• The authoritative introduction to internal credit risk modelling and management for financial institutions

• Topics covered include: default probabilities; expected and unexpected losses; time effects; default correlations; and loss distributions

CONTENTS

On Basle, Regulation and Market Responses Past and Present

Origins of the Regulatory Capital Framework
Some Historical Perspectives
Historical Rational for the Capital Accord
Credit Risk, Regulatory Capital and the Basle Accord
Evolutionary Nature of Capital Regulation
Market Response: Clamour for Internal Credit Models
Game Theory: Regulatory Capital Arbitrage
Securitisation of Assets
Concerns Raised by Securitisation
Role of Credit Derivatives
Summary of Federal Deposit Insurance Corporation Improvement Act 1991
Regulatory Capital Rules

Overview of Approach

Essential Components of the Internal Credit Risk Model
Outline of Model Components
Preview of Following Chapters

Modelling Credit Risk

Elements of Credit Risk
Default Risk
Measuring Default Probability - Empirical Method
Measuring Default Probability - The Options Theory Approach
Theoretical EDFs and Agency Ratings
Credit Risk Models
Value of Risk Debt
States of the Default Process and Credit Migration
Merton's Options Theory Approach to Risky Debt
Default Probability, the Default Point and the Distance to Default
Mathematical Preliminary
The Multi-State Default Process and the Probability Measure

Loan Portfolios and Expected Loss

Expected Loss
Adjusted Exposure: Outstandings and Commitments
Covenants
Adjusted Exposure
Usage Given Default
Loss Given Default and the Risky Part of V1
Mathematical Derivation of Expected Loss
Parameterising Credit Risk Models

Unexpected Loss

Causes of Unanticipated Risk
Unexpected Loss
Economic Capital and Unexpected Loss
Derivation of Unexpected Loss (UL)

Portfolio Effects: Risk Contribution and Unexpected Losses

Comparing Expected Loss and Unexpected Loss
The Analysis Horizon and Time to Maturity
Portfolio Expected Loss
Portfolio Unexpected Loss
Risk Contribution
Undiversifiable Risk
Risk Contribution and Correlation of Default
Variation in Asset Value due to Time Effects
Derivation of Portfolio UL
Derivation of Portfolio RCk

Correlation of Default and Credit Quality

Correlation of Credit Quality
Correlation of Default
Default Correlation Matrix and Some Important Observations
Industry Index and Asset Correlation
Estimating Asset Correlation
Obligor-Specific Risk
Further Generalisation to the Multifactor Case
Some Comments and Suggestions
Correlation of Default
First-Passage Time Model of Default Correlation
Industry Default Correlation Matrix
Correlation of Joint Credit Quality Movement

Loss Distribution for Credit Default Risk

Choosing the Proper Loss Distribution
The Beta Distribution
Economic Capital and Probability of Loss
Extreme Events: Fitting the Tail

Monte Carlo Simulation of Loss Distribution

Simulating the Loss Distribution
Some Observations From the Examples
Why EVT and not just Simulation
Mathematics of Loss Simulation
Simulating Default and the Default Point

Extreme Value Theory

Fundamental Regimes for Losses
Extreme Value Theory - Some Basics
Generalised Pareto Distribution
Convergence Criteria
Thresholds Revisited
The Mean Excess Function
History Repeating by Alexander McNeil

Risk-Adjusted Performance Measurement

Risk-Adjusted Performance Measurement
Raroc Defined
Dissecting the Raroc Equation
Approaches to Measurement: Top-Down or Bottom-Up
Revised RAPM

Implementing the Internal Model Across the Enterprise

Sample Portfolio
Negative Raroc
Parameterising and Calibrating the Internal Model
Interpreting the Results of Raroc
Enterprise-Wide Risk Management and RAPM
Sample Credit Portfolio
On to the Next Steps

Credit Concentration and Required Spread

The Credit Paradox
Causes of Concentration Risk
Credit Concentration and Required Spread
The Loan Pricing Calculator
Mathematics of the Loan Pricing Calculator

Epilogue: The Next Steps

Internal Credit Risk Ratings
Data Quality and Opaqueness
Techniques for Assessing Extreme Loss Distributions
Risk-Adjusted Performance Measurement and Risk-Adjusted Pricing
Multi-State Default Process, Marking-to-Market and Multi-Year Analysis Horizons
Differences Between Vendor Models
Integration of Market Risk and Credit Risk
The Multi-State Default Process
Matching Transition Matrices to Historical Data

Appendix

Raroc Remodelled
Tom Wilson

Many Happy Returns
Sanjeev Punjabi

Reconcilable Differences
H. Ugur Koyluoglu and Andrew Hickman

Refining Ratings
Ross Miller

A Credit Risk Toolbox
Angelo Arvanitis, Christopher Browne, Jon Gregory, and Richard Martin

Reviewed by Thomas Donahoe, Director, Metropolitan Life Insurance Company

You will find no better guide to today's spirited debate over proper methods to measure credit risk. Michael Ong challenges risk managers and bank regulators alike as he leads an insightful review of the flaws of the current regulatory capital framework. His prose is direct and his analysis is thorough. Only a leading practitioner could speak convincingly of inadequate solutions, flawed frameworks, and irresponsible regulation.

Through simple explanations and accessible mathematics, Dr. Ong opens the credit risk debate to a wide audience. The book focuses on a large banking book and examines the critical issues of default correlation, data deficiencies, and model error. Using an intuitive approach, Dr. Ong presents the building blocks of risks control in a systematic fashion. The book offers an especially fine presentation on expected and unexpected losses and on extreme value theory. He shows convincingly how an internal model can be properly implemented. Capital allocation and performance measurement receive a thorough treatment. The author achieves his goal of promoting transparency of internal credit risk models which should lead to a greater confidence by senior managers and regulators alike.

Ong's voice can be heard throughout the book beckoning and challenging the reader to help the reform process. The book will become an essential guide to measuring credit risk. A strong text, supplemented by an extensive glossary, index and bibliography as well as an appendix containing 5 important papers on the topic. The publisher is to be commended for its well-placed confidence in Michael Ong to be its first single author work.

Michael K. Ong is professor of finance and director of the finance program at The Stuart Graduate School of Business, Illinois Institute of Technology. He is also executive director of the Center for Financial Markets. Until recently, Dr Ong was executive vice president and chief risk officer for Credit Agricole Indosuez in New York. He had enterprise-wide responsibility for all risk management functions for corporate banking, merchant banking, asset management, capital markets activities, and the Carr Futures Group. Dr Ong received a BS degree in physics, MS degree in applied mathematics, and a PhD degree in applied mathematics from the State University of New York at Stony Brook.