The Marriage of Credit Risk and IFRS9
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Transcript of 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
4 | © 2014 Global Association of Risk Professionals. All rights reserved.
Venn Diagram of Expectations
CollateralExposureClassification criteriaCredit quality migrationNet Exposure
PDs (PIT or TTC)LGDECL
CollateralRecoverySystemsEmerging risks
Quantitative analysis
Qualitative analysis
Implementation Issues
5 | © 2014 Global Association of Risk Professionals. All rights reserved.
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).
6 | © 2014 Global Association of Risk Professionals. All rights reserved.
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
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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
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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
9 | © 2014 Global Association of Risk Professionals. All rights reserved.
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)
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Expected Loss Model vs. IFRS 9 ECL Model
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Is PV(CF) = Credit VaR?????
Exposure @ Par
Discounted CFs….
Future Exposure / Forward Value
Assets Volatility
VaR
ECL
DTD & Loss(Stressed)
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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
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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
14 | © 2014 Global Association of Risk Professionals. All rights reserved.
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
15 | © 2014 Global Association of Risk Professionals. All rights reserved.
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….
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Estimating Distance to Default
Where:
DPT = Default pointSTD = Short Term DebtLTD = Ling Term Debt
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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
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1 year PD vs. Life Time PDs using Transition Matrix
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 ?
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1 year PD vs. Life Time PDs using 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%
20 | © 2014 Global Association of Risk Professionals. All rights reserved.
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.
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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%)
22 | © 2014 Global Association of Risk Professionals. All rights reserved.
LGD for IFRS 9 – Approximation Matrix Approach
Ratings Movement
Collateral
RecoveryTAT
LGD
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LGD for IFRS 9 – Approximation Matrix Approach
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
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LGD for IFRS 9 – Approximation Matrix Approach
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
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LGD for IFRS 9 – Approximation Matrix Approach
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%.
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LGD for IFRS 9 – Approximation Matrix Approach
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%
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LGD for IFRS 9 – Approximation Matrix Approach
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
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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
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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
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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 ????
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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
32 | © 2014 Global Association of Risk Professionals. All rights reserved.
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
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EAD – Forward & Total Value
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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
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