Arbeitspapier

Modeling dependencies between rating categories and their effects on prediction in a credit risk portfolio

The internal-ratings based Basel II approach increases the need for the development of more realistic default probability models. In this paper we follow the approach taken in McNeil and Wendin (2006) by constructing generalized linear mixed models for estimating default probabilities from annual data on companies with different credit ratings. The models considered, in contrast to McNeil and Wendin (2006), allow parsimonious parametric models to capture simultaneously dependencies of the default probabilities on time and credit ratings. Macro-economic variables can also be included. Estimation of all model parameters are facilitated with a Bayesian approach using Markov Chain Monte Carlo methods. Special em- phasis is given to the investigation of predictive capabilities of the models considered. In particular predictable model specifications are used. The empirical study using default data from Standard and Poor gives evidence that the correlation between credit ratings further apart decreases and is higher than the one induced by the autoregressive time dynamics.

Sprache
Englisch

Erschienen in
Series: Discussion Paper ; No. 511

Thema
credit risk
default probability
asset correlation
generalized linear mixed models
Markov chain Monte Carlo
prediction

Ereignis
Geistige Schöpfung
(wer)
Czado, Claudia
Pflüger, Carolin
Ereignis
Veröffentlichung
(wer)
Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
(wo)
München
(wann)
2006

DOI
doi:10.5282/ubm/epub.1880
Handle
URN
urn:nbn:de:bvb:19-epub-1880-2
Letzte Aktualisierung
10.03.2025, 11:41 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Czado, Claudia
  • Pflüger, Carolin
  • Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen

Entstanden

  • 2006

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