8% –4% –16% –28% –40% –52% –64% –76% –88%