Recent Regulation in Credit Risk Management: A Statistical Framework
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 sta...
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doaj-ff094425713e41cdb6e0d102f485dc192020-11-25T02:18:27ZengMDPI AGRisks2227-90912019-04-01724010.3390/risks7020040risks7020040Recent Regulation in Credit Risk Management: A Statistical FrameworkLogan Ewanchuk0Christoph Frei1Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2G1, CanadaDepartment of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2G1, CanadaA 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.https://www.mdpi.com/2227-9091/7/2/40credit riskrisk modellingIFRS 9expected credit lossearly recognitionincome volatility |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Logan Ewanchuk Christoph Frei |
spellingShingle |
Logan Ewanchuk Christoph Frei Recent Regulation in Credit Risk Management: A Statistical Framework Risks credit risk risk modelling IFRS 9 expected credit loss early recognition income volatility |
author_facet |
Logan Ewanchuk Christoph Frei |
author_sort |
Logan Ewanchuk |
title |
Recent Regulation in Credit Risk Management: A Statistical Framework |
title_short |
Recent Regulation in Credit Risk Management: A Statistical Framework |
title_full |
Recent Regulation in Credit Risk Management: A Statistical Framework |
title_fullStr |
Recent Regulation in Credit Risk Management: A Statistical Framework |
title_full_unstemmed |
Recent Regulation in Credit Risk Management: A Statistical Framework |
title_sort |
recent regulation in credit risk management: a statistical framework |
publisher |
MDPI AG |
series |
Risks |
issn |
2227-9091 |
publishDate |
2019-04-01 |
description |
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. |
topic |
credit risk risk modelling IFRS 9 expected credit loss early recognition income volatility |
url |
https://www.mdpi.com/2227-9091/7/2/40 |
work_keys_str_mv |
AT loganewanchuk recentregulationincreditriskmanagementastatisticalframework AT christophfrei recentregulationincreditriskmanagementastatisticalframework |
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1724882008651333632 |