Active Credit Portfolio Management in Practice / Edition 1

by Jeffrey R. Bohn, Roger M. Stein

Overview

State-of-the-art techniques and tools needed to facilitate effective credit portfolio management and robust quantitative credit analysis

Filled 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 management tools in large organizations but which at the same time, avoids technical proofs in favor of real applications.  Throughout this book, readers will be introduced to the theoretical foundations of this discipline, and learn about structural, reduced-form, and econometric models successfully used in the market today. The book is full of hands-on examples and anecdotes. Theory is illustrated with practical application. The authors' Website provides additional software tools in the form of Excel spreadsheets, Matlab code and S-Plus code. Each section of the book concludes with review questions designed to spark further discussion and reflection on the concepts presented.

Product Details

ISBN-13:	9780470080184
Publisher:	Wiley
Publication date:	04/06/2009
Series:	Wiley Finance , #384
Pages:	640
Product dimensions:	6.30(w) x 9.10(h) x 2.10(d)

About the Author

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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Table of Contents

Foreword xi

Preface xiii

Acknowledgments xxvii

Chapter 1 The Framework: Definitions and Concepts 1

What Is Credit? 2

Evolution of Credit Markets 7

Defining Risk 11

A Word about Regulation 13

What Are Credit Models Good For? 14

Active Credit Portfolio Management (ACPM) 16

Framework at 30,000 Feet 19

Building Blocks of Portfolio Risk 23

Using PDs in Practice 32

Value, Price, and Spread 34

Defining Default 38

Portfolio Performance Metrics 38

Data and Data Systems 42

Review Questions 43

Chapter 2 ACPM in Practice 45

Bank Valuation 50

Organizing Financial Institutions: Dividing into Two Business Lines 52

Emphasis on Credit Risk 57

Market Trends Supporting ACPM 59

Financial Instruments Used for Hedging and Managing Risk in a Credit Portfolio 60

Mark-to-Market and Transfer Pricing 63

Metrics for Managing a Credit Portfolio 68

Data and Models 72

Evaluating an ACPM Unit 75

Managing a Research Team 77

Conclusion 86

Review Questions 87

Exercises 87

Chapter 3 Structural Models 89

Structural Models in Context 91

A Basic Structural Model 95

Black-Scholes-Merton 100

Valuation 107

Modifying BSM 117

First Passage Time: Black-Cox 118

Practical Implementation: Vasicek-Kealhofer 124

Stochastic Interest Rates: Longstaff-Schwartz 145

Jump-Diffusion Models: Zhou 150

Endogenous Default Barrier (Taxes and Bankruptcy Costs): Leland-Toft 151

Corporate Transaction Analysis 156

Liquidity 159

Other Structural Approaches 161

Conclusion 171

Appendix 3A: Derivation of Black-Scholes-Merton Framework for Calculating Distance to Default (DD) 171

Appendix 3B: Derivation of Conversion of Physical Probability of Default (PD) to a Risk-Neutral Probability of Default (PD Q) 177

Review Questions 179

Exercises 179

Chapter 4 Econometric Models 183

Discrete-Choice Models 186

Early Discrete-Choice Models: Beaver (1966) and Altman (1968) 191

Hazard Rate (Duration) Models 196

Example of a Hazard-Rate Framework for Predicting Default: Shumway (2001) 204

Hazard Rates versus Discrete Choice 206

Practical Applications: Falkenstein et al. (2000) and Dwyer and Stein (2004) 207

Calibrating Econometric Models 215

Calibrating to PDs 216

Calibrating to Ratings 227

Interpreting the Relative Influence of Factors in Econometric Models 234

Data Issues 238

Taxonomy of Data Woes 241

Biased Samples Cannot Easily Be Fixed 244

Conclusion 249

Appendix 4A: Some Alternative Default Model Specifications 249

Review Questions 252

Exercises 252

Chapter 5 Loss Given Default 255

Road to Recovery: The Timeline of Default Resolution 258

Measures of LGD (Recovery) 260

The Relationship between Market Prices and Ultimate Recovery 265

Approaches to Modeling LGD: The LossCalc (2002, 2005) Approaches and Extensions 273

Conclusion 285

Review Questions 286

Exercises 286

Chapter 6 Reduced-Form Models 289

Reduced-Form Models in Context 291

Basic Intensity Models 296

A Brief Interlude to Discuss Valuation 310

Duffie, Singleton, Lando (DSL) Intensity Model 312

Credit Rating Transition Models 329

Default Probability Density Version of Intensity Models (Hull-White) 340

Generic Credit Curves 348

Conclusion 353

Appendix 6A: Kalman Filter 354

Appendix 6B: Sample Transition Matrices 357

Review Questions 358

Exercises 358

Chapter 7 PD Model Validation 361

The Basics: Parameter Robustness 367

Measures of Model Power 371

Measures of PD Levels and Calibration 379

Sample Size and Confidence Bounds 396

Assessing the Economic Value of More Powerful PD Models 418

Avoiding Overfitting: A Walk-Forward Approach to Model Testing 431

Conclusion 437

Appendix 7A: Type I and Type II Error: Converting CAP Plots into Contingency Tables 438

Appendix 7B: The Likelihood for the General Case of a Default Model 440

Appendix 7C: Tables of ROC ε and n max 441

Appendix 7D: Proof of the Relationship between NPV Terms and ROC Terms 441

Appendix 7E: Derivation of Minimum Sample Size Required to Test for Default Rate Accuracy in Uncorrelated Case 446

Appendix 7F: Tables for Lower Bounds of ε and N on Probabilities of Default 447

Review Questions 452

Exercises 452

Chapter 8 Portfolio Models 455

A Structural Model of Default Risk 460

Measurement of Portfolio Diversification 460

Portfolio Risk Assuming No Credit Migration 461

Structural Models of Default Correlation 465

Credit Migration 470

A Model of Value Correlation 475

Probability of Large Losses 481

Valuation 484

Return Calculations 488

Risk Calculations 491

Portfolio Loss Distribution 498

Capital 514

Economic Capital and Portfolio Management 519

Improving Portfolio Performance 521

Performance Metrics 526

Reduced-Form Models and Portfolio Modeling 530

Correlation in Intensity Models 531

Copulas 534

Frailty 536

Integrating Market and Credit Risk 541

Counterparty Risk in Credit Default Swaps (CDS) and Credit Portfolios 544

Conclusion 546

Review Questions 547

Exercises 548

Chapter 9 Building a Better Bank: A Case Study 551

Description 552

Current Organization 554

Transforming the Capital Allocation Process 556

Portfolio Analysis 558

Active Credit Portfolio Management (ACPM) 562

Data, Systems, and Metrics 563

ACPM and Transforming the Bank 566

Appendix: Figures 569

Exercises 574

References 575

About the Authors 589

Index 591