Abstract
We model the evolution of stylised bank loan portfolios to assess the impact of IFRS 9 and US GAAP expected loss model (ECL) on the cyclicality of loan write-off losses, loan loss provisions (LLPs) and capital ratios of banks, relative to the incurred loss model of IAS 39. We focus on the interaction between the changes in LLPs charges (the flow channel) and stocks (the stock channel) under ECL. Our results show that, when GDP growth doesn’t demonstrate high volatility, ECL model smooths the impact of credit losses on profits and capital resources, reducing the pro-cyclicality of capital and leverage ratios, especially under US GAAP. However, when GDP growth is highly volatile, the large differences in lifetime probabilities of defaults (PDs) between booms and busts cause sharp increases in LLPs in deep downturns, as seen for US banks during the COVID-19 crisis. Volatile GDP growth makes capital and leverage ratios more pro-cyclical, with sharper falls in both ratios in deep downturns under US GAAP, compared to IAS 39. IFRS 9 ECL demonstrates less sensitivity to lifetime PDs fluctuations due to the existence of loan stages, and hence can reduce the pro-cyclicality of capital and leverage ratios, even when GDP is highly volatile.
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Source: 1Bank of England. Quarterly total of Monetary financial institutions sterling and all foreign currency provisions residents and non-residents (in sterling millions) not seasonally adjusted, available at: https://www.bankofengland.co.uk/boeapps/database/fromshowcolumns.asp?Travel=NIxAZxSUx&FromSeries=1&ToSeries=50&DAT=RNG&FD=1&FM=Jan&FY=1963&TD=31&TM=Dec&TY=2025&FNY=Y&CSVF=TT&html.x=66&html.y=26&SeriesCodes=GFQB6SS&UsingCodes=Y&Filter=N&title=GFQB6SS&VPD=Y. 2Federal Reserve Bank of St. Louis. Balance Sheet: Total Assets: Reserve for Losses, available at: https://fred.stlouisfed.org/series/QBPBSTASTLNLESSRES
Source: 1GDP data from ONS data series (Gross Domestic Product: chained volume measures: Seasonally adjusted; available at: https://www.ons.gov.uk/economy/grossdomesticproductgdp/timeseries/abmi/pn2. 2 Bank rate and effective mortgage rate data till 2016 from the spreadsheet “a-millennium-of-macroeconomic-data-for-the-uk” available at: https://www.bankofengland.co.uk/statistics/research-datasets, from the worksheet “A31. Interest rates & asset ps” (Bank Rate in column B and Effective Mortgage Rate in column L). The reminder is sourced from the Bank of England’s databases
Source: Bank of England regulatory returns. See notes to Table 1 for further details
Source: Bank of England regulatory returns
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Notes
International Financial Reporting Standard 9 Financial Instruments. See International Accounting Standards Board (2013).
Current Expected Credit Losses (CECL) standards under US Generally Accepted Accounting Principles (US GAAP—Accounting Standards Code Topic 326). See Financial Accounting Standards Board (2016).
International Accounting Standard 39 Financial Instruments: Recognition and Measurement.
Financial Accounting Standard No 5: Accounting for Contingencies (ASC 450-20) and related standards under US GAAP, codified by the FASB.
Write-off losses are equal to gross carrying amount of a defaulted loan minus LLPs for that loan.
Financial Accounting Standards Board’s standard number 5 and standard number 114.
Throughout the paper, LLPs refer to LLPs expenses. We refer to the reserves on the balance sheet as LLPs stock.
(see Sect. 5.3 for a critical discussion on the implications of PDs sensitivity to GDP on the impact of both IFRS 9 and US GAAP on the cyclicality of the variables we simulate)
Leverage ratio here is the regulatory leverage ratio, which is calculated by divding capital resources on the leverage ratio exposure measure. This exposure measure considers not only on-balance sheet assets, but also off-balance sheet exposures such as guarantees, unused credit facilities, and derivatives. See the Basel Consolidated Framework on the Bank for International Settlements‘ website for more details.
Banks in our model do not have any off-balance sheet exposures. Hence, their total leverage exposures would be equal to total assets (sum of net loan balances).
BOE holds loan-level data, but it does not include all the details needed to calculate LLPs under IFRS 9, US GAAP and IAS 39 (especially life time/12-months PDs and PDs at origination of the loans). Hence, to have a meaningful comparison between the three standards, we need to make sure the underlying loan books are the same, and having hypothetical loan portfolios allows us to do so. Yet, despite being hypothetical, loan portfolios have been generated based on actual data.
It is important to note that there are multiple alternative ways to define cyclicality or pro-cyclicality. For instance, ESRB (2019) defines it as “the mutually reinforcing (‘positive feedback’) mechanisms through which the financial system can amplify business fluctuations and possibly cause or exacerbate financial instability”. We used correlation with GDP as it is simpler to implement in our simulation, and hence our results are based on this definition.
If this term is large enough, then increases in LLPs would increase CET1 resources rather than decreasing them.
In our setup, GDP growth follows the exact same patterns every 50 years (when GDP volatility is low) or every 71 years (when GDP volatility is high). Banks incorporate these patterns when they use the cycle-based setup. For example, if GDP is not volatile, the GDP growth a bank following the cycle-based setup uses to create the baseline scenario would be the average of GDP growth of years − 49, − 48 and − 47. Mathematically,\(E\left[{\Delta GDP}_{t+1}\right]=\frac{1}{3}\sum_{i=1}^{3}\Delta {GDP}_{t-CY+i}\), where CY is cycle length.
It is worth noting that changing composition of banks’ population makes our results less sensitive to the scenario probabilities used. We tried other assumptions about scenarios probabilities under different expectation strategies, and results are robust. Tables are omitted for conciseness, and are available from the authors upon request.
We assume no banks do not fail. We also assume banks maintain ratios only by adjusting capital, and do not ration lending.
This is a simplification of reality, as banks would try to maintain healthy capital ratios, to avoid regulatory (distribution restrictions) or market reactions. The Basel III Accords introduced a 3% minimum Tier 1 leverage ratio, and supplemented it with a buffer for globally systematically important banks (GSIB), set to 50% of the equivalent buffer in the risk-based framework. The UK leverage ratio sets the minimum leverage ratio at 3.25%, but exempt central bank reserves from the calculation. It also sets the GSIB leverage ratio buffer at 35% of the risk-based buffer, and introduces a counter-cyclical leverage ratio buffer set at 35% of the counter-cyclical buffer (CCyB). The UK leverage ratio also includes capital quality restriction, under which 75% of minimum and 100% of buffer requirements have to be met in CET1 capital. At the time, the average UK leverage requirements was 3.69%. Additionally, banks tend to hold some “voluntary” buffers, for a number of reasons (e.g. to avoid potential breach of requirements, or to avoid failing stress tests). We think this provides enough support for our assumption for a conservative capital management, and is in line with leverage ratios data reported in Table 1.
System-wide LLPs and net credit losses are the sum across banks, whereas system-wide capital and leverage ratios are the weighted averages of individual bank ratios (using RWAs and LEs as weights, respectively).
Negative values in LLPs charts indicate reductions in LLPs stock, as the preceding year’s stock exceed LLPs needed in the current year.
On 26 March 2020, BOE provided guidance on IFRS 9 implementation in a letter to the CEOs of UK banks. A further guidance to banks was provided in another letter sent to bank CEOs on 4 June 2020.
On 1 April 2020, the ECB sent a letter to banks under its supervision on IFRS 9 implementation during COVID-19 pandemic:
Regulatory Capital Rule: Revised Transition of the Current Expected Credit Losses Methodology for Allowances (on 31 March 2020).
Measures to reflect the impact of Covid-19 (on 03 April 2020).
Application of IFRS 9 in the light of the coronavirus uncertainty (on 27 March 2020).
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Appendix: Details on out-of-the-sample predictions
Appendix: Details on out-of-the-sample predictions
For this exercise, we fit an extreme values distribution to the low-volatility and high-volatility GDP growth series, and use it to generate synthetic GDP growth series. These synthetic low-volatility and high-volatility series are presented in Fig.
Simulated GDP growth series
18.
For interest rates, we estimate the polynomial (cubic) relationship between each of the interest rates and GDP growth series, for the low-volatility and high-volatility scenarios separately. The estimated models for the low-volatility scenario are as follows:
Meanwhile, the estimated models for the high-volatility scenario are:
We rerun our simulations using GDP growth and interest rate series. Results are shown in Figs.
System-wide LLPs under IAS 39, IFRS 9 and US GAAP—low GDP volatility—out-of-the-sample analysis
19,
System-wide write-off losses under IAS 39, IFRS 9 and US GAAP—low GDP volatility—out-of-the-sample analysis
20,
System-wide capital ratio under IAS 39, IFRS 9 and US GAAP—low GDP volatility—out-of-the-sample analysis
21,
System-wide leverage ratio under IAS 39, IFRS 9 and US GAAP—low GDP volatility—out-of-the-sample analysis
22 and Figs.
System-wide LLPs under IAS 39, IFRS 9 and US GAAP—high GDP volatility—out-of-the-sample analysis
23,
System-wide write-off losses under IAS 39, IFRS 9 and US GAAP—high GDP volatility—out-of-the-sample analysis
24,
System-wide capital ratio under IAS 39, IFRS 9 and US GAAP—high GDP volatility—out-of-the-sample analysis
25,
System-wide leverage ratio under IAS 39, IFRS 9 and US GAAP—high GDP volatility—out-of-the-sample analysis
26 for the low-volatility and high-volatility scenarios, respectively.
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Fatouh, M., Giansante, S. The cyclicality of bank credit losses and capital ratios under expected loss model. Ann Oper Res 330, 807–840 (2023). https://doi.org/10.1007/s10479-023-05445-9
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DOI: https://doi.org/10.1007/s10479-023-05445-9
Keywords
- IFRS 9
- IAS 39
- US GAAP
- Expected credit loss model
- Loan loss provisions
- Cyclicality of bank profits
- Leverage ratio
- Risk-weighted assets