Introduction to Credit Risk Modeling, Second Edition

Introduction to Credit Risk Modeling, Second Edition

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Contains Nearly 100 Pages of New Material The recent financial crisis has shown that credit risk in particular and finance in general remain important fields for the application of mathematical concepts to real-life situations. While continuing to focus on common mathematical approaches to model credit portfolios, Introduction to Credit Risk Modeling, Second Edition presents updates on model developments that have occurred since the publication of the best-selling first edition. New to the Second Edition An expanded section on techniques for the generation of loss distributionsIntroductory sections on new topics, such as spectral risk measures, an axiomatic approach to capital allocation, and nonhomogeneous Markov chainsUpdated sections on the probability of default, exposure-at-default, loss-given-default, and regulatory capital A new section on multi-period modelsRecent developments in structured credit The financial crisis illustrated the importance of effectively communicating model outcomes and ensuring that the variation in results is clearly understood by decision makers. The crisis also showed that more modeling and more analysis are superior to only one model. This accessible, self-contained book recommends using a variety of models to shed light on different aspects of the true nature of a credit risk problem, thereby allowing the problem to be viewed from different more

Product details

  • Electronic book text | 384 pages
  • Taylor & Francis Ltd
  • Chapman & Hall/CRC
  • London, United Kingdom
  • New edition
  • 2nd New edition
  • 18 Tables, black and white; 50 Illustrations, black and white
  • 1584889934
  • 9781584889939

Table of contents

The Basics of Credit Risk Management Expected Loss Unexpected Loss Regulatory Capital and the Basel Initiative Modeling Correlated Defaults The Bernoulli Model The Poisson Model Bernoulli versus Poisson Mixture An Overview of Common Model Concepts One-Factor/Sector Models Loss Dependence by Means of Copula FunctionsWorking Example on Asset Correlations Generating the Portfolio Loss Distribution Asset Value Models Introduction and a Brief Guide to the Literature A Few Words about Calls and Puts Merton's Asset Value Model Transforming Equity into Asset Values: A Working Approach The CreditRisk+ Model The Modeling Framework of CreditRisk+ Construction Step 1: Independent Obligors Construction Step 2: Sector Model Risk Measures and Capital Allocation Coherent Risk Measures and Expected Shortfall Contributory Capital Term Structure of Default Probability Survival Function and Hazard Rate Risk-Neutral vs. Actual Default Probabilities Term Structure Based on Historical Default Information Term Structure Based on Market Spreads Credit Derivatives Total Return Swaps Credit Default Products Basket Credit Derivatives Credit Spread Products Credit-Linked Notes Collateralized Debt Obligations Introduction to Collateralized Debt Obligations (CDOs)Different Roles of Banks in the CDO Market CDOs from the Modeling Point of View Multi-Period Credit ModelsFormer Rating Agency Model: Moody's BET Developments, Model Issues, and Further Reading References Indexshow more

Review quote

... this is a concise book for exploring the limitations of credit risk models and, to a lesser degree, asset valuation models. Read this book for a companionable journey through some of the limiting assumptions that make the models tractable. ... it may be the first one [book] that wastes no time in getting to the point, and moving on.-Annals of Actuarial Science, Vol. 5, June 2011 Bluhm, Overbeck, and Wagner offer help to mathematicians and physicists leaving the academy to work as risk or portfolio managers. For this introduction, they focus on main themes rather than details, and on portfolio rather than single obligor risk. ... this second [edition] takes account of problems in the banking industry [from] 2007-09.-SciTech Book News, February 2011 Having a valid and up-to-date credit risk model (or models) is one of the most important aspects in today's risk management. The models require quite a bit of technical as well as practical know-how. Introduction to Credit Risk Modeling serves this purpose well. ... it would best fit the practitioner's needs. For students it can also be of great use, as an introductory course for credit risk models. A great first step into credit risk modeling. ... The book provides a nice coherent overview of the methods used in capital allocation. ... The book is written in a mixture of theorem-proof and applied styles. ... I find this rather pleasing, as it gives the reader the edge of theoretical exposition, which is extremely important. ... One really useful side of the book is that it provides step-by-step guide to methods presented. This should be really appreciated in industry and among students. ...-MAA Reviews, January 2011 Praise for the First EditionThis is an outstanding book on the default models that are used internally by financial institutions. This practical book delves into the mathematics, the assumptions and the approximations that practitioners apply to make these models work.-Glyn A. Holton, Contingency Analysis There are so many financial tools available today and numbers are likely to grow in the future. If you work in this field of credit risk modelling it is worth looking at the theoretical background, and this book is a well-rounded introduction.-Journal of the Operational Research Society As an introductory survey it does an admirable job. ... this book is an important guide into the field of credit risk models. Mainly for the practitioner ... It is well written, fairly easy to follow.-Horst Behncke, Zentralblatt MATHshow more

About Christian Bluhm

Over the years, Christian Bluhm has worked for Deutsche Bank, McKinsey, HypoVereinsbank's Group Credit Portfolio Management, and Credit Suisse. He earned a Ph.D. in mathematics from the University of Erlangen-Nurnberg. Ludger Overbeck is a professor of probability theory and quantitative finance and risk management in the Institute of Mathematics at the University of Giessen. During his career, he worked for Deutsche Bundesbank, Deutsche Bank, HypoVereinsbank/UniCredit, DZBank, and Commerzbank. He earned a Ph.D. in mathematics from the University of Bonn. Christoph Wagner has worked for Deutsche Bank, Allianz Group Center, UniCredit/HypoVereinsbank, and Allianz Risk Transfer. He earned a Ph.D. in statistical physics from the Technical University of more