Based on historical data, we simulate time series using International Financial Reporting Standards (IFRS) 9's expected credit loss (ECL) model and analyze how these behave compared to loan loss reserves under International Accounting Standard (IAS) 39. While the current model under IAS 39 has been prone to build provisions considered ＂too little, too late,＂ the ECL model is supposed to be more forward-looking. We use a stage-based simulation model to estimate the three components of ECL, probability of default, exposure at default and loss given default, while simultaneously considering the three IFRS 9 stages and possible stage transitions of financial assets. Calibrating our model with European banking data from 2005 to 2014, we develop modeling approaches to all ECL components based on real world assumptions, estimate the expected amount of reserves under the new ECL model, and test to which parameters this amount is most sensitive. Our results suggest that while simulated ECL reserves are not higher compared to IAS 39 reserves in general, they tend to exceed IAS 39 reserves during times of crises. Simulated reserves are very volatile to changes in the market environment and differ substantially for more troubled compared to non-troubled banks, as well as across European countries and regions. We further find a high sensitivity of the ECL depending on the model in use to estimate the probability of default. Our estimates suggest that IFRS 9 reserves are not likely to result in the International Accounting Standards Board's (IASB) envisaged increase in countercyclical loan loss reserves.
|Tidsskrift||Advances in Quantitative Analysis of Finance and Accounting|
|Status||Udgivet - dec. 2018|
Bibliografisk noteCBS Bibliotek har ikke adgang til materialet
- European banking industry
- International Financial Reporting Standard 9 (IFRS 9)
- Expected credit loss model
Seitz, B., Dinh, T., & Rathgeber, A. (2018). Understanding Loan Loss Reserves under IFRS 9: A Simulation-based Approach. Advances in Quantitative Analysis of Finance and Accounting, 16, 311-357. https://doi.org/10.6293/AQAFA.201812_16.0010