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Credit Risk Modeling using Excel and VBA 2nd edition [Kõva köide]

4.11/5 (18 hinnangut Goodreads-ist)
(University of Ulm), (University of Ulm)
  • Formaat: Hardback, 368 pages, kõrgus x laius x paksus: 249x165x23 mm, kaal: 794 g
  • Sari: The Wiley Finance Series
  • Ilmumisaeg: 17-Dec-2010
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0470660929
  • ISBN-13: 9780470660928
Teised raamatud teemal:
Credit Risk Modeling using Excel and VBA 2nd editionSuurem pilt
  • Formaat: Hardback, 368 pages, kõrgus x laius x paksus: 249x165x23 mm, kaal: 794 g
  • Sari: The Wiley Finance Series
  • Ilmumisaeg: 17-Dec-2010
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 0470660929
  • ISBN-13: 9780470660928
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780470660928.html
Märksõnad:
It is common to blame the inadequacy of credit risk models for the fact that the financial crisis has caught many market participants by surprise. On closer inspection, though, it often appears that market participants failed to understand or to use the models correctly. The recent events therefore do not invalidate traditional credit risk modeling as described in the first edition of the book. A second edition is timely, however, because the first dealt relatively briefly with instruments featuring prominently in the crisis (CDSs and CDOs). In addition to expanding the coverage of these instruments, the book will focus on modeling aspects which were of particular relevance in the financial crisis (e.g. estimation error) and demonstrate the usefulness of credit risk modelling through case studies.

This book provides practitioners and students with an intuitive, hands-on introduction to modern credit risk modelling. Every chapter starts with an explanation of the methodology and then the authors take the reader step by step through the implementation of the methods in Excel and VBA. They focus specifically on risk management issues and cover default probability estimation (scoring, structural models, and transition matrices), correlation and portfolio analysis, validation, as well as credit default swaps and structured finance.

Contents (updates and changes in bold)

1. Estimating Credit Scores with Logit
Additional section on credit scoring in the mortgage market and its role in the development of the subprime crisis

2. The structural approach to Default Prediction and valuation
Additional section showing how to implement a widely used alternative modelling approach (CreditGrades). New case study on the evolution of a banks default risk before default (Lehman)

3. Transition Matrices
Explanation on the transition behaviour of structured finance ratings.

4. Prediction of Default and Transition Rates
Update of the prediction case study to include 2007-2009 and discussion of the performance of such prediction models during the subprime crisis.

5. Modeling and estimating Default correlations with the Asset Value Approach
Explanation of precision of the estimates.

6. Measuring Credit portfolio Risk with the Asset Value Approach
New section on how to deal with low precision in input parameters

7. Validation of Rating Systems
Updated with notes on the recent criticism of rating agencies

8. Validation of Credit Portfolio Models
No update

9. Risk Neutral Default Probabilities and Credit default Swaps
More detailed explanation of CDS and the use of CDS in practice and valuation . Demonstration of hedging and trading strategies based on CDS and bond trading.

10. Risk Analysis of Structured Credit
Extend the chapter to include pricing of CDO's. Expansion of risk analysis through discussion of the ratings of structured finance securities and their role in the financial crisis and the precision and reliability of risk estimates. The analysis of First to Default swaps will be extended to typical Credit Linked Note products.

11. Basel II and Internal ratings
Inclusion of explanation of regulatory deficits revealed by the subprime crisis.

Preface to the 2nd edition xi
Preface to the 1st edition xiii
Some Hints for Troubleshooting xv
1 Estimating Credit Scores with Logit
1(26)
Linking scores, default probabilities and observed default behavior
1(3)
Estimating logit coefficients in Excel
4(4)
Computing statistics after model estimation
8(2)
Interpreting regression statistics
10(2)
Prediction and scenario analysis
12(4)
Treating outliers in input variables
16(4)
Choosing the functional relationship between the score and explanatory variables
20(5)
Concluding remarks
25(1)
Appendix
25(1)
Logit and probit
25(1)
Marginal effects
25(1)
Notes and literature
26(1)
2 The Structural Approach to Default Prediction and Valuation
27(28)
Default and valuation in a structural model
27(3)
Implementing the Merton model with a one-year horizon
30(9)
The iterative approach
30(5)
A solution using equity values and equity volatilities
35(4)
Implementing the Merton model with a T-year horizon
39(4)
Credit spreads
43(1)
CreditGrades
44(6)
Appendix
50(2)
Notes and literature
52(3)
Assumptions
52(1)
Literature
53(2)
3 Transition Matrices
55(28)
Cohort approach
56(5)
Multi-period transitions
61(2)
Hazard rate approach
63(6)
Obtaining a generator matrix from a given transition matrix
69(2)
Confidence intervals with the binomial distribution
71(3)
Bootstrapped confidence intervals for the hazard approach
74(4)
Notes and literature
78(1)
Appendix
78(5)
Matrix functions
78(5)
4 Prediction of Default and Transition Rates
83(32)
Candidate variables for prediction
83(2)
Predicting investment-grade default rates with linear regression
85(3)
Predicting investment-grade default rates with Poisson regression
88(6)
Backtesting the prediction models
94(5)
Predicting transition matrices
99(1)
Adjusting transition matrices
100(1)
Representing transition matrices with a single parameter
101(2)
Shifting the transition matrix
103(5)
Backtesting the transition forecasts
108(1)
Scope of application
108(2)
Notes and literature
110(1)
Appendix
110(5)
5 Prediction of Loss Given Default
115(16)
Candidate variables for prediction
115(4)
Instrument-related variables
116(1)
Firm-specific variables
117(1)
Macroeconomic variables
118(1)
Industry variables
118(1)
Creating a data set
119(1)
Regression analysis of LGD
120(3)
Backtesting predictions
123(3)
Notes and literature
126(1)
Appendix
126(5)
6 Modeling and Estimating Default Correlations with the Asset Value Approach
131(18)
Default correlation, joint default probabilities and the asset value approach
131(2)
Calibrating the asset value approach to default experience: the method of moments
133(3)
Estimating asset correlation with maximum likelihood
136(8)
Exploring the reliability of estimators with a Monte Carlo study
144(3)
Concluding remarks
147(1)
Notes and literature
147(2)
7 Measuring Credit Portfolio Risk with the Asset Value Approach
149(32)
A default-mode model implemented in the spreadsheet
149(3)
VBA implementation of a default-mode model
152(4)
Importance sampling
156(4)
Quasi Monte Carlo
160(2)
Assessing Simulation Error
162(3)
Exploiting portfolio structure in the VBA program
165(3)
Dealing with parameter uncertainty
168(2)
Extensions
170(9)
First extension Multi-factor model
170(1)
Second extension t-distributed asset values
171(2)
Third extension Random LGDs
173(2)
Fourth extension Other risk measures
175(2)
Fifth extension Multi-state modeling
177(2)
Notes and literature
179(2)
8 Validation of Rating Systems
181(22)
Cumulative accuracy profile and accuracy ratios
182(3)
Receiver operating characteristic (ROC)
185(2)
Bootstrapping confidence intervals for the accuracy ratio
187(3)
Interpreting caps and ROCs
190(1)
Brier score
191(1)
Testing the calibration of rating-specific default probabilities
192(3)
Validation strategies
195(3)
Testing for missing information
198(3)
Notes and literature
201(2)
9 Validation of Credit Portfolio Models
203(16)
Testing distributions with the Berkowitz test
203(4)
Example implementation of the Berkowitz test
206(1)
Representing the loss distribution
207(2)
Simulating the critical chi-square value
209(2)
Testing modeling details: Berkowitz on subportfolios
211(3)
Assessing power
214(2)
Scope and limits of the test
216(1)
Notes and literature
217(2)
10 Credit Default Swaps and Risk-Neutral Default Probabilities
219(30)
Describing the term structure of default: PDs cumulative, marginal and seen from today
220(1)
From bond prices to risk-neutral default probabilities
221(11)
Concepts and formulae
221(4)
Implementation
225(7)
Pricing a CDS
232(2)
Refining the PD estimation
234(3)
Market values for a CDS
237(3)
Example
239(1)
Estimating upfront CDS and the `Big Bang' protocol
240(1)
Pricing of a pro-rata basket
241(1)
Forward CDS spreads
242(1)
Example
243(1)
Pricing of swaptions
243(4)
Notes and literature
247(1)
Appendix
247(2)
Deriving the hazard rate for a CDS
247(2)
11 Risk Analysis and Pricing of Structured Credit: CDOs and First-to-Default Swaps
249(36)
Estimating CDO risk with Monte Carlo simulation
249(4)
The large homogeneous portfolio (LHP) approximation
253(3)
Systemic risk of CDO tranches
256(3)
Default times for first-to-default swaps
259(4)
CDO pricing in the LHP framework
263(9)
Simulation-based CDO pricing
272(9)
Notes and literature
281(1)
Appendix
282(3)
Closed-form solution for the LHP model
282(1)
Cholesky decomposition
283(1)
Estimating PD structure from a CDS
284(1)
12 Basel II and Internal Ratings
285(14)
Calculating capital requirements in the Internal Ratings-Based (IRB) approach
285(3)
Assessing a given grading structure
288(6)
Towards an optimal grading structure
294(3)
Notes and literature
297(2)
Appendix A1 Visual Basics for Applications (VBA) 299(8)
Appendix A2 Solver 307(6)
Appendix A3 Maximum Likelihood Estimation and Newton's Method 313(6)
Appendix A4 Testing and Goodness of Fit 319(6)
Appendix A5 User-defined Functions 325(8)
Index 333
About the authors

GUNTER LÖFFLER is Professor of Finance at the University of Ulm in Germany. His current research interests are on credit risk and empirical finance. Previously, Gunter was Assistant Professor at Goethe University Frankfurt, and served as an internal consultant in the asset management division of Commerzbank. His Ph.D. in finance is from the University of Mannheim. Gunter has studied at Heidelberg and Cambridge Universities.

PETER N. POSCH is Assistant Professor of Finance at the University of Ulm in Germany. Previously, Peter was co-head of credit treasury at a large bank, where he also traded credit derivatives and other fixed income products for the bank's proprietary books. His Ph.D. in finance on the dynamics of credit risk is from the University of Ulm. Peter has studied economics, philosophy and law at the University of Bonn.