Recent Regulation in Credit Risk Management: A Statistical Framework

Risks (2019) 7, 40 DOI:10.3390/risks7020040

19 Pages Posted: 14 May 2019

See all articles by Logan Ewanchuk

Logan Ewanchuk

University of Alberta - Department of Mathematical and Statistical Sciences

Christoph Frei

University of Alberta - Department of Mathematical and Statistical Sciences

Date Written: April 14, 2019

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.

Keywords: credit risk, risk modelling, IFRS 9, expected credit loss, early recognition, income volatility

JEL Classification: G32, C51

Suggested Citation

Ewanchuk, Logan and Frei, Christoph, Recent Regulation in Credit Risk Management: A Statistical Framework (April 14, 2019). Risks (2019) 7, 40 DOI:10.3390/risks7020040, Available at SSRN: https://ssrn.com/abstract=3371861

Logan Ewanchuk

University of Alberta - Department of Mathematical and Statistical Sciences

University of Alberta
Mathematical and Statistical Sciences
Edmonton, Alberta T6G 2G1
Canada

Christoph Frei (Contact Author)

University of Alberta - Department of Mathematical and Statistical Sciences ( email )

Edmonton, Alberta T6G 2G1
Canada

HOME PAGE: http://www.math.ualberta.ca/~cfrei/

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