Estimation of Probability of Default in Low Default Portfolios
Abstract: Estimation of probability of default (PD) is a fundamental part of credit risk modeling, and estimation of PD in low default portfolios is a common issue for banks and ﬁnancial institutions. The Basel Committee on Banking Supervision requires banks and ﬁnancial institutions to add an additional margin of conservatism to its PD estimates in the case of insuﬃcient data, as in low default portfolios with few default observations. In addition, the Basel regulations also require banks to report PD estimates on grade level. The purpose of this thesis is to study methods for estimation of probability of default in low default portfolios. In order to fulﬁll this purpose, two diﬀerent models for estimation of probability of default in low default portfolios are considered. These are the Benjamin, Cathcart and Ryan (BCR) approach and a Bayesian approach. Because these models estimate PD on a portfolio level, diﬀerent methods for allocation of portfolio PDs to rating grades are also considered. Lastly, methods to assign portfolio PDs to grade level for a portfolio consisting of several subportfolios are compared.
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