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The Governance and Disclosure of IFRS 9 Economic Scenarios

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  • Yolanda S. Stander

    (School of Accounting, College of Business & Economics, University of Johannesburg, P.O. Box 524, Auckland Park, Johannesburg 2006, South Africa)

Abstract

Extraordinary economic conditions during the COVID-19 pandemic caused many IFRS 9 impairment models to produce unreliable results. Severe market reactions, resulting from unprecedented events, prompted swift action from the regulatory authorities to maintain the financial system’s stability. Banks managed the uncertainty and volatility in the models with expert overlays, increasing the risk of biased outcomes. This study examines new ways of enhancing the governance and transparency of the IFRS 9 economic scenarios within banks and suggests additional financial disclosures. Benchmarking is proposed as a useful tool to evaluate the IFRS 9 economic scenarios and ensure effective challenge as part of a model risk governance framework. Archimedean copulas are used to generate objective economic benchmarks. Ideas around benchmarking are illustrated for a set of South African economic variables, and the outcomes are compared to the IFRS 9 scenarios published by the six biggest South African banks in their annual financial statements during the pandemic.

Suggested Citation

  • Yolanda S. Stander, 2023. "The Governance and Disclosure of IFRS 9 Economic Scenarios," JRFM, MDPI, vol. 16(1), pages 1-27, January.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:1:p:47-:d:1033854
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    1. Carriero, Andrea & Galvão, Ana Beatriz & Kapetanios, George, 2019. "A comprehensive evaluation of macroeconomic forecasting methods," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1226-1239.
    2. Marek Chudý & Erhard Reschenhofer, 2019. "Macroeconomic Forecasting with Factor-Augmented Adjusted Band Regression," Econometrics, MDPI, vol. 7(4), pages 1-14, December.
    3. Kenichiro McAlinn & Knut Are Aastveit & Jouchi Nakajima & Mike West, 2020. "Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(531), pages 1092-1110, July.
    4. Montero-Manso, Pablo & Athanasopoulos, George & Hyndman, Rob J. & Talagala, Thiyanga S., 2020. "FFORMA: Feature-based forecast model averaging," International Journal of Forecasting, Elsevier, vol. 36(1), pages 86-92.
    5. Clive Granger & Namwon Hyung & Yongil Jeon, 2001. "Spurious regressions with stationary series," Applied Economics, Taylor & Francis Journals, vol. 33(7), pages 899-904.
    6. Odunayo Magret Olarewaju, 2020. "Investigating the factors affecting nonperforming loans in commercial banks: The case of African lower middle‐income countries," African Development Review, African Development Bank, vol. 32(4), pages 744-757, December.
    7. Rémillard, Bruno & Papageorgiou, Nicolas & Soustra, Frédéric, 2012. "Copula-based semiparametric models for multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 30-42.
    8. Ines Fortin & Sebastian P. Koch & Klaus Weyerstrass, 2020. "Evaluation of economic forecasts for Austria," Empirical Economics, Springer, vol. 58(1), pages 107-137, January.
    9. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    10. Kenny, Geoff & Morgan, Julian, 2011. "Some lessons from the financial crisis for the economic analysis," Occasional Paper Series 130, European Central Bank.
    11. Aldrich, J., 1995. "Correlations genuine and spurious in Pearson and Yule," Discussion Paper Series In Economics And Econometrics 9502, Economics Division, School of Social Sciences, University of Southampton.
    12. Diebold, Francis X. & Schorfheide, Frank & Shin, Minchul, 2017. "Real-time forecast evaluation of DSGE models with stochastic volatility," Journal of Econometrics, Elsevier, vol. 201(2), pages 322-332.
    13. D. Hodge, 2006. "Inflation and growth in South Africa," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 30(2), pages 163-180, March.
    14. David F. Hendry & Katarina Juselius, 2001. "Explaining Cointegration Analysis: Part II," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 75-120.
    15. Kenny, Geoff & Morgan, Julian, 2011. "Some lessons from the financial crisis for the economic analysis," Occasional Paper Series 130, European Central Bank.
    16. Narman Kuzucu & Serpil Kuzucu, 2019. "What Drives Non-Performing Loans? Evidence from Emerging and Advanced Economies during Pre- and Post-Global Financial Crisis," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(8), pages 1694-1708, June.
    17. Enzo Scannella & Salvatore Polizzi, 2021. "How to measure bank credit risk disclosure? Testing a new methodological approach based on the content analysis framework," Journal of Banking Regulation, Palgrave Macmillan, vol. 22(1), pages 73-95, March.
    18. Ms. Mwanza Nkusu, 2011. "Nonperforming Loans and Macrofinancial Vulnerabilities in Advanced Economies," IMF Working Papers 2011/161, International Monetary Fund.
    19. Żelazowski Konrad, 2017. "Housing Market Cycles In The Context Of Business Cycles," Real Estate Management and Valuation, Sciendo, vol. 25(3), pages 5-14, September.
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