Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/257878 
Year of Publication: 
2019
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 7 [Issue:] 2 [Article No.:] 40 [Publisher:] MDPI [Place:] Basel [Year:] 2019 [Pages:] 1-19
Publisher: 
MDPI, Basel
Abstract: 
A recently introduced accounting standard, namely the International Financial Reporting Standard 9, requires banks to build provisions based on forward-looking expected loss models. When there is a significant increase in credit risk of a loan, additional provisions must be charged to the income statement. Banks need to set for each loan a threshold defining what such a significant increase in credit risk constitutes. A low threshold allows banks to recognize credit risk early, but leads to income volatility. We introduce a statistical framework to model this trade-off between early recognition of credit risk and avoidance of excessive income volatility. We analyze the resulting optimization problem for different models, relate it to the banking stress test of the European Union, and illustrate it using default data by Standard and Poor's.
Subjects: 
credit risk
risk modelling
IFRS 9
expected credit loss
early recognition
income volatility
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Appears in Collections:

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.