Active Credit Portfolio Management in Practice
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.
Chapter 1. The Framework: Definitions and Concepts.
What Is Credit?
Evolution of Credit Markets.
A Word About Regulation.
What Are Credit Models Good For?
Active Credit Portfolio Management (ACPM).
Framework at 30,000 Feet.
Building Blocks of Portfolio Risk.
Using PDs in Practice.
Value, Price, and Spread.
Portfolio Performance Metrics.
Data and Data Systems.
Chapter 2. ACPM in Practice.
Organizing Financial Institutions: Dividing into Two Business Lines.
Emphasis on Credit Risk.
Market Trends Supporting ACPM.
Financial Instruments Used for Hedging and Managing Risk in a Credit Portfolio.
Mark-To-Market and Transfer Pricing.
Metrics for Managing a Credit Portfolio.
Data and Models.
Evaluating an ACPM Unit.
Managing a Research Team.
Chapter 3. Structural Models.
Structural Models in Context.
A Basic Structural Model.
First-Passage Time: Black-Cox.
Practical Implementation: Vasicek-Kealhofer.
Stochastic Interest Rates: Longstaff-Schwartz.
Jump-Diffusion Models: Zhou.
Endogenous Default Barrier (Taxes and Bankruptcy Costs): Leland-Toft.
Corporate Transaction Analysis.
Other Structural Approaches.
Appendix 1. Derivation of Black-Scholes-Merton Framework for Calculating Distance-to-Default (DD).
Appendix 2. Derivation of Conversion of Physical Probability of Default (PD) to a Risk-Neutral Probability of Default (PDQ).
Chapter 4. Econometric Models.
Early Discrete Choice Models: Beaver (1966) and Altman (1968).
Hazard Rate (Duration) Models.
Example of a Hazard Rate Framework for Predicting Default: Shumway (2001).
Hazard Rates versus Discrete Choice.
Practical Applications: Falkenstein, et al. (2000) and Dwyer and Stein (2004).
Calibrating Econometric Models.
Calibrating to PDs.
Calibrating to Ratings.
Interpreting the Relative Influence of Factors in Econometric Models.
Taxonomy of Basic Data Woes.
Biased Samples Cannot Easily Be Fixed.
Appendix 1. Some Alternative Default Model Specifications.
Chapter 5. Loss Given Default.
Road to Recovery: The Timeline of Default Resolution.
Measures of LGD (Recovery).
The Relationship between Market Prices and Ultimate Recovery.
Approaches to Modeling LGD: The LossCalc (2002, 2004) Approaches and Extensions.
Chapter 6. Reduced-Form Models.
Reduced-Form Models in Context.
Basic Intensity Models.
A Brief Interlude to Discuss Valuation.
Duffie and Singleton Intensity Model.
Credit Rating Transition Models.
Default Probability Density Version of Intensity Models (Hull-White).
Generic Credit Curves.
Appendix: Kalman Filter.
Chapter 7. PD Model Validation.
The Basics. Parameter Robustness.
Measures of Model Power.
Measures of PD Levels and Calibration.
Sample Size and Confidence Bounds.
Assessing the Economic Value of More Powerful PD Models.
Avoiding Overfitting: A Walk-Forward Approach to Model Testing.
Appendix 1. Type I and Type II Error: Converting Cap Plots into Contingency Tables.
Appendix 2. The Likelihood for the General Case of a Default Model.
Appendix 3. Tables of ROC e and nmax.
Appendix 4. Proof of the Relationship between NPV Terms and ROC Terms.
Appendix 5. Derivation of Minimum Sample Size Required to Test for Default Rate Accuracy in Uncorrelated Case.
Appendix 6. Tables for Lower Bounds of e and N on Probabilities of Default.
Chapter 8. Portfolio Models.
A Structural Model of Default Risk.
Measurement of Portfolio Diversification.
Portfolio Risk Assuming No Credit Migration.
Structural Models of Default Correlation.
A Model of Value Correlation.
Probability of Large Losses.
Portfolio Loss Distribution.
Economic Capital and Portfolio Management.
Improving Portfolio Performance.
Reduced-Form Models and Portfolio Modeling.
Correlation in Intensity Models.
Integrating Market and Credit Risk.
Counterparty Risk in Credit Default Swaps (CDS) and Credit Portfolios.
Chapter 9. Building a Better Bank.
A Case Study.
Transforming the Capital Allocation Process.
Active Credit Portfolio Management (ACPM).
Data, Systems, and Metrics.
ACPM and Transforming the Bank.
About the Authors.
Roger M. Stein, PhD (New York, NY) is Group Managing Director at Moody’s where he leads the newly formed Moody’s Quantitative Research and Analytics group. Previously, he co-led MKMV’s Global Research group. Prior to that he led Moody’s Risk Management Services’ Research Group.
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