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E-grāmata: Introduction to Credit Risk Modeling

3.71/5 (13 ratings by Goodreads)
(Munich, Germany), (University of Giessen, Germany), (Munich, Germany)
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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 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.

Recenzijas

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 todays 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 practitioners 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 MATH

Preface to Second Edition ix
Preface xiii
About the Authors xv
List of Figures
xvii
1 The Basics of Credit Risk Management
1(50)
1.1 Expected Loss
2(20)
1.1.1 Probability of Default (PD)
4(11)
1.1.2 The Exposure at Default
15(5)
1.1.3 The Loss Given Default
20(1)
1.1.4 A Remark on the Relation between PD, EAD, LGD
21(1)
1.2 Unexpected Loss
22(23)
1.2.1 Economic Capital
27(2)
1.2.2 The Losws Distribution
29(7)
1.2.3 Modeling Correlations by Means of Factor Modles
36(9)
1.3 Regulatory Capital and the Basel Initiative
45(6)
2 Modeling Correlated Defaults
51(100)
2.1 The Bernoulli Model
53(5)
2.1.1 A General Bernoulli Mixture Model
55(1)
2.1.2 Uniform Default Probability and Uniform Correlation
56(2)
2.2 The Poisson Model
58(4)
2.2.1 A General Poisson Mixture Model
59(1)
2.2.2 Uniform Default Intensity and Uniform Correlation
60(2)
2.3 Bernoulli versus Poisson Mixture
62(1)
2.4 An Overview of Common Model Concepts
63(17)
2.4.1 Moody's KMV's and RiskMetrics' Model Approach
65(3)
2.4.2 MOdel Approach of CreditRisk+
68(3)
2.4.3 Credit Portfolio View
71(7)
2.4.4 Basic Remarks on Dynamic Intensity Models
78(2)
2.5 One Factor/Sector Models
80(19)
2.5.1 One-Factor Models in the Asset Value Model Setup
80(17)
2.5.2 The CreditRisk+ One-Sector Model
97(1)
2.5.3 Comparison of One-Factor and One-Sector Models
98(1)
2.6 Loss Dependence by Means of Copula Functions
99(12)
2.6.1 Copulas: Variations of a Scheme
103(8)
2.7 Working Example on Asset Correlations
111(7)
2.8 Generating the Portfolio Loss Distribution
118(33)
2.8.1 Some Prerequisites from Probability Theory
120(14)
2.8.2 Conditional Independence
134(1)
2.8.3 Technique I: Recursive Generation
134(6)
2.8.4 Technique II: Fourier Transformation
140(2)
2.8.5 Technique III: Saddle-Point Approximation
142(3)
2.8.6 Technique IV: Importance Sampling
145(6)
3 Asset Value Models
151(28)
3.1 Introduction and a Brief Guide to the Literature
151(1)
3.2 A Few Words about Calls and puts
152(10)
3.2.1 Geometric Brownian Motion
154(1)
3.2.2 Put and Call Options
155(7)
3.3 Merton's Asset Value Model
162(7)
3.3.1 Capital Structure: Option- Theoretic Approach
162(5)
3.3.2 Asset from Equity Values
167(2)
3.4 Transforming Equity into Asset Values: A Working Approach
169(10)
3.4.1 Ito's Formula "Light"
170(1)
3.4.2 Black-Scholes Partial Differential Equation
171(8)
4 The Credit Rik+ Model
179(18)
4.1 The Modeling Framewiork of CreditRisk+
180(3)
4.2 Construction Step 1: Independent Obligors
183(1)
4.3 Construction Step 2: Sector Model
184(13)
4.3.1 Sector Default Distribution
186(4)
4.3.2 Sector Compound Distribution
190(3)
4.3.3 Sector Convolution
193(1)
4.3.4 Calculating the Loss Distribution
193(4)
5 Risk Measures and Capital Allocation
197(28)
5.1 Coherent Risk Measures and Expected Shortfall
198(10)
5.1.1 Expected Shortfall
202(2)
5.1.2 Spectral Risk Measures
204(2)
5.1.3 Density of a Risk Measure
206(2)
5.2 Contributorty Capital
208(17)
5.2.1 Axiomatic Approach to Capital Allocation
209(4)
5.2.2 Capital Allocation in Practice
213(2)
5.2.3 Variance/Covariance Approach
215(2)
5.2.4 Capital Allocation w.r.t. Value-at-Risk
217(1)
5.2.5 Capital Allocations w.r.t. Expected Shortfall
218(2)
5.2.6 A Simulation Study
220(5)
6 Term Structure of Default Probability
225(30)
6.1 Survival Function and Hazard Rate
225(3)
6.2 Risk-Neutral vs. Actual Default Probabilities
228(2)
6.3 Term Structure Based on Historical Default Information
230(18)
6.3.1 Exponential Term Structure
230(1)
6.3.2 Direct Calibration of Multi-Year Default Probabilities
231(4)
6.3.3 Migration Technique and Q-Matrices
235(11)
6.3.4 A Non-Homogeneous Markov Chain Approach
246(2)
6.4 Term Structure Based on Market Spreads
248(7)
7 Credit Derivatives
255(26)
7.1 Total Return Swaps
256(2)
7.2 Credit Default Products
258(4)
7.3 Basket Credit Derivatives
262(11)
7.4 Credit Spread Products
273(3)
7.5 Credit-Linked Notes
276(5)
8 Collateralized Debt Obligations
281(64)
8.1 Introduction to Collateralized Debt Obligations
284(14)
8.1.1 Typical Cash Flow CDO Structure
286(10)
8.1.2 Typical Synthetic CLO Structure
296(2)
8.2 Different Roles of Banks in the CDO Market
298(11)
8.2.1 The Originator's Point of View
298(1)
8.2.2 The Investor's Point of View
298(11)
8.3 CDOs from the Modeling Point of View
309(5)
8.4 Multi-Period Credit Models
314(16)
8.4.1 Migration Model
314(5)
8.4.2 Correlated Default Time Models
319(1)
8.4.3 First-Passage-Time Models
320(5)
8.4.4 Stochastic Default Intensity Models
325(1)
8.4.5 Intertemporal Dependence and Autocorrelation
326(4)
8.5 Former Rating Agency Model: Moody's BET
330(8)
8.6 Developments, Model Issues and Further Reading
338(7)
References 345(14)
Index 359
Over the years, Christian Bluhm has worked for Deutsche Bank, McKinsey, HypoVereinsbanks 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.
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