Please use this identifier to cite or link to this item:
Title: Enhanced PD-implied ratings by targeting the credit rating migration matrix
Authors: Duan, Jin-Chuan 
Li, Shuping 
Keywords: Default
Rating stickiness
Sequential Monte Carlo
Issue Date: 1-Nov-2021
Publisher: KeAi Communications Co.
Citation: Duan, 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.
Rights: Attribution 4.0 International
Abstract: A 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
Source Title: Journal of Finance and Data Science
ISSN: 2405-9188
DOI: 10.1016/j.jfds.2021.05.001
Rights: Attribution 4.0 International
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_1016_j_jfds_2021_05_001.pdf255.34 kBAdobe PDF




checked on Nov 21, 2022

Page view(s)

checked on Nov 17, 2022

Google ScholarTM



This item is licensed under a Creative Commons License Creative Commons