Recent Regulation in Credit Risk Management: A Statistical Framework

Logan Ewanchuk,Christoph Frei

doi:10.3390/risks7020040

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.

Highlights

The global financial crisis of 2007–2008 highlighted the delayed recognition of credit losses as a weakness of the accounting standards at that time
The introduction of International Financial Reporting Standard (IFRS) 9 addressed this issue by requiring banks to build provisions for the lifetime expected credit loss (ECL), rather than one-year ECL, when the credit risk of a loan has significantly increased
IFRS 9 poses the challenging task to banks of defining what exactly such a significant increase in credit risk means for each of their loans

Summary

Introduction

The global financial crisis of 2007–2008 highlighted the delayed recognition of credit losses as a weakness of the accounting standards at that time. Board introduced International Financial Reporting Standard (IFRS) 9, which is an accounting standard that requires banks to recognize increased credit risk of loans early and build additional provisions for such loans. We introduce and analyze a framework to model this trade-off between early recognition of increased credit risk and avoidance of excessive income volatility. Finding a balance between early recognition of credit risk and income volatility, in this sense, is an interesting problem that is at the heart of this paper and directly linked to the new loss impairment standards of IFRS 9. Since Merton (1974) models the underlying asset value of the obligor as a stochastic process over time, we can formulate an optimization problem that considers the impact on the income statement at different reporting moments.

Modelling Income Volatility and Early Recognition of Credit Risk

Analyzing the Optimization Problem for Continuous Asset Distribution

Modelling the Net Asset Value with Brownian Motion

Specific Increments

General Increments

Selection of λ in Comparison to European Union Framework

Findings

Conclusions