Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jfds.2021.05.001
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dc.titleEnhanced PD-implied ratings by targeting the credit rating migration matrix
dc.contributor.authorDuan, Jin-Chuan
dc.contributor.authorLi, Shuping
dc.date.accessioned2022-10-11T07:52:23Z
dc.date.available2022-10-11T07:52:23Z
dc.date.issued2021-11-01
dc.identifier.citationDuan, Jin-Chuan, Li, Shuping (2021-11-01). Enhanced PD-implied ratings by targeting the credit rating migration matrix. Journal of Finance and Data Science 7 : 115-125. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jfds.2021.05.001
dc.identifier.issn2405-9188
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/232002
dc.description.abstractA high-quality and granular probability of default (PD) model is on many practical dimensions far superior to any categorical credit rating system. Business adoption of a PD model, however, needs to factor in the long-established business/regulatory conventions built around letter-based credit ratings. A mapping methodology that converts granular PDs into letter ratings via referencing the historical default experience of some credit rating agency exists in the literature. This paper improves the PD implied rating (PDiR) methodology by targeting the historical credit migration matrix instead of simply default rates. This enhanced PDiR methodology makes it possible to bypass the reliance on arbitrarily extrapolated target default rates for the AAA and AA+ categories, a necessity due to the fact that the historical realized default rates on these two top rating grades are typically zero. © 2021 The Authors
dc.publisherKeAi Communications Co.
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceScopus OA2021
dc.subjectDefault
dc.subjectOther-exit
dc.subjectRating stickiness
dc.subjectSequential Monte Carlo
dc.typeArticle
dc.contributor.departmentRISK MANAGEMENT INSTITUTE
dc.contributor.departmentASIAN INSTITUTE OF DIGITAL FINANCE
dc.description.doi10.1016/j.jfds.2021.05.001
dc.description.sourcetitleJournal of Finance and Data Science
dc.description.volume7
dc.description.page115-125
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