1. bookVolumen 15 (2021): Heft 1 (December 2021)
Konferenz Details
License
Format
Konferenz
eISSN
2558-9652
Erstveröffentlichung
15 Dec 2017
Erscheinungsweise
1 Hefte pro Jahr
Sprachen
Englisch
access type Uneingeschränkter Zugang

A methodological approach to developing and validating IFRS 9 -LGD parameters

Online veröffentlicht: 31 Dec 2021
Volumen & Heft: Volumen 15 (2021) - Heft 1 (December 2021)
Seitenbereich: 683 - 694
Konferenz Details
License
Format
Konferenz
eISSN
2558-9652
Erstveröffentlichung
15 Dec 2017
Erscheinungsweise
1 Hefte pro Jahr
Sprachen
Englisch
Abstract

Since the introduction of the advanced internal rating based approach through the Basel framework, financial institutions and regulators have been dealing with the increased complexity of Loss Given Default models. The development and validation of the parameters has become more formalized and standardized as more prescriptive regulations and guidelines have been published by the European Parliament, European Central Bank and European Banking Authority. Furthermore, following the introduction of IFRS 9 in January 2018 even more emphasis is put on the development and validation as the standard poses new challenges to the way models are designed, developed, validated and increased complexity through the introduction of the lifetime and forward-looking adjustments. This paper address the challenges faced by banks and regulators when assessing and validation the IFRS 9 - Loss Given Default parameters and framework. Moreover, it describes a non-exhaustive list of tests that can be performed to establishing the accuracy, discrimination power and stability of the Loss Given Default outputs. It is important that the framework built around the modeling, development and validation process allows models to be easily integrated in the management framework in a flexible manner that can facilitate any changes that must be brought to the models. Hence, this paper outlines a non-exhaustive list of quantitative validation tests considered would satisfy the requirements of the IFRS 9 standard.

Azzaz, J., Loisel, S., & Therond, P. E. (2015). Some characteristics of an equity security next-year impairment, Review of Quantitative Finance and Accounting, 45, 111-135.10.1007/s11156-014-0432-x Search in Google Scholar

Barth, M. E., & Landsman, W. R. (2010). How did financial reporting contribute to the financial crisis?, European Accounting Review, 19(3), 399-423.10.1080/09638180.2010.498619 Search in Google Scholar

Basel Committee on Banking Supervision (1988). International Convergence of Capital Measurement and Capital Standards, https://www.bis.org/publ/bcbs04a.htm. Search in Google Scholar

Basel Committee on Banking Supervision (2005). International Convergence of Capital Measurement and Capital Standards: A Revised Framework, BIS, Updated November 2005, https://www.bis.org/publ/bcbs107.htm. Search in Google Scholar

Basel Committee on Banking Supervision (2005a). Validation, Newsletter No. 4., https://www.bis.org/publ/bcbs_nl4.htm. Search in Google Scholar

Basel committee on banking supervision (2015). Guidance on credit risk and accounting for expected credit losses, https://www.bis.org/bcbs/publ/d350.pdf. Search in Google Scholar

Bischof, J., Daske, H. (2016). Interpreting the European Union’s IFRS endorsement criteria: the case of IFRS 9, Accounting in Europe, 13(2), 129-168.10.1080/17449480.2016.1210181 Search in Google Scholar

Bolton, P., Freixas, X., & Shapiro, J. (2012). The credit ratings game, The Journal of Finance, 67, 85-111.10.1111/j.1540-6261.2011.01708.x Search in Google Scholar

Buchanan, M. (2009). Why money messes with your mind, New Scientist. Search in Google Scholar

Carlehed, M., & Petrov, A. (2012). A methodology for point-in-time-through-the-cycle probability of default decomposition in risk classification systems, Journal of Risk Model Validation, 6(3), 3-25.10.21314/JRMV.2012.091 Search in Google Scholar

Cheng, M., & Neamtiu, M. (2009). An empirical analysis of changes in credit rating properties: Timeliness, accuracy and volatility, Journal of Accounting and Economics, 47, 108-130.10.1016/j.jacceco.2008.11.001 Search in Google Scholar

Edwards, G.A. (2016). Supervisors’ key roles as banks implement expected credit loss provisioning SEACEN, Financial Stability Journal, 7(1), 1-25. Search in Google Scholar

European Banking Authority (2017). Guidelines on credit institutions’ credit risk management practices and accounting for expected credit losses. Search in Google Scholar

European Banking Authority (2017). Guidelines on PD estimation, LGD estimation and the treatment of defaulted exposures. Search in Google Scholar

European Banking Authority (2016). Guidelines on the application of the definition of default under Article 178 of Regulation (EU) No 575/2013. Search in Google Scholar

Gaurav, C., Forest, Jr. L. R., & 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. Search in Google Scholar

Global Public Policy Committee (2016). The implementation of IFRS 9 impairment requirements by banks. Search in Google Scholar

International accounting standards Board (2014). International Financial reporting standard 9 Financial instruments. Search in Google Scholar

Lando, D. (1998). On cox processes and credit risky securities, Review of Derivatives research, 2, 99-120.10.1007/BF01531332 Search in Google Scholar

Magnan, M., & Markarian, G. (2011). Accounting, governance and the crisis: is risk the missing link?, European Accounting Review, 20, 215-231.10.1080/09638180.2011.580943 Search in Google Scholar

Morgenson, G. (2008). Debt watchdogs: Tamed or caught napping?. New York Times. Search in Google Scholar

Pool, S., De Haan, L., Jacobs, J.P. (2015). Loan loss provisioning, bank credit and the real economy, Journal of Macroeconomics, 45(1), 124-136.10.1016/j.jmacro.2015.04.006 Search in Google Scholar

Reitgruber, W. (2015). Methodological thoughts on expected loss estimation for IFRS 9 impairment: hidden reserves, cyclical loss predictions and LGD back testing, Credit Technology by Serasa Experian, 92, 7-29. Search in Google Scholar

Temim, J. (2016). The IFRS 9 impairment model and its interaction with the Basel framework, Moody’s Analytics Risk Perspectives, 8(1), 13-33. Search in Google Scholar

Weij, W., & Hollander, M. (2009). Improving PD and LGD models: following the changes in the market, SNS Real, Utrecht. Search in Google Scholar

Yang, B.H. (2017). Point-in-time PD term structure models for multi-period scenario loss projection: methodologies and implementations for IFRS 9 ECL and CCAR stress testing, Journal of Risk Model Validation, 11(3), 1-17. Search in Google Scholar

Yang, B. H. & Tkachenko, M. (2012). Modeling exposure at default and loss given default: empirical approaches and technical implementation, The Journal of Credit Risk, 8(2), 81-102.10.21314/JCR.2012.139 Search in Google Scholar

Empfohlene Artikel von Trend MD

Planen Sie Ihre Fernkonferenz mit Scienceendo