Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
This book offers state-of-the-art tools and techniques for controlling credit risk exposure of all types, in every environment. oldest risk in world financial markets - credit risk - has become a leading source of problems and confusion, not just for bankers and investors but for all finance professionals.The Standard & Poor's Guide to Measuring and Managing Credit will help you understand every aspect of credit risk, and provide you with today's most up-to-date techniques and models for identifying, measuring, monitoring, and controlling your organization's credit risk exposure. 'de Servigny and Renault have written a valuable reference book on the analytics of credit markets. Theory and data are integrated seamlessly throughout the manuscript. mathematical treatment is complete, though not overbearing. economics, pricing, structuring and capital allocation aspects are artfully combined into a coherent whole' - Jamil Baz, Global Head of Fixed Income Research, Deutsche Bank.' This is much more than just a 'how to' book - it is analytically complete in that it looks at the microeconomics of industry structure to understand why credit risks have to be measured and monitored as well as being comprehensive in covering all the different approaches used to monitor and measure credit risk' - Bunt Ghosh, Global Head of Fixed Income Research, Credit Suisse First Boston. 'This extensive work, really clear while dealing with sophisticated methodologies, is right in the heart of today's concerns' - Jean-Pierre Mustier, CEO, SG Corporate and Investment Banking. de Servigny and Renault provide a comprehensive treatment of all aspects of modern credit risk measurement, management, and mitigation, not only for large corporations but also for retail and small business (with an excellent chapter on credit scoring).This book is an absolute must for both academics and risk professionals, especially those struggling with the implementation of Basel II' - Michel Crouhy, Head of Business Analytic Solutions, Canadian Imperial Bank of Commerce. Fast-changing regulations, transformative technologies, and today's go-for-broke business mentality present investment banks and other lenders with default problems that are both unprecedented and daunting. To keep pace with this change, finance professionals are finding they must continually review and upgrade their credit risk management tools and techniques. The Standard & Poor's Guide to Measuring and Managing Credit 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 discerning 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; and, 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. The Standard & Poor's Guide to Measuring and Managing Credit 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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle