The review of the open challenges in the IRB loan portfolio credit risk modeling

Issue title: Special Issue In commemoration of Professor Sergey Aivazian and his remarkable contributions to statistics and econometrics: Multivariate Statistics in Applications to Econometrics

Guest editors: Henry Penikas

Article type: Research Article

Authors: Penikas, Henry¹

Affiliations: Bank of Russia, The National Research University Higher School of Economics, The P.N. Lebedev Physical Institute, Moscow, Russia | E-mail: Penikas@hse.ru

Correspondence: [*] Corresponding author: Bank of Russia, The National Research University Higher School of Economics, The P.N. Lebedev Physical Institute, Moscow, Russia. E-mail: Penikas@hse.ru.

Note: [1] The opinions expressed in the paper are solely those of the author and may not represent those of the affiliated institutions.

Abstract: The Basel Committee on Banking Supervision finalized the Basel III accord in the December 2017 and launched the set of its standards – the Basel Framework – in December 2019. Both documents allow bank to use mathematical models for the credit risk estimation. There are quantitative and qualitative requirements for models to be allowed for use in the prudential regulation of banks. The approach is called an Internal-Ratings-Based one (IRB). This paper aims at discussing a set of issues related to IRB credit risk modeling and such model estimates use. Those issues include data pooling in the credit registries, applying copula-discriminant analysis, validating the borrower concentration per grade, assigning the hybrid credit rating, use of model estimates when voting at the credit committee, estimate of the ultimate credit risk-taking by banks.

Keywords: trajectory cluster-analysis, copula discriminant analysis, basel committee, IRB, credit risk, regulation, credit committee voting, data pooling, hybrid credit rating, credit registry

DOI: 10.3233/MAS-200508

Journal: Model Assisted Statistics and Applications, vol. 15, no. 4, pp. 371-388, 2020