Internal Credit Risk Models: Capital Allocation and Performance Measurement

This work provides a practical, accessible step-by-step analysis of the theory and practicalities of credit risk measurement and management. Topics covered include: default probabilities; expected and unexpected losses; time effects; default correlations; and loss distributions.

Table of Contents:
Internal Credit Risk Models Capital Allocation and Performance Measurement -------------------------------------------------------------------------------- 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

About the Author :
Dr. Michael K. Ong is an Executive Vice President and Chief Risk Officer for the Americas for Credit Agricole Indosuez in New York. He has enterprise-wide responsibility for all risk management functions for the Carr Futures Group globally and CAI's North American entities. He is a member of the Executive Committee and additionally, he is a member of the board for Carr Global Advisors. Before joining Credit Agricole Indosuez Michael was the senior vice president and head of enterprise risk for ABN Amro Bank. There he was responsible for the management of information and decision support function for the executive committee on enterprise-wide market, operational, credit and liquidity risk, as well as RAROC and ROE models. Prior to this Michael headed the corporate research unit at First Chicago NBD Corporation, where he was chair of the global risk management research council and head of the market risk analysis unit. He was previously also responsible for quantitative research at Chicago Research and Trading Group (now Nations Banc-CRT) and has served as an assistant professor of mathematics at Bowdoin College. Michael is also an adjunct professor at the Stuart School of Business of the Illinois Institute of Technology. He received a BS degree in physics, cum laude, from the University of the Philippines and degrees of MA in physics and MS and PhD in applied mathematics from the State University of New York at Stony Brook. Michael is a member of the editorial boards of the Journal of Financial Regulation and Compliance and the Journal of Risk.

Review :
"An excellent book... practical rigorous, well-written and easy to understand" Angelo Arvanitis, Egnatia Bank "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...The book will become an essential guide to measuring credit risk."