The Marriage of Credit Risk and IFRS9

Marriage of Credit Risk With IFRS 9

Aakash Ramchand Dil

Manager – Market Risk & Risk Analytics

National Bank of Fujairah - UAE

Global Association of Risk Professionals

May 2016

2

The views expressed in the following material are the

author’s and do not necessarily represent the views of

the Global Association of Risk Professionals (GARP),

its Membership or its Management.

3 | © 2014 Global Association of Risk Professionals. All rights reserved.

Table of Contents

• IFRS 9 overview

• Expected Credit Loss

• Phasing of ECLs

• Role of collateral and LGDs

• Impact on banks

• Implementation timelines


Venn Diagram of Expectations

CollateralExposureClassification criteriaCredit quality migrationNet Exposure

PDs (PIT or TTC)LGDECL

CollateralRecoverySystemsEmerging risks

Quantitative analysis

Qualitative analysis

Implementation Issues


IFRS 9

In July 2014, the International Accounting Standards Board (IASB) issued the final

version of IFRS 9 Financial Instruments (IFRS 9, or the standard), bringing together the

classification and measurement, impairment and hedge accounting phases of the IASB’s

project to replace IAS 39 and all previous versions of IFRS 9.

IFRS 9

Impairment ‘Expected credit loss

model’

Hedge accounting Classification

and measurement

International Financial Reporting Standards 9 (IFRS 9),

IASB's new accounting standard for financial

instruments, provides guidance on classification and

measurement of financial assets, hedge accounting,

and most importantly, a completely new approach on

impairment accounting – one that is based on expected

credit loss (ECL) instead of incurred loss (IL).


IFRS 9 vs. IFRS 39 … is it just a name change ?

Accounting of financial assets is currently guided by the International Accounting

Standard 39 (IAS 39) that recommends the use of the incurred loss model, which

recognizes a credit loss in the profit and loss (P&L) account. This approach to recognize

losses after they have been incurred on a financial asset, has been widely criticized for

being 'too little, too late'.

IAS 39 – Incurred Loss Model

General Provision / Collective Provisions

Performing Watch-list

Specific Provision

Non - Performing

Stage 1

Stage 2

Stage 3Performing

Assets UnderPerforming

AssetsNon -

Performing Assets

EL = 12-month PD *

PV of Cash

Shortfalls EL = Life Time PD * PV

of Cash

ShortfallsEL = Life Time PD * PV

of Cash

Shortfalls

IFRS 9 – forward-lookingexpected credit loss

model


3 Stages of Expected Credit Loss Models

Stage 1

Stage 2

Stage 3Performing

Assets UnderPerforming

AssetsNon -

Performing Assets

ECL = 12-month PD *

PV of Cash

Shortfalls ECL = Life Time PD *

PV of Cash

ShortfallsECL = Life Time PD *

PV of Cash

Shortfalls


3 Stages of Expected Credit Loss Models

The standard does not prescribe specific approaches used to estimate ECLs, but

stresses that the approach adopted must reflect

Probability weighted outcome

The time value of money

Information Set

3. Forward looking

information should be considered

1. Rather than

incorporating a best case or worst case scenario, theECL should in entirety reflect the possibility that a credit loss occurs and the possibility that no creditloss occurs; 2. Estimates of ECL

should be discounted to the reporting date


BASEL Expected Loss Model vs. IFRS 9 ECL Model

Factors requiring

AdjustmentBasel Framework IFRS 9

Time Horizon One year (or 12 months) PD 12 months PD or lifetime PD

Observation period

Five years for retail exposures and seven

years for corporate, bank, and sovereign

exposures

No specific period

Statistical ApproachHybrid of TTC and PIT long run average

defaultsPIT PDs'

Default flagUsually, if the obligor is 90 days past due or

earlierPhasing of exposure

Floor PD and LGD are subject to floors No floors prescribed

EL PD X LGD X EADPD * PV of cash shortfalls

(PD is 12 months or lifetime)


Expected Loss Model vs. IFRS 9 ECL Model


Is PV(CF) = Credit VaR?????

Exposure @ Par

Discounted CFs….

Future Exposure / Forward Value

Assets Volatility

VaR

ECL

DTD & Loss(Stressed)


EAD - Significant Increase in Credit Risk

Exposure

Exposure

Exposure

Time 0 Time 1

Rating 1

Rating 3 / 2

Rating 6

12 months ECL

Life time ECL


EAD – Forward & Total Value

Facility Collat Recoveries Loan Amt coupon Mat Rating

Facility 100 0 0 100 6% 5Y BBB

Cashflows 1 2 3 4 5 PV

AAA 6 5.8 5.53 5.23 86.81 109.37

AA 6 5.79 5.53 5.22 86.65 109.19

A 6 5.79 5.52 5.2 86.16 108.67

BBB 6 5.77 5.48 5.15 85.15 107.55

BB 6 5.69 5.34 4.93 80.06 102.02

B 6 5.66 5.24 4.76 76.44 98.1

CCC 6 5.22 4.54 4.05 63.83 83.64

Default 6 4.29 3.15 3.7 33.99 51.13


Beyond IFRS 9

Banking Book VaR

Year End

Rating

Prob. Of

StateMV

Prob

weightedX - Mu

Prob

weighted

AAA 0.02% 109.37 0.02 2.28 0.00

AA 0.33% 109.19 0.36 2.10 0.01

A 5.95% 108.67 6.47 1.58 0.15

BBB 86.93% 107.55 93.49 0.46 0.19

BB 5.30% 102.02 5.41 (5.07) 1.36

B 1.17% 98.1 1.15 (8.99) 0.95

CCC 0.12% 83.64 0.10 (23.45) 0.66

Default 0.18% 51.13 0.09 (55.96) 5.64

m Mean 107.09 Variance 8.95

STDEV 2.99

Var @ 95% 4.922

Var @ 99% 6.972

VaR @ 5% 6.77% 5.07

VaR @ 1% 1.47% 8.99

If loans distribution is normal

If loans distribution is not normal


PDs – TTC vs. PIT PDs.

Through The Cycle (TTC) or Stressed

PDs:

Count of customers in long term TM

structure as %......

Point In Time (PIT) or Unstressed PDs:

N(d1) -> N(d2) -> 1 – N(d2)

Note: If d1 & d2 is +ve add 0.5 in normal d1 & d2 values….


Estimating Distance to Default

Where:

DPT = Default pointSTD = Short Term DebtLTD = Ling Term Debt


1 year PD vs. Life Time PDs using Transition Matrix

One Year PD Transition Five Years PD Transition

One Year PD Life Time PD Life Time PD

One Year PD Transition Five Years PD Transition

Joint probability of observed rating grade plus

(PD)*(1-PD)

Joint probability of observed rating grade plus (PD)*(Cumulative Rating

downgrade)

Joint probability of observed rating grade plus (PD)*(Cumulative Rating

downgrade)

Assuming life time is 5 years



Rating From Aaa Baa Caa Default

Aaa 87% 13% 0% 0%

Baa 8% 80% 8% 4%

Caa 6% 9% 72% 13%

What is the probability that a “Caa” rated exposure will default over a period of 2

years – Given 1 Year Transition matrix ?



• At the end of year 1 there is 13% chance of default.. And 72% chance that the firm will maintain Caa

rating…

• In year 2 the probability of moving from Caa to Aaa is 6% and PD of Aaa to straight default is 0%.

• Aaa incremental contribution is 6% X 0% = 0.00%

• PD of moving from Caa to Baa is 9% and PD of Baa to straight default is 4%

• Baa Incremental contribution is 9% X 4% = 0.36%

• PD of moving from Caa to Caa is 72% and PD of Caa to straight default is 13%

• Caa Incremental contribution is 72% X 13% = 9.36%

• Joint Probability of Default that a “Caa” rated exposure will default over a period of 2 years will be 13%

plus 9.72% (0% + 0.36% + 9.36%) i.e. 22.72%

Rating From Aaa Baa Caa Default

Aaa 87% 13% 0% 0%

Baa 8% 80% 8% 4%

Caa 6% 9% 72% 13%


Loss Given Default (LGD)

Ratings Movement

Collateral

RecoveryTAT

LGD

LGD is determined by

(i) Loss of principal,

(ii) Carrying costs of non-performing assets,

e.g. interest income lost or foregone, and

(iii) Recovery and workout expenses, for

example direct and indirect administrative

costs.


BASEL LGD – Secured / Unsecured Exposure

Exposure without Collateral Exposure with Collateral – Effective LGD Methodology

Under the foundation approach, BIS prescribes fixed LGD ratios for certain classes of unsecured exposures:

• Senior claims on corporates, sovereigns and banks not secured by recognized collateral attract a 45% LGD.

• All subordinated claims on corporates, sovereigns and banks attract a 75% LGD.

LGD* = LGD X Applicable haircuts

Haircut appropriate for currency mismatch between the collateral and exposure (The standard supervisory haircut for currency risk where exposure and collateral are denominated in different currencies is 8%)


LGD for IFRS 9 – Approximation Matrix Approach

Ratings Movement

Collateral

RecoveryTAT

LGD



Core Assumption:

To begin with this model assumes that in an ideal situation where customer maintains higher

collateralization levels and there is no evident downgrading in risk rating loss given default shall stand at

25%.

Add-on Factor:

In order to generate add-on factor this model constructs an equation based on exponential smoothening

methodology. Using following data points

1 25%

2 35.00%

3 45.00%

4 55.00%

5 65.00%

6 75.00%

7 85.00%

8 95.00%

9 100.00%

y = 0.2533e0.1687x

R² = 0.9521

0%

20%

40%

60%

80%

100%

120%

140%

1 2 3 4 5 6 7 8 9

Exponential smoothening



Smoothening:

An exponential trend line is most useful when data values rise or fall at increasingly higher rates. You

cannot create an exponential trend line if your data contains zero or negative values.

Add-on factors are calculated based on number of notches down grade that are expected in entire life of

portfolio. For demonstration I have considered 22 notch downgrade. For no ratings movement add-on

factor of 0.003 is considered which add-on for 1 notch DG is otherwise it will come down to zero.

0 0.00300

1 0.00300

2 0.00600

3 0.00900

4 0.01199

5 0.01499

6 0.01799

7 0.02099

8 0.02399

9 0.02699

10 0.02998

11 0.03298

12 0.03598

13 0.03898

14 0.04198

15 0.04498

16 0.04798

17 0.05097

18 0.05397

19 0.05697

20 0.05997

21 0.06297

22 0.06597



Matrix Construction:

We will start populating matrix with collateral levels on

rows and ratings movement on columns.

As mentioned earlier minimum LDG considered to start

with considered is 25%.

Matrix is constructed using base of 25% and is taken

forward for each node. Using add-on factors

constructed earlier as per collateralization levels.

In the end after crossing significant rating movements

numbers will start converging to 100%.



Collat levels >90% - 100% 25.0% 25% 26% 27% 28% 29% 31% 33% 36% 38% 41% 45% 48% 52% 56% 61% 66% 71% 76% 82.0% 88% 94% 100%

>80% - 90% 25.3% 26% 26% 27% 28% 30% 32% 34% 36% 39% 42% 45% 49% 53% 57% 61% 66% 71% 77% 82.3% 88% 95% 100%

>70% - 80% 25.6% 26% 26% 27% 29% 30% 32% 34% 36% 39% 42% 45% 49% 53% 57% 62% 66% 71% 77% 82.6% 89% 95% 100%

>60% - 70% 25.9% 26% 27% 28% 29% 30% 32% 34% 37% 39% 42% 46% 49% 53% 57% 62% 67% 72% 77% 82.9% 89% 95% 100%

>50% - 60% 26.2% 26% 27% 28% 29% 31% 32% 35% 37% 40% 43% 46% 50% 53% 58% 62% 67% 72% 77% 83.2% 89% 95% 100%

>45% - 50% 26.5% 27% 27% 28% 29% 31% 33% 35% 37% 40% 43% 46% 50% 54% 58% 62% 67% 72% 78% 83.5% 89% 96% 100%

>40% - 45% 26.8% 27% 28% 29% 30% 31% 33% 35% 38% 40% 43% 47% 50% 54% 58% 63% 68% 73% 78% 83.8% 90% 96% 100%

>35% - 40% 27.1% 27% 28% 29% 30% 32% 33% 35% 38% 41% 44% 47% 50% 54% 59% 63% 68% 73% 78% 84.1% 90% 96% 100%

>30% - 35% 27.4% 28% 28% 29% 30% 32% 34% 36% 38% 41% 44% 47% 51% 55% 59% 63% 68% 73% 79% 84.4% 90% 97% 100%

>25% - 30% 27.7% 28% 29% 29% 31% 32% 34% 36% 38% 41% 44% 47% 51% 55% 59% 64% 68% 74% 79% 84.7% 91% 97% 100%

>22% - 25% 28.0% 28% 29% 30% 31% 32% 34% 36% 39% 41% 44% 48% 51% 55% 59% 64% 69% 74% 79% 85.0% 91% 97% 100%

>19% - 22% 28.3% 29% 29% 30% 31% 33% 35% 37% 39% 42% 45% 48% 52% 56% 60% 64% 69% 74% 80% 85.3% 91% 98% 100%

>17% - 19% 28.6% 29% 29% 30% 32% 33% 35% 37% 39% 42% 45% 48% 52% 56% 60% 65% 69% 74% 80% 85.6% 92% 98% 100%

>15% - 17% 28.9% 29% 30% 31% 32% 33% 35% 37% 40% 42% 45% 49% 52% 56% 60% 65% 70% 75% 80% 85.9% 92% 98% 100%

>13% - 15% 29.2% 29% 30% 31% 32% 34% 35% 38% 40% 43% 46% 49% 53% 56% 61% 65% 70% 75% 80% 86.2% 92% 98% 100%

>10% - 13% 29.5% 30% 30% 31% 32% 34% 36% 38% 40% 43% 46% 49% 53% 57% 61% 65% 70% 75% 81% 86.5% 92% 99% 100%

>7% - 10% 29.8% 30% 31% 32% 33% 34% 36% 38% 41% 43% 46% 50% 53% 57% 61% 66% 71% 76% 81% 86.8% 93% 99% 100%

>5% - 7% 30.1% 30% 31% 32% 33% 35% 36% 38% 41% 44% 47% 50% 53% 57% 62% 66% 71% 76% 81% 87.1% 93% 99% 100%

<=0% - 5% 30.4% 31% 31% 32% 33% 35% 37% 39% 41% 44% 47% 50% 54% 58% 62% 66% 71% 76% 82% 87.4% 93% 100% 100%

Rating movement 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Significant Credit change ??????

Significant Credit change ??????

Collat levels >90% - 100% 50.0% 50% 51% 52% 53% 54% 56% 58% 61% 63% 66% 70% 73% 77% 81% 86% 91% 96% 100% 100.0% 100% 100% 100%

>80% - 90% 50.3% 51% 51% 52% 53% 55% 57% 59% 61% 64% 67% 70% 74% 78% 82% 86% 91% 96% 100% 100.0% 100% 100% 100%

>70% - 80% 50.6% 51% 51% 52% 54% 55% 57% 59% 61% 64% 67% 70% 74% 78% 82% 87% 91% 96% 100% 100.0% 100% 100% 100%

>60% - 70% 50.9% 51% 52% 53% 54% 55% 57% 59% 62% 64% 67% 71% 74% 78% 82% 87% 92% 97% 100% 100.0% 100% 100% 100%

>50% - 60% 51.2% 51% 52% 53% 54% 56% 57% 60% 62% 65% 68% 71% 75% 78% 83% 87% 92% 97% 100% 100.0% 100% 100% 100%

>45% - 50% 51.5% 52% 52% 53% 54% 56% 58% 60% 62% 65% 68% 71% 75% 79% 83% 87% 92% 97% 100% 100.0% 100% 100% 100%

>40% - 45% 51.8% 52% 53% 54% 55% 56% 58% 60% 63% 65% 68% 72% 75% 79% 83% 88% 93% 98% 100% 100.0% 100% 100% 100%

>35% - 40% 52.1% 52% 53% 54% 55% 57% 58% 60% 63% 66% 69% 72% 75% 79% 84% 88% 93% 98% 100% 100.0% 100% 100% 100%

>30% - 35% 52.4% 53% 53% 54% 55% 57% 59% 61% 63% 66% 69% 72% 76% 80% 84% 88% 93% 98% 100% 100.0% 100% 100% 100%

>25% - 30% 52.7% 53% 54% 54% 56% 57% 59% 61% 63% 66% 69% 72% 76% 80% 84% 89% 93% 99% 100% 100.0% 100% 100% 100%

>22% - 25% 53.0% 53% 54% 55% 56% 57% 59% 61% 64% 66% 69% 73% 76% 80% 84% 89% 94% 99% 100% 100.0% 100% 100% 100%

>19% - 22% 53.3% 54% 54% 55% 56% 58% 60% 62% 64% 67% 70% 73% 77% 81% 85% 89% 94% 99% 100% 100.0% 100% 100% 100%

>17% - 19% 53.6% 54% 54% 55% 57% 58% 60% 62% 64% 67% 70% 73% 77% 81% 85% 90% 94% 99% 100% 100.0% 100% 100% 100%

>15% - 17% 53.9% 54% 55% 56% 57% 58% 60% 62% 65% 67% 70% 74% 77% 81% 85% 90% 95% 100% 100% 100.0% 100% 100% 100%

>13% - 15% 54.2% 54% 55% 56% 57% 59% 60% 63% 65% 68% 71% 74% 78% 81% 86% 90% 95% 100% 100% 100.0% 100% 100% 100%

>10% - 13% 54.5% 55% 55% 56% 57% 59% 61% 63% 65% 68% 71% 74% 78% 82% 86% 90% 95% 100% 100% 100.0% 100% 100% 100%

>7% - 10% 54.8% 55% 56% 57% 58% 59% 61% 63% 66% 68% 71% 75% 78% 82% 86% 91% 96% 100% 100% 100.0% 100% 100% 100%

>5% - 7% 55.1% 55% 56% 57% 58% 60% 61% 63% 66% 69% 72% 75% 78% 82% 87% 91% 96% 100% 100% 100.0% 100% 100% 100%

<=0% - 5% 55.4% 56% 56% 57% 58% 60% 62% 64% 66% 69% 72% 75% 79% 83% 87% 91% 96% 100% 100% 100.0% 100% 100% 100%

Rating movement 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

@ 50%

@ 25%



Intuition:

It can be clearly seen that where ever collateral levels are significant there is an LGD advantage,

however, where are no collaterals and ratings deteriorations LGD is higher ultimately converging to

100%. This shows at lower levels of collaterals and ratings deterioration recovery tends to 0% and LGD

increases exponentially. This can lead to an intuitive EL calculation.

in USD Mil. 45% standard LGD

Customer

Exposure 98.10 98.10 98.10

Cost 1.00 1.00 1.00

Net Income 19.00 19.00 19.00

Exposure after collat 88.10 88.10 88.10

Collateral 10.00 10.00 -

Collateralization 11% 11% 0%

Previous Rating BBB BBB BBB

Current Rating B B B

Movement 7 7 7

Prob. Of Default 5.46% 5.46% 5.46%

LGD 45% 31% 39%

Expected Loss 2.16 1.51 1.87

At modeled LGD

Customer 1


Determining ECL

in USD Mil. 45% standard LGD

Customer

Exposure 98.10 98.10 98.10

Cost 1.00 1.00 1.00

Net Income 19.00 19.00 19.00

Exposure after collat 88.10 88.10 88.10

Collateral 10.00 10.00 -

Collateralization 11% 11% 0%

Previous Rating BBB BBB BBB

Current Rating B B B

Movement 7 7 7

Prob. Of Default 5.46% 5.46% 5.46%

LGD 45% 31% 39%

Expected Loss 2.16 1.51 1.87

UL 9.01 6.26 7.76

Total Loss 11.17 7.77 9.63

VaR@95% 5.07 5.07 5.07

VaR@99% 8.99 8.99 8.99

Expected Credit Loss (0.232) (0.161) (0.200)

At modeled LGD

Customer 1


Impact on Risk Based Pricing (RAROC / RARORAC / RORAC)

• RORAC = Net income / Allocated economic capital) allows comparison of investments that

have different levels of risk or different risk profiles

• RAROC = Expected return / Economic capital, or

• RAROC = Expected return / Value at risk.

• RARORAC = Risk adjusted return / Risk adjusted capital


Impact on Banks

Given the IFRS 9 requirements in terms of classification, measurement, and impairment

calculation and reporting, banks should expect to be required to make some changes to the way

they do business, allocate capital, and manage the quality of loans and provisions at origination.

Banks will face modeling, data, reporting, and infrastructure challenges in terms of both:

1. Reassessing the granularity and

2. Enhancing coordination across their finance, risk, and business units.

Other issues:

3. Granular recovery data with TAT, collateral re-valuation, & FSV ???

4. Criteria for phasing, data intensive PV calculations????

5. Intensive quantitative analysis / PV of shortfalls modeling ????

6. Confidence intervals & mean reversion ????


Challenges

• Maximizing Stakeholder Value: Managing expectations and educating external

stakeholders during the period of change

• Market Practice: Understanding the decisions made by their industry peers

• Pricing: Restructuring products when current products and business models may no longer

be viable for a bank or its counterparty

• Impact on Regulatory Ratios: Assessing how reclassifications will impact key ratios,

regulatory capital and risk weights

• Emerging Risk Issues: Assessing the increased operational risk caused by changes to

systems and processes

• Additional Resources: Managing organization wide change: increased competition for

resources, systems, data and process alignment, and management of quality and cost


Timelines

IFRS 9

Impairment ‘Expected credit loss

model’

Hedge accounting Classification

and measurement

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Nov 2009:C&M of

Financial Assets

Oct 2010:C&M of

Financial Liabilities

Jan 2011:Supplementary

doc. On Impairment

Nov 2012:ED on C&M of

Amendments to IFRS 9

Nov 2009:ED on

Impairment

Mar 2013:ED on Financial

InstrumentsExpected Credit Loss

Nov 2013:Hedge

Accounting

Jul 2014:Final

Standard

Jan 2018:IFRS 9

Effective Date


EAD – Forward & Total Value


Annexure: Citations & Credits

• GARP (Exam prep 1998)

• PwC - ifrs-9-classification-measurement

• KPMG – 4-first-impressions-ifrs9-financial-instruments

• EY - IFRS-changes-impacting-the-banking-industry

• RiskPrep – KMV (Distance to Default)

• Bionic Turtle - KMV

• Aptivaa – Building Blocks of Impairment Modeling

• Tata Consulting Services (TCS) - Expected-Loss-0515-1-IFRS9BaselComparison

• DICO-IFRS 9 Modelling and Implementation

• BCBS guidelines for IFRS9

• BIS BCBS128

• Other Public Sources

• JPMorgan CreditMetricsTM 1997 – Technical Document

• Merton CVAR document

• CREDIT RISK Credit risk measurement, New approaches to value at risk and other paradigms

ANTHONY SAUNDERS & LINDA ALLEN

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The Marriage of Credit Risk and IFRS9

Economy & Finance

Transcript of The Marriage of Credit Risk and IFRS9