PRO-CYCLICALITY IMPLICATIONS OF IFRS9 AND … number Title slide Slide number Title slide ... • To...

PRO-CYCLICALITY IMPLICATIONS OF IFRS9 AND THE RWA FRAMEWORK

Brad Carr, Senior Director, Regulatory Affairs

Jonathan Ng, Policy Advisor, Regulatory Affairs

Hassan Haddou, Policy Advisor, Regulatory Affairs

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Slide number

Title slideSlide number

Title slide

3 Executive summary 13 Bank 2A – retail

4 - 5 This analysis 14 Bank 2B – diversified

6 Section 1 – IRB banks 15 Detailed impact analysis

7 Bank 1A – retail 16Section 3 – observations and conclusions

8 Bank 1B – whole 17 – 18 Observations and conclusions

9 Bank 1C – cyclical wholesale 19 Section 4 – appendix

10 Bank 1D – diversified 20 – 22 Methodology and assumptions

11 Detailed impact analysis 23Reg EL shortfall sensitivity analysis

12 Section 2 – SA banks 24 Other studies

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• At the 2009 G20 Pittsburgh Summit1 pro-cyclicality was identified as a regulatory priority. Some initiatives have attempted to address this (CCyB and stress testing)

• The introduction of IFRS 9 (based on expected loss (EL)), in conjunction with the current risk-weighted asset (RWA) framework in a downturn period, may have unintended implications, in particular greater capital volatility

• Based on our IRB and SA bank stress scenario, IFRS9 provisioning can erode CET1 between ~70-120 bps, while RWA inflation can erode CET1 between ~40-150 bps. When aggregating both impacts, this has a doubling effect in CET1 erosion

• The absence of capital relief for holding a higher level of provisions brings additional pressure to banks’ CET1 ratios in a stressed environment

• A key consequence will likely be a tightening in credit availability, which prolongs the downturn and this feedback loop would have procyclical effects

• In addition, a new statement in the Basel III standards suggests ratings for RWA may become even more procyclical than the current framework, which is beyond the modeling performed in this analysis2

• Policy makers are urged to consider the loss-absorption capacity of the additional provisions in the capital framework

• In this analysis, a key assumption made is the classification of Stage 2 loans (see slide 20 – 22 for more detail). We note that individual banks will have different assumptions, in particular “staging” of loans, which will produce different impacts

1. Leaders’ statement of the Pittsburgh Summit September, 25th – 29th 2009, page 82. “Ratings systems should be designed in such a way that idiosyncratic or industry-specific changes are a driver of migrations from one category to another, and business cycle effects may also be a driver” Basel III: Finalizing post-crisis reforms, December 2017, page 86

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• To illustratively demonstrate the pro-cyclicality implications of the new accounting requirements compounded with the current RWA framework during a downturn, we have modelled the CET1 capital ratio impacts for:

• IFRS 9 estimates in a benign (e.g. current) environment; and

• IFRS 9 and RWA inflation estimates in a stressed environment

across different bank balance sheet scenarios, under IRB and SA approaches

• The IIF understands there are other areas in the regulatory framework that may have pro-cyclical implications (e.g. initial margin in derivatives), but have focused on the two major frameworks (i.e. IFRS 9 and RWA) to demonstrate the potential impacts on banks

• The model is based on several simplified assumptions (e.g. PD doubling stress scenario, “staging” of loans, calculation of lifetime PDs and constant Reg EL to accounting provisions shortfall) to estimate the ‘Day 1’ and stressed CET1 impacts

• Note that the model is not intended to precisely estimate CET1 impacts of IFRS 9 for banks, but to:

• provide an illustrative example of potential CET1 impacts on our hypothetical bank balance sheets; and

• highlight the implications of having higher capital volatility for banks

5

• The model utilizes IRB credit risk data across a sample of banks’ Pillar 3 disclosures as a baseline to derive average PDs, LGDs, EAD and RWA density across the major Basel asset classes1,2

• The model assumes a severe downturn scenario for:

• EL provisioning to be approximately a 100% increase in PDs across all asset classes to estimate PIT PDs

• the RWA framework to be approximately a 75% increase in PDs across all asset classes to estimate a hybrid (PIT and TTC) approach3

• For more detail on our methodology and assumptions refer to the appendix (slides 20 - 22)

• The IIF intends to complete similar analysis on the CECL accounting requirements as a subsequent addendum in due course

1. The sample of banks used are six major banks from Europe, Canada and Australia2. Major Basel asset classes are defined as residential mortgages, credit cards, other retail, corporates, sovereigns and financial institutions3. Note that exposures are assumed to migrate commensurately to their scaled PD and therefore may be assigned higher risk weights

Section 1 – IRB banks

• Bank 1A is predominately retail focused with 65% of its exposures in mortgages and 20% in credit cards and other retail

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• Day 1 CET1 impact of IFRS 9 is 80 bps because of long tenor mortgages

• Incremental CET1 impact stressing the IFRS 9 provisioning is 120 bps (i.e. total of 200bps)

• In conjunction with RWA inflation during a stress, the CET1 impact is 270 bps

11.2%12.0%

(80 bps)

0%

2%

4%

6%

8%

10%

12%

14%

CET1 (pre IFRS9impact)

IFRS9 impact CET1 (post IFRS9impact)

Bank 1A (Retail focused)

8.5%

12.0%

(150 bps)

(200 bps)

0%

2%

4%

6%

8%

10%

12%

14%

CET1 (prestress impact)

RWA inflation IFRS9 impact CET1 (postIFRS9 impact)

Bank 1A (Retail focused) - Stressed environment

• Bank 1B is predominately wholesale focused with 50% of its exposures in corporates, 20% in sovereigns and 15% financial institutions

8

• Day 1 CET1 impact of IFRS 9 is 30 bps


• In conjunction with RWA inflation during a stress the CET1 impact is 160 bps

11.7%12.0%

(30 bps)

0%

2%

4%

6%

8%

10%

12%

14%



Bank 1B (Wholesale focused)

10.1%

12.0%

(90 bps)

(100 bps)

0%

2%

4%

6%

8%

10%

12%

14%



Bank 1B (Wholesale focused) - Stressed environment

• Bank 1C has the same portfolio composition as Bank 1B, however all of its corporate exposures are with specialized lending activities (income producing commercial real estate)1

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11.7%12.0%

(30 bps)

0%

2%

4%

6%

8%

10%

12%

14%



Bank 1C (Cyclical wholesale focused)

9.4%

12.0%

(150 bps)

(110 bps)

0%

2%

4%

6%

8%

10%

12%

14%



Bank 1C (Cyclical wholesale focused) -Stressed environment

1. The SL-IPRE calculations are on the assumption that RWA is IRB-modeled and not under the Slotting Criteria

• Bank 1D has a diversified portfolio with 48% of exposures in retail and 52% of exposures in non-retail

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11.5%12.0%

(50 bps)

0%

2%

4%

6%

8%

10%

12%

14%



Bank 1D (Diversified)

9.6%

12.0%

(90 bps)(150 bps)

0%

2%

4%

6%

8%

10%

12%

14%



Bank 1D (Diversified) - Stressed environment

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BanksCET1 (after Reg EL adjustment)1,2 ($m)

IFRS 9 impact3 ($m)CET1 (post IFRS 9

impact ($m)4RWA ($m)

Bank 1A – Base case 42,622 (2,867) 39,755 355,180

Bank 1A – stressed environment 42,622 (8,221) 34,401 406,007

Change - (5,354) (5,354) 50,827

Bank 1B – Base case 50,748 (1,154) 49,594 422,899

Bank 1B – stressed environment 50,748 (4,521) 46,227 458,955

Change - (3,367) (3,367) 36,056

Bank 1C – Base case 52,093 (1,401) 50,692 434,109

Bank 1C – stressed environment 52,093 (5,280) 46,813 498,181

Change - (3,879) (3,879) 64,072

Bank 1D – Base case stress 47,129 (2,100) 45,029 392,740

Bank 1D – stressed environment 47,129 (6,449) 40,680 422,991

Change - (4,349) (4,349) 30,251

1. All banks are assumed to have a $2,000m regulatory EL shortfall to accounting provisions (“shortfall”). We note the shortfall varies with each individual bank and in some recent examples (i.e. Canadian banks) has absorbed most of the IFRS 9 CET1 impacts. In the appendix (slide 23) we have provided sensitivity analysis as to how the shortfall changes the day 1 IFRS 9 CET1 impact of our hypothetical banks. We further note that the shortfall will also change for individual banks in a stressed period, but have kept this constant for comparability across the benign and stressed scenarios.2. All banks’ CET1($) starting point are back solved to achieve a 12% CET1 target ratio3. IFRS 9 impact is the $ impact on CET1. The IFRS 9 impact is the increase in provisioning from the incurred loss approach and in excess of the regulatory EL to accounting provisions4. All banks are assumed to already exceed their deferred tax asset (DTA) threshold (10% of CET1). As such, the additional DTA created by the higher provisions will be fully deducted from CET1 (i.e. neutralize any benefits from additional DTA on CET1 ratios).

• Detailed dollar breakdown of: (1) Day 1 CET1 impact of IFRS 9, (2) IFRS 9 provisioning in stress and (3) RWA inflation in stress

Section 2 – SA banks

• Bank 2A is predominately retail focused with 70% of its exposures in mortgages2, 20% in other retail, 5% in SME corporates and 5% in sovereigns

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• Incremental CET1 impact stressing the IFRS 9 provisioning is 80 bps (i.e. total 160bps)

• In conjunction with RWA inflation during a stress the CET1 impact is 140 bps1. Only two bank balance sheet scenarios are presented given that the balance sheets commonly observed in SA banks are akin to 2A and 2B examples2. Approximately 70% of the mortgage exposures have a LTV ratios less than 80%; under stressed conditions, it is assumed that this proportion drops to 50%

11.2%12.0%

(80 bps)

0%

2%

4%

6%

8%

10%

12%

14%



Bank 2A (Retail focused)

9.8%

12.0%

(60 bps)

(160 bps)

0%

2%

4%

6%

8%

10%

12%

14%



Bank 2A (Retail focused) - Stressed environment

• Bank 2B has a diversified portfolio with 30% of its exposures in mortgages2, 20% in other retail, 20% of exposures in SME corporates, and 20% in rated corporates and 10% of sovereigns.

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• Incremental CET1 impact stressing the IFRS 9 provisioning is 80 bps (i.e. total 150bps)

• In conjunction with RWA inflation during a stress the CET1 impact is 120 bps1. Only two bank balance sheet scenarios are presented, given that the balance sheets commonly observed in SA banks are akin to 2A and 2B examples. For comparison to our other scenarios, the 2B example relatively aligns to 1D2. Approximately 70% of the mortgage exposures have a LTV ratios less than 80%; under stressed conditions, it is assumed that this proportion drops to 50%

11.3%12.0%

(70 bps)

0%

2%

4%

6%

8%

10%

12%

14%



Bank 2B (Diversified)

10.1%

12.0%

(40 bps)(150 bps)

0%

2%

4%

6%

8%

10%

12%

14%



Bank 2B (Diversified) - Stressed environment

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• Detailed dollar breakdown of: (1) Day 1 CET1 impact of IFRS 9, (2) IFRS 9 provisioning in stress and (3) RWA inflation in stress

1. IFRS 9 impact is the $ impact on CET1. The IFRS 9 impact is the increase in provisioning from the incurred loss approach and in excess of the regulatory EL to accounting provisions

Banks CET11($m)IFRS 9

impact ($m)

CET1 (post IFRS 9

impact ($m)RWA ($m)

Bank 2A – Base case 73,934 (5,042) 68,892 616,119

Bank 2A – stressed environment 73,934 (10,585) 63,349 647,901Change - (5,543) (5,543) 31,782Bank 2B – Base case 92,425 (5,693) 86,732 770,211

Bank 2B – stressed environment 92,425 (11,776) 80,649 795,368

Change - (6,083) (6,083) 25,157

Section 3 – Observations and conclusions

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IRB Banks

• In summary, the estimated results show that:

• Day 1 CET1 impacts: ranges between 30-80bps

• Incremental CET1 impact of stressing the provisioning: ranges between 70-120 bps

• Combined with RWA inflation, the CET1 erosion can range between 190-350 bps under severe conditions

SA Banks

• In summary, the estimated results show that:

• Day 1 CET1 impacts: ranges between 70-80bps

• Incremental CET1 impact of stressing the provisioning: ~80 bps

• Combined with the RWA inflation, the CET1 erosion can range between 190-220 bps under severe conditions

• CET1 impacts for IRB and SA banks are more severe for retail focused banks in comparison to wholesale focused banks due to the longer maturities of retail products

• If rating systems in RWA become fully subject to business cycles (per the final Basel III standards1), then we would have to revise our PD assumption up from 75% to 100%, further compounding these impacts

1. “Ratings systems should be designed in such a way that idiosyncratic or industry-specific changes are a driver of migrations from one category to another, and business cycle effects may also be a driver” Basel III: Finalizing post-crisis reforms, December 2017, page 86

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• In the event of a downturn, regulators may relax capital buffers (for e.g. CCyB) and/or regulatory minima, however this may give limited confidence to the market and its CET1 expectations for banks

• Impacts of this magnitude on CET1 ratios (even if still complying with regulatory minima) include potential downgrades, increasing banks’ funding cost and tightening credit availability in order to reduce RWA

• For example, if Bank 1A’s CET1 minimum is 12.0% and during the downturn its CET1 ratio falls to 8.5%, regulators may relax the minimum to 8%. However, the market and its investors would be more concerned that its CET1 ratios eroded by ~350 bps, despite being compliant with regulatory requirements

• What considerations need to be made by policy makers?

• Review the treatment of the new provisioning requirements under the capital framework

o neutralization and/or addback to CET1 for the additional provisioning amounts in times of stress; and

o review the appropriateness of the current DTA threshold limit

• Further review the stability of the RWA framework – continuation of TTC versus PIT nature of the RWA framework, which builds on the work the EBA and the IIF RWA Task Force has conducted

Section 4 - Appendix

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DATA• The model uses IRB credit risk data from a sample of banks’ Pillar 3 disclosures to

derive a PD band matrix across the major Basel asset classes1,2

• Within each PD band of each Basel asset class, an average PD, LGD, EAD and RWA density is derived using the bank sample data

• PD bands are categorized using the standardized EBA PD bands

• Estimated maturity profile is derived from available bank sample disclosures

• Effective interest rates used for discounting EL in each asset class are based on approximated market rates

KEY ASSUMPTIONS – IRB• All banks are assumed to have a constant regulatory EL shortfall to accounting

provisions

• All banks’ starting point is assumed to already exceed their deferred tax asset (DTA) threshold (10% of CET1)

• All banks are assumed to have the same and constant level of other risk RWA (i.e. operational and market risk)

• All banks are assumed to have a target CET1 capital ratio of 12%

• Maturity profile is consistent across the bank balance sheet scenarios

1. The sample of banks used are six major banks from Europe, Canada and Australia2. Major Basel asset classes are defined as residential mortgages, credit cards, other retail, corporates, sovereigns and financial institutions

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• The maximum residual maturity is 5 years for all exposures with the exception of mortgages, which assumes the majority of exposures are between 5 years and 10 years

• The PD band matrix is constant across the case studies

• All exposures with PDs less than 2.5% is assumed to be Stage 1, all exposures with PDs greater than 2.5% and less than 100% is assumed to be Stage 2 and exposures with PDs equal to 100% is assumed to be Stage 3

• Lifetime PDs are assumed to have a marginal linear increase of 20% from 1yr PDs

• Cyclical wholesale bank balance sheet scenarios assumes corporate PDs are that of corporate IPREs PDs

• The model uses regulatory LGDs for calculating EL

• Discount factors (based on the asset class’ effective interest rate) are used to calculate EL for stage 1, stage 2 exposures (lifetime exposures)

• The model assumes a severe downturn scenario for:

• EL provisioning to be approximately a 100% increase in PDs across all asset classes to estimate PIT PDs. Thus a scaling factor of 2 is applied to the base case PDs

• RWA framework to be approximately a 75% increase in PDs across all asset classes to estimate a hybrid PD (PIT and TTC). Thus a scaling factor of 1.75 is applied to the base case PDs1

1. Note that exposures are assumed to migrate down the credit curve in line with their assigned average PD

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KEY ASSUMPTIONS – SA

• For each asset class, SA and IRB have similar profiles in terms of credit quality and maturity

• The model applies for each exposure the appropriate SA risk-weight (current Basel rules)

• For a given exposure, IFRS 9 calculations are assumed to be identical for SA and IRB banks

• Regulatory EL shortfall does not apply to SA banks

• All banks’ starting point is assumed to already exceed their deferred tax asset (DTA) threshold (10% of CET1)

• The model assumes a severe downturn scenario consistent with the one used for IRB banks

• The model assumes that under stressed conditions, the proportion of mortgages with a LTV less than 80% drops from 70% to 50% of the overall portfolio

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Banks

CET1 (pre Reg EL

adjustment) ($)

Reg EL shortfall ($)

IFRS 9 impact ($)

CET1 (post Reg EL

adjustment and IFRS 9 impact ($)

RWA ($) CET1 (%)CET1 change

from base case (bps)

Bank 1A – Base case 44,622 (2,000) (2,867) 39,755 355,180 11.2% -

Bank 1A – no Reg EL shortfall 42,622 0 (4,867) 37,755 355,180 10.6% (60)

Bank 1A – higher Reg EL shortfall

46,622 (4,000) (867) 41,755 355,180 11.8% 60

Bank 1B – Base case 52,748 (2,000) (1,154) 49,594 422,899 11.7% -

Bank 1B – no Reg EL shortfall 50,748 0 (3,154) 47,594 422,899 11.3% (40)

Bank 1B – higher Reg EL shortfall

54,748 (4,000) (846) 49,902 422,899 11.8% 10

Bank 1C – Base case 54,093 (2,000) (1,401) 50,692 434,109 11.7% -

Bank 1C – no Reg EL shortfall 52,093 0 (3,401) 48,692 434,109 11.2% (50)

Bank 1C – higher Reg EL shortfall

56,093 (4,000) (599) 51,494 434,109 11.9% 20

Bank 1D – Base case 49,129 (2,000) (2,100) 45,029 392,740 11.5% -

Bank 1D – no Reg EL shortfall 47,129 0 (4,100) 43,029 392,740 11.0% (50)

Bank 1D – higher Reg EL shortfall

51,129 (4,000) (100) 47,029 392,740 12.0% (0)

• Each of the hypothetical banks shown in our analysis have a constant regulatory EL shortfall (“shortfall”). However, in reality the shortfall varies with each individual bank and in some recent examples (i.e. Canadian banks reporting in November 2017) this has partially absorbed the day 1 CET1 impacts for IFRS 9 provisions

• The below table provides sensitivity analysis as to how different shortfall levels change the day 1 IFRS 9 CET1 impact for our hypothetical bank balance sheets

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• While there are still a couple of years until CECL will be effective for GAAP compliant banks around the world, IFRS 9 was effective from 1 January 2018

• There have been studies completed by Deloitte, EY and the rating agencies projecting the day 1 CET1 impacts of IFRS 9 to be relatively low (ranging between 0 – 100 bps)1 which agrees with our estimates. This can be partially due to the regulatory EL shortfall to accounting provisions cushioning2

• However, the pro-cyclicality nature of IFRS 9 is an area of concern for the market as highlighted in the European Systemic Risk Board paper3 and Barclays study4

• The Barclays study estimates a 200 – 300 bps CET1 erosion due to additional provisioning in a typical downturn

• Note that Barclays assumes a typical downturn would experience 5x increase in PDs to normalized levels, which is more severe compared to our model assumptions

1. Sixth Global IFRS Banking Survey, Deloitte, May 2016 Moody’s Survey: Capital impact of IFRS 9 adoption will be modest for most banks, Moody’s, March 2017 EY IFRS 9 Impairment Banking Survey, EY, August 2017

2. IFRS 9 impact cushioned by regulatory buffer, Fitch Ratings, March 20173. Financial stability implications of IFRS 9, European Systemic Risk Board, July 20174. IFRS 9 –Bigger than Basel IV, Barclays, January 2017

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