Product Description

The authoritative guide on internal credit risk measurement and management for financial institutions.

With unequalled conviction, Ong shows how an internal model can be properly implemented by explaining the fundamentals of the quantitative building blocks that apply. With accessible mathematics and highly illustrative explanations he removes the obstacles to provide practical solutions that will inspire you to greater confidence.

In tandem with a historical overview of the market response to regulatory directives, discussion of the inadequacies of the current regulatory framework is of key concern throughout the book. Calling on more concise, global standardisation for the measurement of credit risk the author clarifies the ambiguity inherent in current regulation to arm you with the required tools for effective internal modelling.

Internal Credit Risk Models provides you with methods of approaches in order to ascertain credible credit loss accounts in the event of an extreme market occurrence. What’s more it discusses default probabilities, expected and unexpected losses, time effects, default correlations and extreme value theory.

Product Details

• Amazon Sales Rank: #744277 in Books
• Published on: 1999-01-05
• Original language: English
• Binding: Hardcover
• 372 pages

Review
An excellent book... practical rigorous, well- written and easy to understand. -- Angelo Arvanitis, Egnatia Bank

The book will become an essential guide to measuring credit risk. -- Thomas Donahoe, Director, Metropolitan Life Insurance Company

About the Author
Dr Michael K. Ong, is a senior vice president and head of enterprise risk for ABN Amro Bank. He is responsible for the management 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.

Before joining ABN Amro, Michael was head of 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. Prior to First Chicago NBD, he was 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.

Customer Reviews

Internal Credit Risk Models
This book is the most comprehensive literature I have seen for credit risk modeling. It covers from the basic BIS regulatory capital framework to the state-of-the-art credit risk models. Numerous worked examples demonstrate the calculation for different risk weighted capitals with or without netting clearly. Well-known credit models such as KMV, CreditMetric and CreditRisk+ are rigorously explained. Advanced issues like default correlation, joint credit migration and loss tail events are also addressed appropriately. With the general framework, economic capital and risk adjusted performance measurement can be quantified. This book is full of concise but descriptive flow charts and diagrams for implementation purpose. I strongly recommend this book to those who want to understand and implement an internal credit model for capital regulatory and allocation purpose.

A Clear approach to a complex subject
It provides a coherent framework for thinking about and modeling bank credit risk. It describes how to model default probabilities, expected and unexpected losses, time effects, default correlation, loss distribution and RAROC measures. The writer is a practioner and makes simple and important observations about the important issues. The text is clear and mathematics is used as example, not as explanation.

A great credit risk modelling book!
Detailed, but not overly mathematic.

Good coverage of all the main points required of any good credit risk measurement, management, and reporting system. Would recommend this book to anyone who might be using KMV's Portfolio Manager product, or for someone who's creating/programming a new credit risk management/reporting system and needs a good framework from which to copy from.