Rent 9780470080184 | Active Credit Portfolio Management in Practice | Jeffrey R. Bohn; Roger M. Stein @knetbooks.com

Summary Table of Contents Author Biography

State-of-the-art techniques and tools needed to facilitate effective credit portfolio management and robust quantitative credit analysisFilled with in-depth insights and expert advice, Active Credit Portfolio Management in Practice serves as a comprehensive introduction to both the theory and real-world practice of credit portfolio management. The authors have written a text that is technical enough both in terms of background and implementation to cover what practitioners and researchers need for actually applying these types of risk managemen... MORE

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Foreword	p. xi
Preface	p. xiii
Acknowledgments	p. xxvii
The Framework: Definitions and Concepts	p. 1
What Is Credit?	p. 2
Evolution of Credit Markets	p. 7
Defining Risk	p. 11
A Word about Regulation	p. 13
What Are Credit Models Good For?	p. 14
Active Credit Portfolio Management (ACPM)	p. 16
Framework at 30,000 Feet	p. 19
Building Blocks of Portfolio Risk	p. 23
Using PDs in Practice	p. 32
Value, Price, and Spread	p. 34
Defining Default	p. 38
Portfolio Performance Metrics	p. 38
Data and Data Systems	p. 42
Review Questions	p. 43
ACPM in Practice	p. 45
Bank Valuation	p. 50
Organizing Financial Institutions: Dividing into Two Business Lines	p. 52
Emphasis on Credit Risk	p. 57
Market Trends Supporting ACPM	p. 59
Financial Instruments Used for Hedging and Managing Risk in a Credit Portfolio	p. 60
Mark-to-Market and Transfer Pricing	p. 63
Metrics for Managing a Credit Portfolio	p. 68
Data and Models	p. 72
Evaluating an ACPM Unit	p. 75
Managing a Research Team	p. 77
Conclusion	p. 86
Review Questions	p. 87
Exercises	p. 87
Structural Models	p. 89
Structural Models in Context	p. 91
A Basic Structural Model	p. 95
Black-Scholes-Merton	p. 100
Valuation	p. 107
Modifying BSM	p. 117
First Passage Time: Black-Cox	p. 118
Practical Implementation: Vasicek-Kealhofer	p. 124
Stochastic Interest Rates: Longstaff-Schwartz	p. 145
Jump-Diffusion Models: Zhou	p. 150
Endogenous Default Barrier (Taxes and Bankruptcy Costs): Leland-Toft	p. 151
Corporate Transaction Analysis	p. 156
Liquidity	p. 159
Other Structural Approaches	p. 161
Conclusion	p. 171
Derivation of Black-Scholes-Merton Framework for Calculating Distance to Default (DD)	p. 171
Derivation of Conversion of Physical Probability of Default (PD) to a Risk-Neutral Probability of Default (PDQ)	p. 177
Review Questions	p. 179
Exercises	p. 179
Econometric Models	p. 183
Discrete-Choice Models	p. 186
Early Discrete-Choice Models: Beaver (1966) and Altman (1968)	p. 191
Hazard Rate (Duration) Models	p. 196
Example of a Hazard-Rate Framework for Predicting Default: Shumway (2001)	p. 204
Hazard Rates versus Discrete Choice	p. 206
Practical Applications: Falkenstein et al. (2000) and Dwyer and Stein (2004)	p. 207
Calibrating Econometric Models	p. 215
Calibrating to PDs	p. 216
Calibrating to Ratings	p. 227
Interpreting the Relative Influence of Factors in Econometric Models	p. 234
Data Issues	p. 238
Taxonomy of Data Woes	p. 241
Biased Samples Cannot Easily Be Fixed	p. 244
Conclusion	p. 249
Some Alternative Default Model Specifications	p. 249
Review Questions	p. 252
Exercises	p. 252
Loss Given Default	p. 255
Road to Recovery: The Timeline of Default Resolution	p. 258
Measures of LGD (Recovery)	p. 260
The Relationship between Market Prices and Ultimate Recovery	p. 265
Approaches to Modeling LGD: The LossCalc (2002, 2005) Approaches and Extensions	p. 273
Conclusion	p. 285
Review Questions	p. 286
Exercises	p. 286
Reduced-Form Models	p. 289
Reduced-Form Models in Context	p. 291
Basic Intensity Models	p. 296
A Brief Interlude to Discuss Valuation	p. 310
Duffie, Singleton, Lando (DSL) Intensity Model	p. 312
Credit Rating Transition Models	p. 329
Default Probability Density Version of Intensity Models (Hull-White)	p. 340
Generic Credit Curves	p. 348
Conclusion	p. 353
Kalman Filter	p. 354
Sample Transition Matrices	p. 357
Review Questions	p. 358
Exercises	p. 358
PD Model Validation	p. 361
The Basics: Parameter Robustness	p. 367
Measures of Model Power	p. 371
Measures of PD Levels and Calibration	p. 379
Sample Size and Confidence Bounds	p. 396
Assessing the Economic Value of More Powerful PD Models	p. 418
Avoiding Overfitting: A Walk-Forward Approach to Model Testing	p. 431
Conclusion	p. 437
Type I and Type II Error: Converting CAP Plots into Contingency Tables	p. 438
The Likelihood for the General Case of a Default Model	p. 440
Tables of ROC ϵ and nmax	p. 441
Proof of the Relationship between NPV Terms and ROC Terms	p. 441
Derivation of Minimum Sample Size Required to Test for Default Rate Accuracy in Uncorrelated Case	p. 446
Tables for Lower Bounds of ϵ and N on Probabilities of Default	p. 447
Review Questions	p. 452
Exercises	p. 452
Portfolio Models	p. 455
A Structural Model of Default Risk	p. 460
Measurement of Portfolio Diversification	p. 460
Portfolio Risk Assuming No Credit Migration	p. 461
Structural Models of Default Correlation	p. 465
Credit Migration	p. 470
A Model of Value Correlation	p. 475
Probability of Large Losses	p. 481
Valuation	p. 484
Return Calculations	p. 488
Risk Calculations	p. 491
Portfolio Loss Distribution	p. 498
Capital	p. 514
Economic Capital and Portfolio Management	p. 519
Improving Portfolio Performance	p. 521
Performance Metrics	p. 526
Reduced-Form Models and Portfolio Modeling	p. 530
Correlation in Intensity Models	p. 531
Copulas	p. 534
Frailty	p. 536
Integrating Market and Credit Risk	p. 541
Counterparty Risk in Credit Default Swaps (CDS) and Credit Portfolios	p. 544
Conclusion	p. 546
Review Questions	p. 547
Exercises	p. 548
Building a Better Bank: A Case Study	p. 551
Description	p. 552
Current Organization	p. 554
Transforming the Capital Allocation Process	p. 556
Portfolio Analysis	p. 558
Active Credit Portfolio Management (ACPM)	p. 562
Data, Systems, and Metrics	p. 563
ACPM and Transforming the Bank	p. 566
Appendix: Figures	p. 569
Exercises	p. 574
References	p. 575
About the Authors	p. 589
Index	p. 591
Table of Contents provided by Ingram. All Rights Reserved.

Jeffrey R. Bohn, PhD, leads the Financial Strategies group at Shinsei Bank in Tokyo. Previously, he led Moody's KMV's (MKMV's) Global Research group and MKMV's Credit Strategies group. After Moody's acquired KMV, he and Roger Stein coheaded MKMV's research and product development.

Roger M. Stein, Phd, is Group Managing Director of the newly formed Quantitative Research and Analytics group at Moody's Investors Service in New York. Previously, he was head of research for Moody's Risk Management Services. After Moody's acquired KMV, he and Jeffrey Bohn co-headed MKMV's research and product development.

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