Statistical aspects of setting up a credit rating system

Statistische Aspekte der Erstellung eines Kredit Ratings Verfahren

  • The new international capital standard for credit institutions (“Basel II”) allows banks to use internal rating systems in order to determine the risk weights that are relevant for the calculation of capital charge. Therefore, it is necessary to develop a system that enfolds the main practices and methods existing in the context of credit rating. The aim of this thesis is to give a suggestion of setting up a credit rating system, where the main techniques used in practice are analyzed, presenting some alternatives and considering the problems that can arise from a statistical point of view. Finally, we will set up some guidelines on how to accomplish the challenge of credit scoring. The judgement of the quality of a credit with respect to the probability of default is called credit rating. A method based on a multi-dimensional criterion seems to be natural, due to the numerous effects that can influence this rating. However, owing to governmental rules, the tendency is that typically one-dimensional criteria will be required in the future as a measure for the credit worthiness or for the quality of a credit. The problem as described above can be resolved via transformation of a multi-dimensional data set into a one-dimensional one while keeping some monotonicity properties and also keeping the loss of information (due to the loss of dimensionality) at a minimum level.
  • Die Bewertung der Qualität eines zu vergebenden Kredits (bzw. bei nachträglicher Bewertung auch die eines bereits erteilten) bezüglich der Sicherheit seiner Rückzahlung bezeichnet man als Kredit-Rating. Hierbei bietet sich natürlich aufgrund der Vielzahl der möglichen Einflussfaktoren eine Beurteilung an, die auf mehreren Einzelwerten basiert. Allerdings besteht seitens des Gesetzgebers die Tendenz zukünftig einheitliche, typischerweise eindimensionale Bewertungsskalen zu verlangen. Die oben beschriebene Problematik verlangt nach einer Transformation eines mehrdimensionalen Datensatzes in einen eindimensionalen unter Beibehaltung gewisser Monotonieeigenschaften und bei gleichzeitig minimalem Informationsverlust.

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Metadaten
Author:Beatriz Clavero Rasero
URN (permanent link):urn:nbn:de:hbz:386-kluedo-20009
Advisor:Ralph Korn
Document Type:Doctoral Thesis
Language of publication:English
Year of Completion:2006
Year of Publication:2006
Publishing Institute:Technische Universität Kaiserslautern
Granting Institute:Technische Universität Kaiserslautern
Acceptance Date of the Thesis:2006/09/19
Tag:Unreinheitsfunktion
Discriminatory power ; credit risk ; impurity functions; regression analysis
GND-Keyword:Kreditrisiko ; Regressionsanalyse; Trennschärfe <Statistik>
Faculties / Organisational entities:Fachbereich Mathematik
DDC-Cassification:510 Mathematik
MSC-Classification (mathematics):62H10 Distribution of statistics
62H12 Estimation
62H15 Hypothesis testing
62H20 Measures of association (correlation, canonical correlation, etc.)
62H30 Classification and discrimination; cluster analysis [See also 68T10]

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