Tsukahara, Fábio Yasuhiro and Kimura, Herbert and Sobreiro, Vinicius Amorim and Arismendi Zambrano, Juan (2016) Validation of default probability models: A stress testing approach. International Review of Financial Analysis, 47. pp. 70-85. ISSN 1057-5219

Preview

Download (786kB) | Preview

Abstract

This study aims to evaluate the techniques used for the validation of default probability (DP) models. By generating simulated stress data, we build ideal conditions to assess the adequacy of the metrics in different stress scenarios. In addition, we empirically analyze the evaluation metrics using the information on 30,686 delisted US public companies as a proxy of default. Using simulated data, we find that entropy based metrics such as measure M are more sensitive to changes in the characteristics of distributions of credit scores. The empirical sub-samples stress test data show that AUROC is the metric most sensitive to changes in market conditions, being followed by measure M. Our results can help risk managers to make rapid decisions regarding the validation of risk models in different scenarios.

Item Type:	Article
Keywords:	Portfolio; Credit risk; Banking; Default probability; Validation techniques;
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	10204
Identification Number:	https://doi.org/10.1016/j.irfa.2016.06.007
Depositing User:	Juan Arismendi Zambrano
Date Deposited:	12 Nov 2018 14:35
Journal or Publication Title:	International Review of Financial Analysis
Publisher:	Elsevier
Refereed:	Yes
URI:	https://www.sciencedirect.com
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

Repository Staff Only(login required)

Item control page

Downloads

Origin of downloads