[article]
Titre : |
Cyclical adjustment of point-in-time PD |
Type de document : |
texte imprimé |
Auteurs : |
S. Ingolfsson, Auteur ; B. T. Elvarsson, Auteur |
Année de publication : |
2011 |
Article en page(s) : |
pp. 374–380 |
Note générale : |
Recherche opérationnelle |
Langues : |
Anglais (eng) |
Mots-clés : |
Banking Risk Capital budgeting Time series Forecasting |
Index. décimale : |
001.424 |
Résumé : |
Banking regulation stipulates that to calculate minimum capital requirements a long-term average of annual default probability (PD) should be used. Typically, logistic regression is applied with a 12-month sample period to obtain retail PD estimates. Thus the output will reflect the default rate in the sample, and not the long-term average. The ensuing calibration problem is addressed in the paper by a ‘variable scalar methodology’, based on an actual application in a commercial bank. Using quarterly intra-bank loss data over 15 years, a state-space model of the credit cycle is estimated by a Kalman filter, resulting in a structural decomposition of the credit cycle. This yields an adjustment factor for each point in the cycle for each of two client segments. The regulatory compliance aspects of such a framework, as well as some practical issues are presented and discussed. |
DEWEY : |
001.424 |
ISSN : |
0160-5682 |
En ligne : |
http://www.palgrave-journals.com/jors/journal/v61/n3/abs/jors2009136a.html |
in Journal of the operational research society (JORS) > Vol. 61 N° 3 (Mars 2010) . - pp. 374–380
[article] Cyclical adjustment of point-in-time PD [texte imprimé] / S. Ingolfsson, Auteur ; B. T. Elvarsson, Auteur . - 2011 . - pp. 374–380. Recherche opérationnelle Langues : Anglais ( eng) in Journal of the operational research society (JORS) > Vol. 61 N° 3 (Mars 2010) . - pp. 374–380
Mots-clés : |
Banking Risk Capital budgeting Time series Forecasting |
Index. décimale : |
001.424 |
Résumé : |
Banking regulation stipulates that to calculate minimum capital requirements a long-term average of annual default probability (PD) should be used. Typically, logistic regression is applied with a 12-month sample period to obtain retail PD estimates. Thus the output will reflect the default rate in the sample, and not the long-term average. The ensuing calibration problem is addressed in the paper by a ‘variable scalar methodology’, based on an actual application in a commercial bank. Using quarterly intra-bank loss data over 15 years, a state-space model of the credit cycle is estimated by a Kalman filter, resulting in a structural decomposition of the credit cycle. This yields an adjustment factor for each point in the cycle for each of two client segments. The regulatory compliance aspects of such a framework, as well as some practical issues are presented and discussed. |
DEWEY : |
001.424 |
ISSN : |
0160-5682 |
En ligne : |
http://www.palgrave-journals.com/jors/journal/v61/n3/abs/jors2009136a.html |
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