Repositorio de producción científica de la Universidad de Sevilla

The probability of default in internal ratings based (IRB) models in Basel II: an application of the rough sets methodology

 

Advanced Search
 
Opened Access The probability of default in internal ratings based (IRB) models in Basel II: an application of the rough sets methodology
Cites
Show item statistics
Icon
Export to
Author: Samaniego Medina, Reyes
Vázquez Cueto, María José
Coordinator/Director: Cossío Silva, Francisco José
Department: Universidad de Sevilla. Departamento de Economía Aplicada III
Date: 2009
ISBN/ISSN: 9788473566094
Document type: Presentation
Abstract: El nuevo Acuerdo de Capital de junio de 2004 (Basilea II) da cabida e incentiva la implantación de modelos propios para la medición de los riesgos financieros en las entidades de crédito. En el trabajo que presentamos nos centramos en los modelos ...
[See more]
The new Capital Accord of June 2004 (Basel II) opens the way for and encourages credit entities to implement their own models for measuring financial risks. In the paper presented, we focus on the use of internal rating based (IRB) models for the assessment of credit risk and specifically on the approach to one of their components: probability of default (PD). In our study we apply the rough sets methodology to a database composed of 106 companies, applicants for credit, with the object of obtaining those ratios that discriminate best between healthy and bankrupt companies, together with a series of decision rules that will help to detect the operations potentially in default, as a first step in modelling the probability of default. Lastly, we compare the results obtained against those obtained using classic discriminant análisis. We conclude that the rough sets methodology presents better risk classification results.
Size: 109.3Kb
Format: PDF

URI: https://hdl.handle.net/11441/77835

This work is under a Creative Commons License: 
Attribution-NonCommercial-NoDerivatives 4.0 Internacional

This item appears in the following Collection(s)