UNDERSTANDING LOAN LOSS RESERVES UNDER IFRS 9: A SIMULATION-BASED APPROACH

10.6293/AQAFA.201812_16.0010

Barbara Seitz；Tami Dinh；Andreas Rathgeber

Expected Credit Loss Model ； International Financial Reporting Standard 9 (IFRS 9) ； Simulation ； European Banking Industry

Advances in Quantitative Analysis of Finance and Accounting

16期（2018 / 12 / 31）

311 - 357

英文

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.

1. Altman, E. I.,Brady, B.,Resti, A.,Sironi, A.(2005).The link between default and recovery rates: Theory, empirical evidence, and implications.The Journal of Business,78(6),2203-2228.
2. Barclays(2017).,未出版
3. Basel Committee on Banking Supervision(2015).Revisions to the standardised approach for credit risk.
4. Basel Committee on Banking Supervision(2015).The interplay of accounting and regulation and its impact on bank behaviour: Literature review.Basel, Switzerland:Bank for International Settlements.
5. Basel Committee on Banking Supervision(2016).Regulatory treatment of accounting provisions.
6. Beatty, A.,Liao, S.(2014).Financial accounting in the banking industry: A review of the empirical literature.Journal of Accounting and Economics,58,339-383.
7. Beatty, A.,Liao, S.(2011).Do delays in expected loss recognition affect banks’ willingness to lend?.Journal of Accounting and Economics,52(1),1-20.
8. Bhat, G., Lee, J. A., & Ryan, S. G. (2014). Using loan loss indicators by loan type to sharpen the evaluation of the determinants and implications of banks’ loan loss accruals. Retrieved from https://ssrn.com/abstract=2490670. doi:10.2139/ssrn.2490670
9. Bhat, G., Ryan, S. G., & Vyas, D. (2013). The implications of credit risk modeling for banks’ loan loss provisions and loan-origination procyclicality. Retrieved from https://ssrn.com/abstract=1978409. doi:10.2139/ssrn.1978409
10. Bluhm, C.,Overbeck, L.,Wagner, C.(2010).Introduction to credit risk modeling.Boca Raton, FL:Chapman & Hall/CRC.
11. Bouvatier, V.,Lepetit, L.(2012).Effects of loan loss provisions on growth in bank lending: Some international comparisons.International Economics,132,91-116.
12. Bushman, R. M.,Williams, C. D.(2012).Accounting discretion, loan loss provisioning, and discipline of banks’ risk-taking.Journal of Accounting and Economics,54(1),1-18.
13. Callao, S.,Jarne, J. I.,Laínez, J. A.(2007).Adoption of IFRS in Spain: Effect on the comparability and relevance of financial reporting.Journal of International Accounting, Auditing and Taxation,16,148-178.
14. Chen, X.,Abebe, A.,Zhang, K.,Jahera, J. S., Jr.(2015).Country banking crisis prediction using transvariation analysis.Review of Pacific Basin Financial Markets and Policies,18,1550001.
15. Cohen, B. H.,Edwards, G, A.(2017).The new era of expected credit loss provisioning.BIS Quarterly Review,39-56.
16. Deutsche Bundesbank. (n.d.). Parameters (monthly and daily data). Retrieved from https://www.bundesbank.de/Navigation/EN/Statistics/Time_series_databases/Money_and_capital_markets/money_and_capital_markets_list_node.html?nsc=true&https=1&listId=www_skms_it03c
17. Domikowsky, C.,Bornemann, S.,Duellmann, K.,Pfingsten, A.(2014).Loan loss provisioning and procyclicality: Evidence from an expected loss model.Muenster, Germany:University of Muenster.
18. European Banking Authority. (2015). 2015 EU-wide transparency exercise. Retrieved from https://www.eba.europa.eu/risk-analysis-and-data/eu-wide-transparency-exercise/2015
19. European Banking Authority. (2016). Report on results from the EBA impact assessment of IFRS 9. Working paper, European Banking Authority, London, UK.
20. Fillat, J. L.,Montoriol-Garriga, J.(2010).,Boston, MA:Federal Reserve Bank of Boston.
21. Financial Stability Board. (2016). 2016 list of global systemically important banks (G-SIBs). Retrieved from http://www.fsb.org/2016/11/2016-list-of-global-systemically-important-banks-g-sibs/
22. Financial Stability Board. (2015). 2015 update of list of global systemically important banks (G-SIBs). Retrieved from http://www.fsb.org/2015/11/2015-update-of-list-of-global-systemically-important-banks-g-sibs/
23. Financial Stability Board. (2012). 2012 Update of list of global systemically important banks (G-SIBs). Retrieved from http://www.fsb.org/2012/11/r121031ac/
24. Financial Stability Board. (2011). Policy measures to address systemically important financial institutions. Retrieved from http://www.fsb.org/wp-content/uploads/r111104bb.pdf?page_moved=1
25. Financial Stability Board. (2014). 2014 Update of list of global systemically important banks (G-SIBs). Retrieved from http://www.fsb.org/2014/11/2014-update-of-list-of-global-systemically-important-banks/
26. Hill, P.,Brooks, R.,Faff, R.(2010).Variations in sovereign credit quality assessments across rating agencies.Journal of Banking & Finance,34,1327-1343.
27. Hodder, L. D.,Hopkins, P. E.,Wahlen, J. M.(2006).Risk-relevance of fairvalue income measures for commercial banks.The Accounting Review,81,337-375.
28. Hu, N.,Liu, L.,Zhu, L.(2018).Credit default swap spreads and annual report readability.Review of Quantitative Finance and Accounting,50,591-621.
29. International Accounting Standards Board. (2013). Financial instruments: Recognition and measurement. IAS 39. Retrieved from https://www.ifrs.org/login/?returnUrl=/issued-standards/list-of-standards/ias-39-financial-instruments-recognition-and-measurement/
30. International Accounting Standards Board. (2014a). Financial instruments. IFRS 9. Retrieved from https://www.ifrs.org/login/?returnUrl=/issued-standards/list-of-standards/ifrs-9-financial-instruments/
31. International Accounting Standards Board. (2014b). Project Summary. IFRS 9. Retrieved from https://www.ifrs.org/-/media/project/financial-instruments/project-summaries/ifrs-9-project-summary-july-2014.pdf
32. International Accounting Standards Board,Financial Accounting Standards Board(2009).Spanish Provisions under IFRS.Paper presented at the joint IASB-FASB meeting
33. International Swaps and Derivatives Association (2009). Standard North American corporate CDS converter specification. Retrieved from http://www.cdsmodel.com/cdsmodel/assets/cds-model/docs/ISDA%20Standard%20CDS%20Contract%20Converter%20Specification%20-%20Sept%204%2c%202009.pdf
34. International Swaps and Derivatives Association (2003). Standard North American corporate CDS contract specification. Retrieved from http://www.cdsmodel.com/cdsmodel/assets/cds-model/docs/Standard%20CDS%20Contract%20Specification.pdf
35. Jin, J. Y.,Kanagaretnam, K.,Lobo, G. J.,Mathieu, R.(2017).Social capital and bank stability.Journal of Financial Stability,32,99-114.
36. Kanagaretnam, K.,Lobo, G. J.,Mathieu, R.(2003).Managerial incentives for income smoothing through bank loan loss provisions.Review of Quantitative Finance and Accounting,20,63-80.
37. Kiesel, F.,Kolaric, S.(2018).Measuring the effect of watch-preceded and direct rating changes: A note on credit markets.Review of Quantitative Finance and Accounting,50,653-672.
38. Lemmon, M. L.,Roberts, M. R.,Zender, J. F.(2008).Back to the beginning: Persistence and the cross-section of corporate capital structure.The Journal of Finance,63,1575-1608.
39. Lobo, G. J.,Yang, D.-H.(2001).Bank managers’ heterogeneous decisions on discretionary loan loss provisions.Review of Quantitative Finance and Accounting,16,223-250.
40. Maung, M.,Chowdhury, R. H.(2014).Credit rating changes and leverage adjustments: Concurrent or continual?.Review of Pacific Basin Financial Markets and Policies,17,1450027.
41. Norden, L.(2017).Information in CDS spreads.Journal of Banking & Finance,75,118-135.
42. Novotny-Farkas, Z.(2015).The significance of IFRS 9 for financial stability and supervisory rules.Brussels, Belgium:European Parliament.
43. Pasiouras, F.,Gaganis, C.,Zopounidis, C.(2006).The impact of bank regulations, supervision, market structure, and bank characteristics on individual bank ratings: A cross-country analysis.Review of Quantitative Finance and Accounting,27,403-438.
44. Rathgeber, A.,Wang, Y.(2011).Market pricing of credit-linked notes: The case of retail structured products in Germany.Journal of Credit Risk,7,73-101.
45. Schich, S. T.(1997).Schätzung der deutschen Zinsstrukturkurve.Frankfurt, Germany:Deutsche Bundesbank.
46. Smith, L. D.,Jin, B.(2007).Modeling exposure to losses on automobile leases.Review of Quantitative Finance and Accounting,29,241-266.
47. Ziaei, S. M.(2017).Effects of financial soundness and openness on financial development.Review of Pacific Basin Financial Markets and Policies,20