Recent Regulation in Credit Risk Management: A Statistical Framework
Logan Ewanchuk () and
Christoph Frei ()
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Logan Ewanchuk: Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2G1, Canada
Christoph Frei: Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2G1, Canada
Risks, 2019, vol. 7, issue 2, 1-19
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.
Keywords: credit risk; risk modelling; IFRS 9; expected credit loss; early recognition; income volatility (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 M2 M4 K2 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:7:y:2019:i:2:p:40-:d:222690
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