Journalartikel

Jahr der Veröffentlichung: 2010

Seiten: 2693-2706

Zeitschrift: Computational Statistics & Data Analysis

Bandnummer: 54

Heftnummer: 11

ISSN: 0167-9473

eISSN: 1872-7352

Open Access Status: Green

DOI Link: https://doi.org/10.1016/j.csda.2009.03.004

Verlag: Elsevier

Abstract: 

Basel II imposes regulatory capital on banks related to the default risk of their credit portfolio. Banks using an internal rating approach compute the regulatory capital from pooled probabilities of default. These pooled probabilities can be calculated by clustering credit borrowers into different buckets and computing the mean PD for each bucket. The clustering problem can become very complex when Basel II regulations and real-world constraints are taken into account. Search heuristics have already proven remarkable performance in tackling this problem. A Threshold Accepting algorithm is proposed, which exploits the inherent discrete nature of the clustering problem. This algorithm is found to outperform alternative methodologies already proposed in the literature, such as standard k-means and Differential Evolution. Besides considering several clustering objectives for a given number of buckets, we extend the analysis further by introducing new methods to determine the optimal number of buckets in which to cluster banks' clients.

Harvard-Zitierstil: Lyra, M., Paha, J., Paterlini, S. and Winker, P. (2010) Optimization heuristics for determining internal rating grading scales, Computational Statistics & Data Analysis, 54(11), pp. 2693-2706. https://doi.org/10.1016/j.csda.2009.03.004

APA-Zitierstil: Lyra, M., Paha, J., Paterlini, S., & Winker, P. (2010). Optimization heuristics for determining internal rating grading scales. Computational Statistics & Data Analysis. 54(11), 2693-2706. https://doi.org/10.1016/j.csda.2009.03.004