A proposed benchmark model using a modularised approach to calculate IFRS 9 expected credit loss

References

• Aalen, O. O., & Johansen, S. (1978). An empirical transition matrix for non-homogeneous markov chains based on censored observations. Scandinavian Journal of Statistics, 5(3), 141–27. https://www.jstor.org/stable/4615704
   Web of Science ®Google Scholar
• Allignol, A., Schumacher, M., & Beyersmann, J. (2011). Empirical transition matrix of multi-state models: The etm package. Journal of Statistical Software, 38(4), 1–15. https://doi.org/10.18637/jss.v038.i04
   Web of Science ®Google Scholar
• Aptivaa. (2016b). Building blocks of impairment modeling (Issue 02) [ Online]. http://www.aptivaa.com/blog/wp-content/uploads/2016/04/Blog_02-Building-Blocks-of-Impairment-Modeling.pdf
   Google Scholar
• Aptivaa. (2016c). Cash shortfall & LGD – Two sides of the same coin [ Online]. http://www.aptivaa.com/blog/cash-shortfall-lgd-two-sides-of-the-same-coin/
   Google Scholar
• Aptivaa. (2016c). IFRS9 model risk management - Given the short shrift? (Issue 14) [ Online]. http://www.aptivaa.com/blog/wp-content/uploads/2016/08/IFRS9-Model-Risk-Management.pdf
   Google Scholar
• Aptivaa. (2016d). Crystal gazing - Estimating lifetime PDs (Issue 10) [ Online]. http://www.aptivaa.com/blog/wp-content/uploads/2016/05/Crystal-Gazing-Estimating-Lifetime-PDs.pdf
   Google Scholar
• Aptivaa. (2016e). PD calibration – A delicate balancing act [ Online]. http://www.aptivaa.com/blog/wp-content/uploads/2016/04/PD-Calibration-A-Delicate-Balancing-Act.pdf
   Google Scholar
• Aptivaa. (2016f). Impairment modelling in retail – Many moving parts [ Online]. http://www.aptivaa.com/blog/wp-content/uploads/2016/07/Impairment-Modeling-in-Retail-Many-Moving-Parts.pdf
   Google Scholar
• Basel Committee on Banking Supervision (BCBS). (2006). Basel II: International convergence of capital measurement and capital standards: A revised framework. Bank for International Settlements.
   Google Scholar
• Basel Committee on Banking Supervision (BCBS). (2015). BCBS (Basel Committee on banking Supervision): Guidance on accounting for expected credit losses. Bank of International Settlement. https://www.bis.org/bcbs/publ/d350.htm
   Google Scholar
• Beerbaum, D. (2015). Significant increase in credit risk according to IFRS 9: Implications for financial institutions. International Journal of Economics and Management Sciences, 4(9), 1–3. https://doi.org/10.4172/21626359.1000287
   Google Scholar
• Bellotti, T., & Crook, J. (2012). Loss given default models incorporating macroeconomic variables for credit cards. International Journal of Forecasting, 28(1), 171–182. https://doi.org/10.1016/j.ijforecast.2010.08.005
   Web of Science ®Google Scholar
• Bülbül, D., Hakenes, H., & Lambert, C. (2019). What influences banks’ choice of credit risk management practices? Theory and evidence. Journal of Financial Stability, 40(1), 1–14. https://doi.org/10.1016/j.jfs.2018.11.002
   Web of Science ®Google Scholar
• Chawla, G., Forest, L. R., Jr., & Aguais, S. D. (2016). Point-in-time loss-given default rates and exposures at default models for IFRS 9/CECL and stress testing. Journal of Risk Management in Financial Institutions, 9(3), 249–263.
   Google Scholar
• Cohen, B. H., & Edwards, G. A. (2017). The new era of expected credit loss provisioning. Bank of International Settlement. Quarterly Review.
   Google Scholar
• European Banking Authority (EBA). (2016). Consultation paper EBA/CP/2016/10: Draft guidelines on credit institutions’ credit risk management practices and accounting for expected credit losses [ Online]. https://www.eba.europa.eu/documents/10180/1532063/EBA-CP-2016-10±%28CP±on±Guidelines±on±Accounting±for±Expected±Credit%29.pdf
   Google Scholar
• Financial Conduct Authority (FCA). (2018). The prudential sourcebook for investment firms.
   Google Scholar
• Global Public Policy Committee (GPPC). (2016). The implementation of IFRS 9 impairment requirements by banks: Considerations for those charged with governance of systemically important banks. http://www.ey.com/Publication/vwLUAssets/Implementation_of_IFRS_9_impairment_requirements_by_systemically_important_banks/$File/BCM-FIImpair-GPPC-June2016%20int.pdf
   Google Scholar
• Guillot, M. (2011). Period versus cohort life expectancy. In R. G. Rogers & E. M. Crimmins (Eds.), International handbook of adult mortality (pp. 625). Springer Science + Business Media.
   Google Scholar
• Hallblad, J. (2015). The multi-year through-the-cycle and point-in-time probability of default [Master’s Thesis in Economics]. UMEA University.
   Google Scholar
• IFRS. (2014). IRFS9 financial instruments: Project summary [ Online]. http://www.ifrs.org/Current-Projects/IASB-Projects/Financial-Instruments-A-Replacement-of-IAS-39-Financial-Instruments-Recognitio/Documents/IFRS-9-Project-Summary-July-2014.pdf
   Google Scholar
• Jong, R. (2016). Practical aspects of implementing IFRS9 requirements for expected credit losses. KMPG. https://assets.kpmg.com/content/dam/kpmg/is/pdf/2016/10/IFRS%209/2_Ryan%20Jong.pdf
   Google Scholar
• McPhail, J., & McPhail, L. (2014). Forecasting lifetime credit losses: Modelling considerations for complying with the new FASB and IASB current expected loss models. Journal of Risk Management in Financial Institutions, 7(4), 375–388. http://bit.ly/2qzrWAF
   Google Scholar
• Miu, P., & Ozdemir, B. (2017). Adapting the Basel II advanced internal ratings-based models for International financial reporting standard 9. Journal of Credit Risk, 13(2), 53–83. https://doi.org/10.21314/JCR.2017.224
   Web of Science ®Google Scholar
• Novotny-Farkas, Z. (2016). The interaction of the IFRS 9 expected loss approach with supervisory rules and implications for financial stability. Accounting in Europe, 13(2), 197–227. https://doi.org/10.1080/17449480.2016.1210180
   Web of Science ®Google Scholar
• Tasche, D. (2013). The art of probability-of-default curve calibration. Journal of Credit Risk, 9(4), 63–103. https://doi.org/10.21314/JCR.2013.169
   Web of Science ®Google Scholar
• Wenzelburger, H., & Gersbach, J. (2010). Sophistication in risk management,bank equity, and stability. International Review of Finance, 10(1), 63–91. https://doi.org/10.1111/j.1468-2443.2010.01105.x
   Google Scholar
• Yang, B. H., Wu, B., Cui, K., Du, Z., & Fei, G. (2019). International financial reporting standard 9 expected credit loss estimation: Advanced models for estimating portfolio loss and weighting scenario losses. Journal of Risk Model Validation, 14(1), 1–16. https://doi.org/10.21314/JRMV.2020.217.
   Web of Science ®Google Scholar