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Introduction to Credit Risk Modeling, Second Edition (Chapman & Hall/CRC Financial Mathematics, nr. 19)

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Notă GoodReads:
en Limba Engleză Hardback – June 2010
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 distributions
  • Introductory sections on new topics, such as spectral risk measures, an axiomatic approach to capital allocation, and nonhomogeneous Markov chains
  • Updated sections on the probability of default, exposure-at-default, loss-given-default, and regulatory capital
  • A new section on multi-period models
  • Recent 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 angles.
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Specificații

ISBN-13: 9781584889922
ISBN-10: 1584889926
Pagini: 386
Ilustrații: 50 black & white illustrations, 18 black & white tables
Dimensiuni: 160 x 231 x 23 mm
Greutate: 0.77 kg
Ediția: Revizuită
Editura: CRC Press
Seria Chapman & Hall/CRC Financial Mathematics


Public țintă

Advanced undergraduate and graduate students and researchers in quantitative finance and finance; practitioners in quantitative finance and investment.

Cuprins

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 Functions
Working 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 Models
Former Rating Agency Model: Moody’s BET
Developments, Model Issues, and Further Reading
References
Index

Recenzii

… 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 Edition
This 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 MATH

Notă biografică

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-Nürnberg.
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 Munich.