Project:Sandbox

3.1.10 Hypothetical Scenario Generation

As described in Section 11.1, customization of internal hypothetical scenarios such as the BAC

Severely Adverse (BAC SA) is managed by ESPE as part of the scenario design process. In addition,

we partner with ESPE in generating the supervisory scenarios such as for CCAR, ECB/EBA, and

PRA stress testing.

In order to achieve the desired stress level speci�ed by ESPE and to use the prescribed variables

appropriately for supervisory scenario construction, the following methodologies may be applied for

the internal hypothetical scenarios (BAC Severely Adverse and BAC Adverse scenarios, and other

Management/Exploratory/Sensitivity scenarios) and supervisory scenarios (CCAR, ECB/EBA and

PRA stress testing). However, if deemed appropriate, these may be applied to other hypothetical

scenario generation.

Methodologies are deemed appropriate as they are leveraging the estimated relationship de-

scribed in the model theory above, with simple modi�cations.

3.1.10.1 Supervisory Scenario Generation General Approach (CCAR/EBA/PRA)

The derived market model supports the generation of supervisory scenarios for CCAR, EBA/ECB,

and PRA (ICAAP) stress testings. The model generally follows three approaches to generate the

supervisory scenarios for required variables. Speci�cally,

• Use prescribed variables directly, if applicable

• Use prescribed variables to map our variables with speci�c mapping methodologies

• Use standard model where inputs have been provided or mapped via one of the above two

methods

Prescribed Variables: The list below shows the prescribed variables as of January 2023 that are

used directly (for EBA, Chow-Lin disaggregation is applied as described in Section 15.1, but no

other adjustments).

• CCAR:

- N/A

• EBA/ECB:

- BRENT

• PRA:

- N/A

Mapped Variables: The list below shows the prescribed variables that are used to map out our

variables (as of January 2023). Please note that these may change with new prescription from the

regulators. Please refer to the mapping methodologies for those variables under Section 3.1.10.

• CCAR:

- Dow Jones Total Stock Market Index (see Section 3.1.10.2)

• EBA/ECB:

- SP500 (USD, quarterly average), EUROSTOXX (Euro, quarterly average), FTSE100

(GBP, quarterly average), Nikkei225 (JPY, quarterly average), MSCI EMLA (USD,

quarterly average), MSCI EMG (USD, quarterly average), AUD(quarterly average),

BRL (quarterly average), CAD (quarterly average), CNY (quarterly average), INR

(quarterly average), (see Section 3.1.10.5)

• PRA:

- SP500 (USD, quarterly average + quarter-end shock at RQ4), FTSE100 (GBP, quarterly

average + quarter-end shock at RQ4)

3.1.10.2 S&P 500 for BAC SA and CCAR

In the case where such customization guidance for a particular variable involves ESPE speci�fication

of a stress magnitude (peak/trough level), one of two techniques may be used. The �rst, based on

the target/loading concept (see [3]), may be used to achieve the desired results and is described

below in 3.1.10.2.1. The second method targets only the growth rate during of the descent of a

speciified scenario while leaving the recovery and long-run horizon growth rates model based as

described in 3.1.10.2.2. The second method was introduced when, during periods of strong recovery

immediately following heightened stress, the model based recovery exceeded the spot level. In

scenarios where the scaled path exceeds spot level the �rst method ampli�es the recovery leading

to unreasonably high recovery rate during the stress horizon.

3.1.10.2.1 Cumulative Decline Path Target Method for BAC SA

As an example, for the CCAR 2019 cycle, one of the customizations for HP BAC is that the S&P

500 troughs in cumulative decline at approximately 50%. The proposed methodology is described

below. For time t = 1 to 13 for BAC SA (HP BAC) scenario,

• Calculate model-based maximum cum. decline (CumD_Model^MAX) from model-based HP BAC

(CumD_Model_t)

• Set S&P500 target cum. decline trough to -50% (CumD_Target)

• Get a scalar for relative shift as

* Scalar = CumD_Target / CumD_Model^MAX

• Calculate new cum. decline for HP BAC as

* CumD_Override_t = CumD_Model_t * Scalar

• Calculate the fi�nal S&P500 levels as

* LVL_Override_t = SP500Spot * (1 + CumD Override_t)

3.1.10.2.2 Growth Rate Path Target Method for BAC SA

As an example, for the CCAR 2023 cycle, one of the customizations for HP BAC is that the S&P

500 troughs in cumulative decline of at least 50%. The proposed methodology is described below.

For time t = 1 to 13 for BAC SA (HP BAC) scenario,

• Calculate the log difference from spot to the target level as


 * TARGET_DLN = LN[SP500_{t=0} * ((1+TARGET)/SP500_{t=0})]

• Calculate the log difference path of the model based S&P 500

• Calculate the total log di�erence from spot to the level trough, which occurs at time T, as

* MODEL DLN = SUM_t=1^T DLN(SP500_Model)_t

• If TARGET_DLN is less than MODEL_DLN, distribute the di�erence across the decline (from

t = 1 to T)


 * DLN (SP500 Override)_t = DLN (SP500 Model)_t * (TARGET_DLN - MODEL_DLN) / T

• Convert the S&P 500 overriden log di�erence path back to levels to obtain the �final path.

3.1.10.2.3 S&P 500 CCAR Scenarios

CCAR scenarios have been derived using the prescribed Dow Jones Total Stock Market Index as

Fed does not provide the S&P500 Index. Since these two indices are very highly correlated, with

correlation of almost 100% (see Figure 39 below), S&P500 Index is assumed to have the same

quarterly return as the prescribed Dow Jones Total Stock Market Index. The Fed's prescribed U.S.

Dow Jones Total Stock Market (float cap) is expressed in end of quarter value and from Bloomberg.

The training window ranges from the �rst data point available from Fed, to the most recent 4Q;

for CCAR 2023, it ranges from 1Q 2001 to 4Q 2022. the quarterly growth rates of prescribed Dow

Jones index is applied on S&P500 spot value.

3.1.10.3 Equity Variables for BAC SA

In the case where ESPE has specific guidance on some variables to reach a particular cumulative

decline, a scalar is calculated based on model-driven decline and target decline and applied to the

shock of each quarter.

As an example, for 3Q19 cycle, one of the customizations for BAC SA (HP BAC) is that MSCI

Asia Paci�c (MSCI ACAP) declines approximately by �50%, MSCI LATAM (MSCI EMLA) by

~60%, and the NIKKEI225 USD by ~41%. To achieve these targets, the following methodology

shall be applied for each variable. This methodology can be applied to any international equity

variables (Single Economy Index or Aggregated Index) if similar customization is required.

• For each quarter t over the projection horizon, calculate the model-based cumulative decline

for HP BAC (CumDmodel_HPBACt) and TD BASE (CumDmodel_TDBASE_t)

• For each quarter t over the projection horizon, calculate the model-based HP BAC shock

(Shock CumDmodel HPBAC) as:

* Shock CumDmodel HPBACt = CumDmodel HPBACt 􀀀 CumDmodel TDBASEt

• For target quarter q, de�ne the target HP BAC shock as:

* Shock CumDtarget HPBACq = CumDtarget HPBACt 􀀀 CumDmodel TDBASEt

where q represents the quarter where model-based HP BAC reaches its trough over the pro-

jection horizon and CumDtarget HPBACt represents the maximum cumulative decline target

speci�ed by ESPE

• Calculate a scalar

* Scalar = Shock CumDtarget HPBACq / Shock CumDmodel HPBACq

• For each quarter t over the projection horizon, calculate the overlaid HP BAC shock as:

* Shock CumDoverlaid HPBACt = Scalar � Shock CumDmodel HPBACt

• For each quarter t over the projection horizon, calculate the overlaid HP BAC cum decline

as:

* CumDoverlaid HPBACt = Shock CumDoverlaid HPBACt + CumDmodel TDBASEt

• For each quarter t over the projection horizon, calculate the overlaid HP BAC level as:


 * Leveloverlaid HPBACt = Spot Level � (1 + CumDoverlaid HPBACt)

The methodology discussed in 3.1.10.2 was initially considered instead of above steps. However,

because of the di�erences in how the long-term is generated for S&P500 (i.e. assumption-based)

and international equities (i.e. model-based), a slightly di�erent methodology was chosen for the

international equities.

Speci�cally, for S&P500, only the 13-quarter results are model based and the assumption that

S&P500 growth rate is the same as NGDP growth rate is applied to generate long-run path beyond

13th quarters. However, for the international equities, the entire 40-year path is model-based. In

the case where S&P500 methodology is applied to international equities, the growth would be much

more aggressive in the long-run. Thus, for MSCI Asia Paci�c, MSCI EMLA and Nikkei225 where

the entire forecast horizon is model-based, the BACSA customization is achieved by customizing

the model-based max BACSA shock relative to baseline scenario to increase the severity of the

BACSA path to hit the trough targets provided by ESPE.

3.1.10.4 Brent Oil for BAC SA

The following methodology shall be applied to achieve the desired results in the case where ESPE

customization for Brent Oil includes a particular quarter to reach trough and recovery level at the

end of 13 quarters.

For example, in 1Q 2023 cycle, the methodology to achieve the speci�cation from ESPE that

BAC SA (HP BAC) oil prices decline following the modeled path up to RQ5, with a cumulative

declining of at least 45% by RQ7 and followed by subsequent steady recovery of around 46% from

trough by RQ13 is summarized below.

• Use the model-based HP BAC path for the fi�rst 5 quarters.

• Assume that the cumulative growth at RQ5 is 0% and the cumulative growth at RQ7 is

25.014% and linearly interpolate the cumulative growth between RQ5 and RQ7. This ensure

that the spot to RQ7 decline is around 45%.

• Using the RQ5 oil price and the interpolated cumulative growth values, the �nal path for

BAC SA scenario of oil price is generated as the follow:

* Brent_t = BrentRQ5 * (1 + CumGrowth_t)

where t = RQ6 to RQ7

• Assume that the cumulative growth at RQ7 is 0% and the cumulative growth at RQ13 is 46%

and linearly interpolate the cumulative growth between RQ7 and RQ13

• Using the RQ7 oil price and the interpolated cumulative growth values, the �nal path for

BAC SA scenario of oil price is generated as the follow:

� Brentt = BrentRQ7 * (1 + CumGrowtht)

where t = RQ8 to RQ13

3.1.10.5 Equity and Exchange Rate Variables for EBA

For EBA stress testing, the equity and exchange rate (FX) variables are generally prescribed from

EBA; however, the prescribed scenarios are in average (AVG) values, whereas our scenarios are

in quarter-end values (END). Additionally, equity variables are all in local FX rather than USD

terms and FX rates are all given against the EUR rather than the USD. For EBA 2023, the

prescribed quarterly average equity variables are S&P500 (USD), EUROSTOXX (EUR), FTSE

(GBP), Nikkei225 (JPY), MSCI EMLA (USD) and MSCI EMG (USD) and the prescribed quarterly

average FX variables are AUD, BRL, CAD, CNY, and INR. Where these variables are prescribed,

a mapping methodology is used to achieve the desired results.

For all EBA Equity variables, the Baseline scenario is prescribed with zero growth rates, there-

fore, for each time period t, ENDt = SpotEND;USD.

For EBA FX variables, the EURUSD scenarios are used to convert prescribed FX rates to USD

terms consistent with GSG FX variables, then the Chow-lin process described in 15.1 is used to

convert this annual prescription to quarterly terms. For both baseline and adverse scenarios some

prescribed paths are set to the EBA spot throughout the horizon. In cases where the EBA scenario

path is set to the EBA spot, scenarios are created in the same manner as the zero growth Equity

scenarios, i.e. for each time period t, ENDt = SpotEND.

For non-zero growth EBA scenarios, the following steps are taken to ensure the severity of the

prescribed variables in AVG and local FX terms are transmitted to the relevant internal variables

in END and USD terms. Note: FX variables are provided with both baseline and adverse paths

and conversions to USD terms are done prior to the Chow-Lin process; therefore, the step below

in which we calculate the path based on deviations from baseline before Chow-Lin, as well as the

step where we convert to USD after the Chow Lin, are only relevant to Equity variables.

• EBA provided deviations from baseline are used to calculate annual average equity paths in

local currencies.

• Derive quarterly levels for each variable from calculated EBA annual average paths with

Chow-Lin disaggregation (see Section 15.1);

* Prescribed^qtr = Chow-Lin(Prescribed^ann)

• Apply currency conversion to quarterly equity levels in local currencies derived above to

convert quarterly equity paths to USD as (note that FX is 1 for variables prescribed in

USD);

* Prescribed^qtr;USD = Prescribedqtr * FX

• For each time period t, calculate quarter-over-quarter annualized growth rates as;

* Prescribed_t = (Prescribed_t^qtr;USD / Prescribed_{i-1}^qtr;USD)^4  - 1

**** got to here

• For each time period t, prescribed calculate shock from baseline rates as;

* Shock_t = (

Prescribedqtr;USD

t;HP EBAS

Prescribedqtr;USD

t;HP EBAB

) 􀀀 1

• To map out and get END values, apply either the growth rates to each spot value in END to

get the END paths (ENDt) or apply the shock to the baseline; For SP500 and non-zero FX

baselines the growth rates are applied as;

� for t = 1, ENDt = SpotUSD � (1 + Prescribedt)

1

4

� for t > 1, ENDt = ENDt􀀀1 � (1 + Prescribedt)

1

4

• For other equity variables and non-zero FX adverse scenarios shocks from baseline are applied

as;

� for t > 0, ENDt;HP EBAS = ENDt;HP EBAB � (1 + Shockt)

In EBA 2021, alternative methodologies were tested but were discarded as severity tended to

be reduced from the prescription. The current method preserves the prescribed severity.

In EBA 2023, as in EBA 2021, the Stock price prescription included both European Union

and the UK. This contrasts with the EBA 2020 release where a single row "European Union" was

given. GSG leveraged in 2023 (similarly to EBA 2021), the UK row of table for FTSE100 and the

"European Union" row for the EUROSTOXX50. This is a sensible assumption, as the Eurozone

represents roughly 85% of the European Union GDP.

3.1.10.6 HY EUR DEV methodology for EBA

Up until EBA 2023 (202301.3), the HY EUR DEV variable was obtained simply by leveraging its

model for both EBAB and EBAS, described in section 3.1.7. The implication is that VSTOXX and

A-BBB spreads drive the variable path regardless of the prescription. In EBA 2023, it was clear

that the ECB prescription in the ECB narrative, Table 4.1.9., involved a stress in EBAS on iTraxx

EU Overall that was more severe than its history but a stress on iTraxx EU Crossover (i.e High

Yield) that reached a peak that was less severe than its historical peak. The methodology up to

2023 took iTraxx Overall into consideration but not iTraxx Crossover.

Although that methodology was undoubtedly sound as it relied on the approved model, the

prescribed Crossover information relates to High Yield EU spreads speci�cally, and as such GSG

intends to henceforth leverage this information in order to better align the �nal HY EUR DEV path

with the prescription. Speci�cally, the methodology for HY EUR DEV should follow the process

outlined below.

1. Temporally disaggregate iTraxx Crossover using the Chow-Lin method, exactly as has been

done currently for iTraxx Overall.

2. Run HY EUR DEV model for both EBAB and EBAS.

3. Calculate historical maxima for HY EUR DEV and iTraxx Crossover, call them hist max

(HY EUR DEV) and hist max(iTraxx Crossover) respectively.

4. Calculate EBAS scenario maximum for HY EUR DEV and iTraxx Crossover, call them EBAS

max(HY EUR DEV) and EBAS max(iTraxx Crossover) respectively.

5. Scale the model-based EBAS HY EUR DEV path so that:

EBAS max(HY EUR DEV ) = hist max(HY EUR DEV )�

EBAS max(iT raxx Crossover)

hist max(iT raxx Crossover)

(41)

This will ensure that the shape of the EBAS path is dictated by the model, while adjusting

its severity to be in line with the severity of the Crossover prescription. Although both are High

Yield indexes, the composition of Crossover compared to HY EUR DEV is slightly distinct with

Crossover and was estimated by GSG to have 9% weight on somewhat higher ratings (BBB- and

higher).

Weights

Credit iTRAXX CROSSOVER HY EUR DEV

BBB- and up 9.30% 0.00%

B- and lower 90.70% 100.00%

Table 7: Index Composition Weights

iTRAXX Crossover EBAS Peak Crossover (temporally disaggregated using Chow-Lin) 893.4

iTRAXX Crossover Historical Peak Crossover GFC 1039.67

Ratio of iTRAXX peaks (EBAS/historical) 0.86

HY EUR DEV historical peak 2079

New peak of HY EUR DEV 1786.51

A number of variants were explored, some of which are shown in the chart below for comparison.

One alternative approach would involve simply using the Crossover EBAS to EBAB shocks and

apply them on top of the HY EUR DEV EBAB path; this was seen as far too mild compared to

any other alternative. Another alternative way to conduct the overlay would imply a scaling that

used just the raw annual average prescribed EBAS iTraxx Crossover, we would end up with the

dashed green line. This would be a reasonable approach and the simpler one, however it would

imply that we would be using an annual average to scale down a quarterly peak. We could also use

the rating-based weights and compute a weighted average of the production path and the Override

path, which would leave us with the brown solid line. As these weights are likely to move and

are not immediately calculable, GSG deemed the simplest approach to just use the temporally

disaggregated Crossover-scaled HY EUR DEV path (solid green line). Furthermore, the di�erence

to the unweighted option is small.

3.1.11 Long-Term Extension

3.1.11.1 Extension Methodology

Extension of CECL/IFRS 9 related variables to the designated long-term horizon is covered in

detail below. For the following variables, the extension past the 10-year horizon follows the same

long-term extension methodologies described in the respective sections of Section 3.1.

• S&P 500;

• NASDAQ;

• International Equity Volatility Indices: VSTOXX, Nikkei VI, IVUKX30;

• International Equity Indices: EUROSTOXX 50 (USD), Nikkei 225 (USD), FTSE 100 (USD),

MSCI Emerging Markets, MSCI EM Latin America, MSCI AC Asia Paci�c;

• International High Yield Corporate Spread indices: EMHY ASIA, EMHY LATAM, EMHY EMEA,

HY EUR DEV.

For the derived currency indices below, the extension methodology for EBA scenarios is to hold

the scenario

at at the Q4 Year 3 level at the end of the prescribed 12Q horizon. For all other

scenarios follow the relevant sections of Section 3.1. This follows the follows the same method as

the core currency indices [4].

• Derived Currency indices: AUD, CAD, BRL, CNY, INR;

Note that FTSE 100 (USD), IVUKX30, and MSCI AC Asia Paci�c are not requested IFRS9

variables. However, downstream variables were requested which use these variables as inputs and

thus their 40-year extension methodology are added.

3.1.11.2 Rationale for Extension Methodology

The scenario projections generated are extended beyond the 13Q horizon to a 10 year horizon, or

longer hoizon as needed. For some use cases such as IFRS9/CECL, the scenario projections are

extended beyond the 10-year horizon to produce lifetime expected credit losses depending on the

maturity of the BAC loan population. Although the CECL and IFRS9 standards require lifetime

expected credit loses, they provide

exibility in how loss forecasts can be extended through the

maturity of loan products (refer to Section 11.4.4 and 11.4.5). In general, as the forecast horizon

increases, the availability of detailed information decreases and the degree of judgement required to

estimate expected credit losses increases. Given the challenge of loss forecasting over longer time

horizons, forecasts are only required if they are reasonable and supportable. The standards do not

prescribe a speci�c extrapolation/reversion methodology beyond the reasonable and supportable

period.

The baseline projections for the derived market model are mostly model based, except for S&P

500 and Brent Oil variables which use futures from Bloomberg where available. The projections

beyond 13Q are also mostly model based, except for S&P 500, derived currencies, and Brent whose

projections converge to their respective baselines or referenced variable's growth rates. Since the

baseline scenarios are representative of long-run equilibrium values, following baseline beyond 13Q

is a reasonable approach from a theoretical perspective. The derived market model incorporates the

short-run business

uctuations used for stress testing with the long-run dynamics of an economy in

equilibrium that governs CECL and IFRS9.

Macroeconomic variables have exhibited cyclical patterns through history and modern macroe-

conomic theory posits the return to a long-equilibrium after short-run random disruptions. As

part of this theoretical construct, appropriate monetary policy and price-wage dynamics are fac-

tors returning the economy to its steady state growth. By assumption, business cycle shocks are

transitory, returning the economy to its long run state. As a result, it is reasonable for all sce-

narios to converge to a baseline path (representing the most likely long-term outcome) after the

shocks dissipate. The question is thus the selection of the most appropriate long-run projection.

Long-run, forward looking consensus coupled with theoretical equilibria should provide the most

reasonable and supportable values for core variables. Derived variables, including derived market

variables, should then follow from the estimated statistical relationships with core variables in order

to maintain the magnitude and direction of these mutual relationships.

The alternative scenarios are allowed to deviate from the long-run economic equilibrium in

the short run, as shocks propagate through the variables. In the case of derived market model,

alternative scenarios converge to the baseline path after the 13Q horizon if not purely model-

based. Market shocks and dislocations have historically reverted more quickly than macroeconomic

dislocations. For example, the BAML's ML A BBB spread to UST 30Y spiked and reverted over

2007 to 2009, thus returning to its longer run path. Similarly, VIX and MOVE reverted quickly

to their longer term averages. These are the variables in core market model, which also cascade

into derived market model. Furthermore, the FRB prescribed paths for VIX, A-BBB, and UST10Y

show reversion over the course of 13Q stress horizon. The quick reversion to longer term paths and

the interconnection with the core and core market models make a 13Q revision reasonable.

Finally, according to Enterprise Credit Risk Group, for the majority of the products in scope for

CECL/IFRS9, losses are concentrated in the �rst 5 years of the horizon. Beyond this horizon, the

impact of Macroeconomic Variables (MEV) projections is considered to be immaterial to the overall

lifetime ECL. And hence, the choice of the extrapolation/reversion methodology is considered to

be of low materiality.

A model system founded upon a theoretical system is preferable to alternatives in that it in-

corporates forward looking consensus when available rather than static historical averages. For

example, the e�ects of the \great moderation" present in macroeconomic data from the 1980s has

reduced the volatility of macroeconomic data and lowered in

ation expectations signi�cantly. This

has had the e�ect of aligning actual in

ation with the Fed's target. The long history of in

ation

(particularly \core" in

ation) has a mean value of 3.7%, higher than the Fed target Simply using the

long-term mean would ignore this structural break due to policy changes. Similarly, the \neutral"

real Fed funds rate has also undergone an important change in recent history and has decreased

from 3% to 0-1% making the current Fed Funds e�ective rate near neutral (thus equilibrium) rather

than below the historical average. The interconnection between the core model and the derived

market model allows the forward looking consensus to

ow to these variables.

3.1.11.3 CECL/IFRS 9 Application

Although the CECL and IFRS 9 standards require lifetime expected credit loses, they provide

exibility in how loss forecasts can be extended through the maturity of loan products (refer to

Sections 11.4.4 and 11.4.5). In addition, there is no explicit guidance from the CECL/IFRS 9

standards on the long-term extrapolation/reversion methodology. Neither is there any explicit

guidance on whether the reversion needs to be done at the macro-economic variable level or loss

level.

Given this regulatory guidance and the challenge of accurately forecasting over longer time

horizons, the macro-economic variable scenarios in the long-run are generally held constant at their

respective long-run equilibrium values.

Note that as EBA mandates the use of IFRS 9 standard in its stress testing, 40Y extension

methodologies will be applied for EBA scenarios. Refer to Section 11.4.3.2 for the discussion.

3.1.11.4 EBA/ECB Stress Testing Application

The EBA prescribes projections only for a 12-quarter horizon. Unless otherwise noted, the short-

term for EBA refers to the 12-quarters from the latest realization to align with the prescribed

projection horizon from the EBA.

For non-EBA scenarios, the scenario projections are generated from the economic scenario mod-

els for a minimum of 10-year horizon (medium term). For some use cases such as CECL/IFRS9, the

scenario projections are extended even beyond the 10-year horizon (long term) to produce lifetime

expected credit losses depending on the maturity of the BAC loan population.

To ensure consistency, EBA scenarios are also extended beyond the 12-quarter horizon. How-

ever, to extend the EBA scenarios beyond the 12-quarter horizon, EBA does not prescribe speci�c

projections. Furthermore, EBA provides only limited guidance on the methodology to extend the

EBA scenarios beyond the 12-quarter horizon.

Currently, for the model-based scenarios, scenarios are extended from the 13-quarter horizon to

the longer term horizons, such that projections are typically extended by continuing to follow the

model where all inputs are available or are held constant in the appropriate space (levels, growth,

etc.). If the model associated with a variable includes a reversion from the short-term 13-quarter

horizon to the BAC baseline feature for non-EBA scenarios, the reversion period will be reduced

to begin after a 12-quarter horizon rather than after the standard 13-quarter horizon for the EBA

scenarios.

The economic scenario models long-term extension methods are currently used for CECL/IFRS9.

In paragraph 46, the EBA states: \banks are requested to forecast credit impairments in

uenced

by the materialisation of a set of single scenarios (baseline and adverse) on the basis of IFRS 9."

Therefore, simply leveraging the current CECL/IFRS 9 long-term extension methodologies for

EBA scenarios would follow the spirit of the EBA Regulatory Guidance. However, as explained

below, it would deviate from the letter of the Guidance which is why some adjustments were made.

The EBA Methodological Note [81] postulates in its paragraph 129: \After the scenario horizon

- excluding GDP, for which constant growth rates shall be assumed - all macroeconomic parameters

and property prices used in the estimation shall be assumed to stay

at (i.e. stable absolute house

prices and other macroeconomic variables considered in the modelling, without assuming any growth

or reversion to the baseline)."

Paragraph 129 lacks speci�cs, which creates several challenges in addressing this guidance, which

we mention next as well as what our thought process was in tackling them.

Firstly, keeping the RGDP QoQann scenarios

at could be prone to generating counter-intuitive

results, such as stress level paths overtaking the baseline level paths for certain variables, e.g. in the

event that stress paths rebound late in the horizon, an outline actually prescribed in earlier EBA

exercises. Therefore, to better align with the guidance while excluding this undesirable possibility,

after the third year of the horizon, RGDP QoQann paths were set constant in both baseline and

stress at the TD BASE 10Y QoQann value. The 10Y point was selected as it is reasonable to

assume the economy stabilizes in its long-term equilibrium at that point (indeed, even the current

BAU approach uses a 10Y point interpolation), and it ensures a more reliable extension than using

the third year last quarter point, which in past experience could induce the stress level to be

more benign than the baseline. The same assumption is applied for RPCE, RDPI, NGDP and

international RGDPs and NGDPs as a matter of consistency. Each TD BASE 10Y QoQann value

is used for the horizon after the 12th quarter for these variables.

In order to ensure some level of consistency between the US Unemployment with US RGDP

paths post-3 year horizon, the Unemployment rate was allowed to revert to baseline gradually

instead of being forced to remain permanently stressed. By the same logic, CLAIMS was set to

BAU.

Setting HPI and CREPI

at in absolute terms, which is o�cially called in the guidance, forces

in

ation rates to be set at zero from Year 4 onward in order to ensure that real property prices

do not converge to zero in the long run, an outcome not reasonable from an economic rationale

standpoint. This might induce a measure of inconsistency with the long term NGDP, but it is the

only way to deliver \absolute

at property prices\.

Therefore, after internal testing, discussion with downstream, and review of ESPE, it was de-

cided that, although our current long run methodology is able to deliver reasonable paths and aligns

with IFRS9, we should implement some adjustments to our long run methodology with regards to

some variables in order to adhere to the guidance as much as possible without compromising the eco-

nomic reasonability and internal consistency of the paths. In particular, these were the adjustments

to the Long Term extension method, implemented to the paths at Year 4 and beyond:

• Real GDP QoQann (US and International) set constant to TD BASE 10Y QoQann after Year

3 under EBA baseline and stress scenarios. Same for US and international NGDPs, and US

RPCE and US RDPI.

• Rates are set at the Quarterly Average of Year 3 for the rest of the long term horizon for both

quarterly and monthly series.

• Core Market (MOVE, VIX, ML AAA AA UST30Y and ML A BBB UST30Y corporate spreads)

constant at the Year 3 Q4 level for the rest of the long term horizon.

• Core Currencies are

at at the Q4 Year 3 level. As most currencies are and have been already

at even within the three year horizon for several past cycles, this decision is of low materiality.

• HPI and CREPI

at in levels as directed by the EBA. Consequently, to ensure consistency

HCPI and CCPI levels are set

at at year 4 and beyond as well.

Derived Market and Market Risk variables leverage the BAU long-term extension methodologies

discussed in each whitepaper, as the extension method in these cases is model-driven and essentially

re

ect assumptions in upstream variables. Also, UNEMP in Core, and Derived Econ, except for

RPCE, RDPI and CREPI, follow the BAU approach.

Furthermore, after discussions with the downstream credit loss team for which the long term

paths could be relevant, it was possible to ascertain that the general GSG LT extension method

is not materially di�erent from the adjusted LT extension method from an expected credit loss

perspective. Therefore, GSG determined the EBA extension method applicable in the EBA exercise

for each variable taking into account economic reasonability and internal consistency between the

variables. These decisions were taken in accordance with ESPE which reviewed this option.

3.1.12 CECL Methodology

The purpose of this section is to outline the Bank's approach to incorporating forward looking

expectations of the macroeconomic environment, which is a requirement under the CECL standard.

The methodology discussed in this section is provided by the Credit Risk Information, Strategy

and Allowance (CRISA; main contact: Brittany Noble) and any changes to their methodology will

be reported under OMR, and they will be updated in the whitepaper in the most closest submissions

planned.

The CECL Standard is a requirement for the Bank's domestic portfolios and any international

portfolios consolidated under a domestic entity. The macroeconomic forecast framework is incor-

porated into the ECL calculation across all entities and portfolios in scope for CECL.

3.1.12.1 CECL Requirements

Per the CECL Standard7, the Expected Credit Loss (ECL) estimate should re

ect all available

information that a�ects the collectability of cash

ows. Unlike the current US GAAP incurred-

loss methodology, an estimate of ECL is required to incorporate management's assessment of past

events, current conditions, and forecasts of future conditions as they a�ect the collectability of cash

ows.

The CECL Standard states that provisions should equal the expected credit losses over the life

of the asset. As the forecast horizon increases depending on the life of the asset, the availability of

detailed information of future events decreases and the degree of judgment required to estimate ECL

increases. Given the challenge of forecasting over longer time horizons, forecasts are only required

if they are reasonable and supportable. For periods beyond the reasonable and supportable forecast

horizon, an entity may revert to historical loss information8.

• An entity shall not rely solely on past events to estimate expected credit losses

• When an entity uses historical loss information, it shall consider the need to adjust histor-

ical information to re

ect the extent to which management expects current conditions and

reasonable and supportable forecasts to di�er from the conditions that existed for the period

over which historical information was evaluated

• The adjustments to historical loss information may be qualitative in nature and should re

ect

changes related to relevant data (such as changes in unemployment rates, property values,

commodity values, delinquency, or other factors that are associated with credit losses on the

nancial asset or in the group of �nancial assets)

• Some entities may be able to develop reasonable and supportable forecasts over the contractual

term of the �nancial asset or a group of �nancial assets

• An entity is not required to develop forecasts over the contractual term of the �nancial asset

or group of �nancial assets

• For periods beyond which the entity is able to make or obtain reasonable and supportable

forecasts of expected credit losses, an entity shall revert to historical loss information deter-

mined in accordance with paragraph 326-20-30-8 that is re

ective of the contractual term of

the �nancial asset or group of �nancial assets

• An entity shall not adjust historical loss information for existing economic conditions or

expectations of future economic conditions for periods that are beyond the reasonable and

supportable period

• An entity may revert to historical loss information at the input level or based on the entire

estimate

• An entity may revert to historical loss information immediately, on a straight-line basis, or

using another rational and systematic basis

3.1.12.2 Interpretation

• The Standard requires the assessment of losses over the life of the loan by incorporating

forward-looking information. One common way to incorporate forward-looking information

is the use of macroeconomic forecasts (i.e., economic scenarios)

• Forecasts are incorporated over a period that is deemed reasonable and supportable

• Beyond the reasonable and supportable period, Banks can revert to using historical loss

information

• The Standard does not require multiple scenarios to be forecasted nor does it prescribe a

speci�c reversion methodology beyond the reasonable and supportable period

3.1.12.3 Bank's Guiding Principles Supporting the Macroeconomic Framework

• Utilize existing BAC baseline macroeconomic forecast

{ Objective, industry consensus forecasts for core macroeconomic variables

{ Market-implied forecasts for variables such as interest rates and where there are market

observables

{ Model-derived forecasts for other variables re

ecting observed historical patterns from

multiple cycles

• Will utilize additional scenarios in the qualitative reserves process re

ecting other forecasted

economic paths

• Governance of the macroeconomic scenarios will consider appropriateness for CECL use case

Principle 1: Address the spirit of the Standard

• Incorporates changes in macroeconomic expectations in a timely and appropriate manner by

using a baseline macroeconomic forecast and a qualitative reserves process

Principle 2: Represent management's best estimate of expected credit losses

• Baseline forecasts along with additional lower probability scenarios are used to achieve best

estimate of expected credit losses

• Establish governance process to ensure scenarios are appropriate for the CECL use case

Principle 3: Consistency across multiple use cases and operational e�ciency

• Existing baseline macroeconomic scenario framework to be leveraged across the Company for

multiple use cases. Ensures consistency across strategic lending decisions and determination

of expected credit losses

Principle 4: Provide Management the tools to react to emerging economic conditions

• Multiple resources (e.g., reports, tools, scenarios) provide management with the information

required to react appropriately to changes in economic environment

3.1.12.4 Key Audit Principles

• Forecasts are unbiased

• Assumptions must be linked to the Firm's risk identi�cation process

• Results are consistent with management's expectations of the key risks that should drive

portfolio losses (i.e., ongoing monitoring of output is intuitive and consistent with real life

observations)

3.1.12.5 Bank's Current Approach

Approach Implementation

The working group researched multiple options and narrowed to two approaches which majority of

the industry is considering as possibilities:

• Option 1: Multiple Path - Baseline + Multiple Scenarios

{ Multiple economic paths will be run through the models to produce expected credit

losses across each path

{ These would then be probability weighted based on management judgment using a num-

ber of objective data points to determine quarterly allowance and provision

• Option 2: Single Path - Historical Reversion for Key Variable(s)

{ Single economic path that converges to a pre-determined historical average will be used

to determine quarterly allowance and provision

The current view is that Option 1 will be the champion approach whereby multiple economic paths

will be run through the models to produce expected credit losses across each path:

• Multiple paths provide information to dynamically react to evolving economic / credit cir-

cumstances, and capturing upside and downside risk to the baseline CECL estimate

• Multiple scenarios including both upside and downside risk relative to the baseline can be

used to estimate current economic conditions within the business cycle

These would then be probability weighted (based on management judgment using a number of

objective data points) to determine quarterly allowance and provision. The Bank's initial lean is

to incorporate the baseline scenario results as formulaic and incorporate the impact of probability

weighted scenarios within the 9-factor imprecision reserves under factor 2 \Economic Uncertainty".

Rationale

The Bank has determined to base the methodology approach and the choice of scenarios on the ro-

bust and well-established scenario generation and forecasting framework that has been approved by

the Board and validated by MRM. The Bank will primarily utilize existing BAC baseline macroeco-

nomic forecast because it contains objective, industry consensus forecasts for core macroeconomic

variables, market-implied forecasts for variables such as interest rates and where there are mar-

ket observables as well as model-derived forecasts for other variables re

ecting observed historical

patterns from multiple cycles. This drives operational e�ciency and consistency because the base-

line scenario is also used in broader enterprise operations and risk management processes such as

corporate planning.

The Bank will choose additional scenarios from the suite of scenarios currently produced by

scenarios generation (True Distribution and stress testing scenarios). A range of scenarios that

includes upside (relative to baseline) as well as moderate and severe recessionary scenarios, is

required to ensure that the Bank appropriately captures the risk that portfolios can have non-

linear impacts under di�erent macro scenarios.

The governance of the macroeconomic scenarios will consider appropriateness for the CECL use

case. In the event custom scenarios are required, governance will include approval from appropriate

committees, MRM validation, etc.

Choice of Scenarios

The current lean for parallel run is to use four scenarios under CECL including the baseline to

account for inherent uncertainty in any one single forecast path. The additional scenarios in Option

1 that will be used to inform imprecision reserves will be the following:

• Scenario(s) that represent an upside from baseline

• Scenario(s) that represent a moderate recession

• Scenario(s) that represent a severe recession

The choice of scenarios will be approved each quarter by the Allowance Committee for Credit

Losses (ACLC). In each of the previous parallel runs, the following alternate scenarios were approved

by the ACLC to be used to generate CECL results:

• 1Q: TD BASE, TDA U4, TDX SS, TDA D5, TDA D4

• 2Q: TD BASE, TDA U4, HP D5 (BAC-A), HP BAC (BAC-SA)

• 3Q: TD BASE, TDA U4, HP CEA, HP CESA using 2Q customization

Starting 4Q19 parallel run, a version of BAC-Adverse and BAC-Severely Adverse for CECL

were generated using ESPE customization guidance from a previous quarter, and labeled as \CEA"

and \CESA" respectively.

Probability Weighting the Multiple Scenarios

The Bank's initial lean is to use probability weights based on di�erent data points (i.e., Leading

Indicators Report, credit cycle, asset quality trends, probability of recession from internal and ex-

ternal sources, etc.) to account for current economic conditions. The choice of probability weights

will be approved each quarter by the ACLC (see governance section for more detail).

Baseline Scenario

The baseline macroeconomic scenario chosen includes approximately 900 variables projected for

ten or more years. The baseline forecast is derived from consensus or external sources, where

available (Blue Chip, Zillow Survey, Market-Based Forwards and Futures, Oxford Economics, etc.).

If not available through consensus or other external sources, internally developed models are used

to estimate variables. The baseline serves as an anchor for the economic scenario model system

which generates alternative scenarios used for other use cases such as CCAR / EST and IFRS 9.

For example, the model uses a 13-quarter to 10-year gradual reversion for key economic variables

following Blue Chip Consensus. The long-run level follows Blue Chip Consensus for core variables

such as GDP, unemployment rate, and in

ation. After the 10-year horizon, the variable is held

constant (either growth rate or level).

Forecast Extension

• All scenarios outlined above are to extend for 40 years

• Certain loan balances in the commercial portfolio have maturities that extend beyond 40 years.

These balances are not material and have an insigni�cant impact on the total allowance of

the Company

• GRA Enterprise Scenario Generation is responsible for the forecast extension methodology

and associated MRM submissions and documentation

• Once the Enterprise Scenario Generation team has extended the variables o�ine, GRA Tech

is responsible for integrating the extended scenarios into their respective implementation

processes and model execution platforms

3.1.12.6 Governance

As CECL introduces new elements into the allowance process such as use of macroeconomic sce-

narios and probability weighting, certain aspects of governance will be enhanced to account for the

selection of the scenarios and probability weights that will be established on a periodic basis.

Existing committees and structures will be used, and governance will be established in line with

each governing body's mandate. The following list below outlines the critical responsibilities from

a governance perspective for the scenario and probability weighting selection:

• GRA will produce the Baseline variable deck which is shared with the Independent Review

and Challenge Committee (IRC)

• The Corporate Planning Execution team will recommend the Baseline variables to the IRC

via an email to the ESPE

• IRC will approve the Baseline Macroeconomic Variables (MEVs)

• The allowance team will recommend any additional scenarios (e.g. TDA scenarios) and asso-

ciated probability weights to the Allowance for Credit Loss Committee (ACLC) for approval

• If customization were to be required, IRC will govern the selection of the scenario, and will

approve the scenario customization and the MEVs associated with the customized scenario

The following section outlines the approach that the Allowance team will incorporate to select the

scenarios and propose a recommended probability weighting to the ACLC:

• Each quarter, the Allowance team will review the choice of macroeconomic scenarios to be

used for CECL allowance process. The range of probability weights will be determined every

quarter as well by utilizing both internal and external resources such as leading Indicators

Report, Economic recession probabilities and other reports which depict both bullish and

bearish outlooks

• The Allowance team recommends the scenarios and the range of probability weights to the

ACLC at the beginning of each quarter typically after the company's earnings release via

email to the ACLC committee

• The Allowance team �nalizes that recommendation at the quarter end ACLC meeting on

business day 3 where the committee will approve the selection of scenarios and the associated

probability weights

3.1.12.7 Macroeconomic Forecasting Options

The Bank has researched multiple options and narrowed to two approaches which the majority of

the industry is considering as possibilities:

• Option 1: Using multiple paths - existing long-term baseline economic forecast supplemented

with multiple alternative forward looking scenarios

• Option 2: Using a single path - existing baseline economic forecast for short-term (e.g., two

years) followed by reversion to pre-determined historical average for key variable(s)

Figure 42 shows two options described above using \Unemployment rate" as an example. Please

note that all variables have such a stark variance between baseline and historical averages.