IFRS 9
Stage 2 Definition Approaches
August 2015
Stage 2
•
Why is it important?
• Key to regulatory desire to recognise losses earlier
• Primary factor driving loss volatility and magnitude of
provision
• No specific guidance in the rules as to what to use although
some strong indications of what is not acceptable
•
S2 definition will be more critical for long term products, of
low risk at origination
Moving to S2 implies recognising losses in advance of the
income from the assets. This requires an increase in economic
capital to absorb the cash shortfall or encourages
securitisation/sale to remove these assets from the balance
sheet.
•
2
Regulatory input
•
•
•
IASB has published (Jul 2014) the IFRS9 standard for
implementation by 2018 for all relevant entities
BIS/GAECL committee has published draft guidance (Feb
2015) which re-iterates some key points and suggests
more stringent definitions which are relevant for
internationally active or sophisticated banks
• Throughout this talk * items are additional
considerations raised by BIS/GAECL
The BoE has stated it has not yet decided on a specific
reporting format, it has also at industry events suggested
that auditors will be expected to provide a statement of
compliance on behalf of the eight largest UK financial
institutions.
3
IFRS9 Expected Loss Requirements
•
3 Stages defined• 1: No evidence of increase in risk:
Provide for forward looking losses for cases defaulting in next 12 months
•
2: Significant evidence of increase in risk from that
anticipated at origination:
Provide for forward looking losses for behavioural lifetime of asset
•
3: Evidence of impairment:
Provide for forward looking losses for behavioural lifetime of asset –
recognise income net of impairment charge
Provisions are calculated as the difference between the book value of an
asset and the discounted expected cash flows of the asset using the
Effective Interest Rate for the product (EIR)
4
Stage 2 concepts
•
•
•
•
•
•
•
•
•
30 day rebuttable presumption as a backstop (* relying
primarily on this backstop would be a weak implementation)
Significant increase in default risk (not EL) from original risk
recognition
*Significant determined in terms of additional to that which
would be included in the pricing decision at origination
Take account of changes in maturity
Forward looking, taking account of macro-economic factors at
regional or sectoral level etc
Using all reasonable and supportable information
* Applied consistently across all entities within a group
*Simple notch downgrade approach is unlikely to be sufficiently
granular
* Low risk exemption should not be relied on
5
Modelling Concepts & Components
•
Dispersion & Mean Reversion
•
Markov Chain Matrix
•
EMV Decomposition
•
EMO Decomposition
6
Dispersion & Mean Reversion
•
Two features of stochastic processes in general are:
•
While there is certainty on an accounts current state
there is uncertainty on state state in the future. The
uncertainty grows over time. (dispersion)
•
If an account start at either extreme of the risk
distribution it is likely that on average the account will
migrate towards a less extreme position (mean
reversion)
7
Markov Chain
•
•
The Markov property is that the probability of an object
occupying any given future state depends only on its
current state.
A transition matrix describes a set of transition
probabilities that fulfil this requirement
To
From
•
0.7
0.2
0.1
If V(0) is vector of current states then:
0.1
0.6
0.3
V(t)=V(0).Mt
0
0
1
In a more general form M may change over time M(t).
8
Markov Chain – z-shift
•
•
In a more general form M may change over time M(t).
It is useful to be able to describe a single parameter shift
that stresses a matrix to plausible forms of M(t) – z-shift is
one popular approach
Cumulative
Inverse Normal
0.7
0.2
0.1
0.7
0.9
1.0
0.5244
1.281
Inf
0.1
0.6
0.3
0.1
0.7
1.0
-1.281
0.524
Inf
0
0
1
0
0
1.0
-Inf
-Inf
Inf
Shift
(eg add 0.1)
0.733
0.182
0.085
0.733
0.916
1.0
0.6244
1.381
Inf
0.118
0.615
0.267
0.118
0.734
1.0
-1.181
0.624
Inf
0
0
1
0
0
1
-Inf
-Inf
Inf
9
Modelling mean future PD
2
0.094
0.1
0.094
0.094
0.091
0.086
0.086
0.089
0.081
0.102
0.124
0.109
0.105
0.104
0.109
0.1
0.097
0.094
0.08
0.083
0.077
0.079 NA
NA
NA
3
0.181
0.192
0.181
0.18
0.174
0.181
0.165
0.171
0.183
0.227
0.207
0.193
0.185
0.2
0.19
0.191
0.187
0.181
0.154
0.158
0.147 NA
NA
NA
NA
4
0.261
0.276
0.26
0.26
0.276
0.26
0.238
0.291
0.304
0.283
0.275
0.256
0.266
0.262
0.273
0.276
0.269
0.261
0.222
0.228 NA
NA
NA
NA
NA
5
0.335
0.354
0.333
0.366
0.353
0.334
0.361
0.43
0.338
0.335
0.323
0.328
0.31
0.336
0.35
0.353
0.345
0.334
0.284 NA
NA
NA
NA
NA
NA
6
0.403
0.426
0.441
0.44
0.424
0.474
0.5
0.448
0.375
0.369
0.388
0.358
0.373
0.404
0.421
0.425
0.415
0.402 NA
NA
NA
NA
NA
NA
NA
7
0.465
0.541
0.509
0.508
0.579
0.632
0.501
0.478
0.397
0.426
0.408
0.413
0.431
0.466
0.486
0.491
0.479 NA
NA
NA
NA
NA
NA
NA
NA
8
0.574
0.607
0.572
0.675
0.751
0.615
0.519
0.492
0.446
0.435
0.458
0.464
0.484
0.524
0.546
0.551 NA
NA
NA
NA
NA
NA
NA
NA
NA
9
0.632
0.668
0.744
0.857
0.716
0.625
0.524
0.542
0.446
0.479
0.504
0.511
0.533
0.577
0.601 NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
10
0.686
0.857
0.931
0.805
0.717
0.621
0.568
0.534
0.484
0.52
0.547
0.554
0.578
0.625 NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
11
0.868
1.059
0.864
0.797
0.704
0.666
0.553
0.572
0.518
0.557
0.586
0.594
0.619 NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
12
1.063
0.974
0.847
0.775
0.748
0.642
0.587
0.608
0.55
0.591
0.622
0.63 NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
13
0.971
0.947
0.818
0.816
0.716
0.677
0.619
0.64
0.58
0.623
0.655 NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
14
0.938
0.909
0.856
0.777
0.749
0.708
0.648
0.67
0.607
0.652 NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
15
0.895
0.946
0.81
0.808
0.78
0.737
0.674
0.697
0.631 NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
16
0.927
0.891
0.839
0.838
0.808
0.764
0.698
0.722 NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
17
0.87
0.92
0.866
0.865
0.834
0.788
0.721 NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
18
19
0.895
0.918
0.946
0.97
0.891
0.913
0.889
0.912
0.858
0.88 NA
0.811 NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
20
21
22
23
0.939
0.958
0.976
0.992
0.993
1.013
1.032 NA
0.934
0.954 NA
NA
0.933 NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Eve
r2
M
V
Vintage
Maturity
1
Jan-10
0
Feb-10
0
Mar-10
0
Apr-10
0
May-10
0
Jun-10
0
Jul-10
0
Aug-10
0
Sep-10
0
Oct-10
0
Nov-10
0
Dec-10
0
Jan-11
0
Feb-11
0
Mar-11
0
Apr-11
0
May-11
0
Jun-11
0
Jul-11
0
Aug-11
0
Sep-11
0
Oct-11
0
Nov-11
0 NA
Dec-11 NA
NA
10
EMV decomposition (PLS)
1.1
1.2
1.3
1.4
E=m+v
5
• PLS is effectively linear regression
on a set of orthogonal components
(similar to PCA)
• By limiting the number of
components you can reduce the
number of free parameters and so
increase Signal:Noise in estimates
• 8 parameters now contain 99.5% of
the information (cf 72)
10
Index
15
20
Eve
25
M
V
r2
20
1.
05
15
1.
00
10
0.
90
0.
85
5
Ind
e
x
0.
95
ed
Fit t
M
V
Exogenous_LM
1.0
11
0.
80
Vi
nt
ag
e_
LM
EMO decomposition (PLS)
• EMO decomposition is equivalent to EMV but replaces vintage information
with the risk grade at Observation.
• For model M2 we should consider using Basel LGD segment
• The raw data for this is arranged as in the table (slice for risk grade 12)
PD_Grade_Obs 2
Outcome Month
Average of PD Column Labels
Row Labels
1
2
201102
0.000%
201103
0.000% 0.005%
201104
0.004% 0.005%
201105
0.000% 0.003%
201106
0.004% 0.005%
201107
0.004% 0.006%
201108
0.003% 0.008%
201109
0.000% 0.003%
201110
0.001% 0.005%
201111
0.001% 0.004%
201112
0.000% 0.003%
201201
0.006% 0.007%
201202
0.001% 0.003%
201203
0.018% 0.018%
201204
0.003% 0.022%
201205
0.003% 0.003%
201206
0.001% 0.020%
201207
0.000% 0.000%
201208
0.001% 0.003%
201209
0.003% 0.004%
201210
0.001% 0.003%
201211
0.000% 0.003%
201212
0.000% 0.004%
201301
0.000% 0.001%
201302
0.000% 0.007%
201303
0.001% 0.001%
Months since Observation
3
4
5
6
7
8
9
10
11
12
13
14
15
0.020%
0.022%
0.021%
0.018%
0.023%
0.012%
0.012%
0.017%
0.023%
0.018%
0.027%
0.028%
0.023%
0.023%
0.022%
0.025%
0.022%
0.012%
0.022%
0.018%
0.033%
0.017%
0.028%
0.032%
0.039%
0.026%
0.027%
0.032%
0.023%
0.018%
0.024%
0.024%
0.029%
0.027%
0.030%
0.023%
0.020%
0.037%
0.023%
0.033%
0.029%
0.028%
0.032%
0.024%
0.033%
0.033%
0.037%
0.020%
0.036%
0.040%
0.028%
0.023%
0.032%
0.035%
0.032%
0.028%
0.027%
0.032%
0.020%
0.035%
0.037%
0.035%
0.044%
0.026%
0.031%
0.033%
0.029%
0.044%
0.046%
0.036%
0.040%
0.036%
0.026%
0.032%
0.033%
0.028%
0.041%
0.038%
0.029%
0.026%
0.041%
0.041%
0.043%
0.044%
0.037%
0.024%
0.028%
0.035%
0.042%
0.041%
0.039%
0.040%
0.039%
0.047%
0.027%
0.024%
0.048%
0.033%
0.026%
0.037%
0.035%
0.034%
0.042%
0.059%
0.039%
0.045%
0.037%
0.034%
0.049%
0.041%
0.040%
0.031%
0.044%
0.025%
0.034%
0.036%
0.036%
0.028%
0.028%
0.036%
0.042%
0.035%
0.056%
0.039%
0.037%
0.042%
0.037%
0.056%
0.050%
0.037%
0.049%
0.032%
0.038%
0.045%
0.036%
0.023%
0.041%
0.036%
0.044%
0.037%
0.054%
0.038%
0.039%
0.041%
0.048%
0.045%
0.044%
0.059%
0.037%
0.037%
0.044%
0.039%
0.033%
0.045%
0.035%
0.043%
0.044%
0.057%
0.034%
0.039%
0.046%
0.048%
0.052%
0.045%
0.037%
0.046%
0.049%
0.040%
0.035%
0.041%
0.033%
0.044%
0.041%
0.047%
0.050%
0.043%
0.046%
0.036%
0.053%
0.059%
0.045%
0.047%
0.044%
0.038%
0.035%
0.035%
0.037%
0.033%
0.049%
0.060%
0.045%
0.051%
0.034%
0.054%
0.059%
0.060%
0.040%
0.037%
0.035%
0.030%
0.040%
0.031%
0.045%
0.050%
0.039%
0.051%
0.043%
0.049%
0.058%
0.035%
0.035%
0.030%
0.038%
0.044%
0.040%
0.051%
0.053%
0.035%
0.052%
0.038%
0.051%
0.053%
0.045%
0.035%
0.040%
0.049%
0.040%
0.058%
0.048%
0.033%
0.047%
0.044%
0.053%
0.045%
12
M0: Predict probability of default from origination
Use:
• A version of this model is potentially required to determine mean PD risk
assessed at origination over remaining life.
• Used in impairment forecasting to determine PD for new business
• Used in Stage 1 v 2 allocation (back dated version of models required)
Methodology:
• Standard Partial Least Squares EMV approach as used in current forecasting
tools
• Incorporate segmentation/dependency by origination APR / price segment if
possible (to reflect expectations backed out of pricing)
• Macro-economic effects modelled as time series of e-component
• Alternative Panel Regression or Survival model approaches are also feasible
but do not necessarily provide benefit
Key Model Risks:
• E-component may be difficult to model
13
M1: Predict probability of default from observation
Use:
• Used as forward looking calibration of PiT PD for IFRS9 S1 model
• Used in impairment forecasting to determine PD for S1
• Used in Stage 1 v 2 allocation
Methodology:
• Standard Partial Least Squares approach enhanced to include current
risk grade as key driver
• Macro-economic effects modelled as time series of e-component
• Alternative Panel Regression or Survival model approaches are also
feasible but do not necessarily provide benefit
Key Model Risks:
• E-component may be difficult to model
• Likelihood is that short term impact of E component on PD is small
as risk grade is recalibrated quarterly to reflect economic conditions
14
10
M1: Typical Results
5
0
RelativeRisk
0.2
-5
0.0
Risk Grade 1
Risk Grade 6
Risk Grade 12
-10
-0.2
Exogenous
0.4
Exogenous
-RealDispIncome/10
201101 201107 201201 201207 201301 201307 201402 201408
Date
Index
0
10
20
Maturity
Maturity (months)
30
40
15
Stage 2 Allocation: Models based approach
•
Definition of “significantly” higher risk – not necessarily statistical
•
* Recent draft BIS paper highlights that IRB advanced banks
would not be expected to rebut 30 day for S2 and 90 day for S3
•
Strict interpretation would require the bank to capture the
lifetime PD curve expected at point of origination (baking in only
performance known at that time and economic expectations at
that point in time – i.e. a backdated version of M0 for all points
in the past) and use that as comparator.
•
Possibly unrealistic given some products have been open since
1970s or earlier
•
Additional guidance in BIS paper is based on credit risk
element of pricing and what that implies about management’s
risk assessment for the asset.
16
What to compare?
•
Compare remaining lifetime PD compared to that anticipated
at origination
• May be able to use 12month PD as a surrogate if no
evidence that this is materially inaccurate
• Remaining lifetime PD is not necessarily a single number but
a hazard rate across the remaining life.
•
Proposal: Calculate the average remaining life PD
weighted by the discounted contractual cash flows at
each period
•
This is a consistent estimate of which accounts will generate
higher credit losses without relying on effects associated with
collateral and guarantees (which is what would occur if you
used LEL itself and which the standard prohibits)
17
Stage 2 Allocation: Possible Methodology step 1
•
To identify significant increase in risk versus expectations at origination – use M0 to predict
mean remaining lifetime PD by vintage and price point expectation (segment or continuous
APR)
•
Use targeted migration matrices adjusted to achieve M0 curve on average to determine 90 th
%ile point. (this can be adjusted as required).
PD Hazard Rate
Accounts allocated to S2
Distribution of lifetime PD by grade
predicted based on targeted historic grade
migrations to estimate expected dispersion
of cases
90th %ile
Mean remaining life
PD anticipated from
observation based on
curve at origination
Origination
Time
Observation
18
Stage 2 Allocation: Possible Methodology step 2
•
Use 90th %ile as boundary risk grade to identify accounts today with significant increase in risk
from that vintage
•
(In addition use backstop 30 day + other flags as required)
•
Alternatively can apply collectively to the vintage as a proportion moving to Stage 2.
PD Hazard Rate
Accounts allocated to S2
Observed distribution of remaining life PD
from M1 for cases in vintage/price segment
Distribution of lifetime PD by grade
predicted based on targeted historic grade
migrations to estimate expected dispersion
of cases
90th %ile
Mean remaining life
PD anticipated from
observation based on
curve at origination
Origination
Time
Observation
19
Non-modelled S2 triggers
•
Having a modelled approach to defining S2 as the primary
determinant does not remove the need to consider
specific flags which should also be considered (to simplify
forecasting however it would be helpful if they
represented a small proportion of S2 triggers).
• 30 day past due
• Restructuring of the account in such a way as to reduce
the NPV of the account (e.g. would need to
demonstrate that re-pricing of cards to lower APRs was
compensated by longer life and spend to avoid S2)
• Evidence of credit deterioration of the obligor on
other accounts
20
Incorporating expert judgement and collective
impairment adjustments
•
The IFRS9 references that for many asset classes (and
retail in particular) it may, on an individual loan basis be
impossible to determine which accounts have experienced
increased risk since origination.
•
To remedy this management is required to consider
collective impairment adjustments for regions, sectors or
other segments which may be considered to have
significantly higher risk.
•
This process could be used to adjust the estimated
lifetime PD for each account – this would naturally then
be reflected collectively in the Stage 2 definition process
described on the previous slides
21
High Level Calculation Approach:
Impairment Forecasting
Forecasting and Stress testing IFRS9 provision is a challenge principally due to the need
to estimate future S2 allocation (the losses over the lifetime are required for the current
book for actuals). This requires estimation of the future risk grade distribution and
comparison to that expected
1.
Based on Actuals models for current book it is possible to calculate a probability of
defaulting and subsequent losses for every future period
•
2.
3.
Add in losses for future lending analogous to current processes
To work out provision at future points need an estimate of LEL weighted probability
of being S2+ at each point (and hence proportion providing at LEL rather than EL)
•
Use transition matrix approach with z-shift target to forecast predicted LEL
from actuals to provide balances by risk buckets for each Risk Grade at Obs.
(Grade Dispersion)
•
Use forecast grade dispersion from origination to present as starting point to
determine expected grade distribution and hence S2+ proportion at points in
the future using migration targeted to vintage level M0 curves
Calculate discounted future cash flows at different points in the future to determine
provision at each point and thereby impairment charge
22
Transition Arrangements
•
7.2.20 If, at the date of initial application, determining
whether there has been a significant increase in credit risk
since initial recognition would require undue cost or
effort, an entity shall recognise a loss allowance at an
amount equal to lifetime expected credit losses at
each reporting date until that financial instrument is
derecognised (unless that financial instrument is low credit
risk at a reporting date, in which case paragraph 7.2.19(a)
applies).
This may be quite punitive and would suggest considerable
investment to avoid this outcome.
23
Open questions
•
Should changes in effective lifetime of an asset be included
within the increase in significant risk – it is possible that
economic conditions are stable but asset life has increased
therefore default risk over the remaining life is higher?
•
If adopting a multiple economic scenario approach to
estimating losses accounts may be stage 2 on some
simulated scenarios and not others. Does every account
have a Stage 2 weighting in this interpretation consistent
with the relative weightings of the economic scenarios?
•
How do you manage the process of backdating
expectations of risk at origination when these weren’t
collected at the time?
24