Please use this identifier to cite or link to this item:
Title: Probability of Default Model Validation
Issue Date: 2007
Citation: YONG NING FOO (2007). Probability of Default Model Validation. ScholarBank@NUS Repository.
Abstract: In order for an institution to qualify for the Basel II internal ratings based (IRB) approach for measuring credit risk capital, a financial institution would need to have a robust validation framework in place. For that purpose, a model validation framework (together with two excel programs) had been developed by Oliver Wyman for OCBC Bank. In this project, we examine the applicability and implementation accuracy focusing on the two most important aspects of model validation: (1) measuring the discriminative power and (2) calibration accuracy. The Receiver Operator Characteristic (ROC) curve and its summary statistics, area under ROC (AUC) and accuracy ratio form the most important set of tools in evaluating the discriminative power of the models. The documentation as given in [1] and the implementation as provided by [4] has been found to be sound and accurate. The anchor point test and the curve calibration shape test form the two most important tests to evaluate the calibration accuracy of the models. Except for the failure to correctly compute the standard error of adjusted default rate as documented in section 4.1.6, the documentation and implementation as found in [1] and [5] respectively have been found to be sound and accurate.
Appears in Collections:Master's Theses (Restricted)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Yong Ning Foo_Yong Ning Foo - NUS-OCBC - Thesis.pdf310.77 kBAdobe PDF


NoneLog In

Page view(s)

checked on Sep 18, 2020


checked on Sep 18, 2020

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.