Global Bank Stress Test-2021-11-08-CEF

The IMF’s Global Bank Stress
Test (GST)
Dimitrios Laliotis on behalf of the GST Team
CEF Financial Stability and Stress Testing online course
November 8, 2021
GST Team: Xiaodan Ding, Ibrahim Ergen, Marco Gross, Ivo
Krznar, Dimitrios Laliotis, Fabian Lipinsky, Pavel
Lukyantsau, Srobona Mitra
INTERNATIONAL MONETARY FUND 1

Presentation Outline
1. The GST and its purpose
2. Coverage in terms of countries and banks
3. The underlying methodology
4. Data inputs for the GST
5. GST output
6. The GST tool
7. Further extensions
8. The methodology’s strengths and weaknesses
2

The GST and its Purpose
• Conduct global bank solvency stress test conditional on macrofinancial
scenarios, in a consistent manner across a large number of
countries (advanced and emerging)
• Operate with public bank-level data, with implications for model choices
(simpler than in IMF FSAPs)
• Enhance macrofinancial analysis, quantification of risks and
vulnerabilities, and inform policy discussions, for example, regarding
sufficiency of capital buffers
3

GST Timeline & Milestones
March 2020
June 2020
September 2020
October 2020
January 2021
July 2021
December 2021
Start of the
work
Presentation to
Management
Informal Board
Meeting
GFSR Chapter 4
Rollout of
the tool
1 st Update
Cycle
IMF
Departmental
GST paper
Relevant past & future work:
‣ GST tool developed and made available to country teams
‣ User manual delivered + road-shows conducted
‣ Tool update cycle: once a year (first update Jul - Aug 2021)
‣ Departmental paper: Illustration of policy impact analysis and sectorization; allows users to sectorize loan
losses (household, corporates) and assess policy impact
‣ Extensions on policy impact
4

What is a Global Bank Stress test
• Global bank solvency stress test of impact of pandemic shock- scenario-based stress test of
individual banks in 33 major banking systems
• Motivation: unprecedented pandemic shock
• Publicly-available data: annual frequency (1995-2019); consolidate, less granular data
• Methodology: simpler than usual in FSAPs: bank level econometric models that link main drivers
of income statements to scenarios
• Scenarios: Baseline WEO; Adverse scenarios based on RES adverse scenarios simulated using
FSGM (any other global and complete adverse scenario model will also work)
• No direct policy offset: Macro and sectoral policies to support borrowers captured in scenarios;
the analysis is aggregate and does not explicitly capture the effects of sectoral policies on banks’
balance sheets
5

Country and Bank Sample Coverage
• 33 countries major banking
systems, covering 92% of
global banking sector assets
• Around 450 banks (ca. 350
without subsidiaries)
• Covering at least 80% of total
banking system assets per
country (>90% in many
countries)
• Choice between consolidated
vs. locational perspective
• UST: wider country coverage
Africa Asia and Pacific Europe Middle East Western Hemisphere
South Africa Australia Austria Saudi Arabia Brazil
China Belgium Canada
Hong Kong SAR Denmark Mexico
India Finland United States
Indonesia
France
Japan
Germany
Singapore Greece
Republic of Korea Ireland
Italy
Luxembourg
Netherlands
Norway
Portugal
Russia
Spain
Sweden
Switzerland
Turkey
United Kingdom
6

Reminder: How do Solvency Stress Tests Work?
Income statement
Interest income
Interest expense
Provisions
Noninterest income
Noninterest expense
Dividends
Taxes
Net income
Macro
scenarios
Other comprehensive
income (OCI)
Balance sheet
Assets
Credit
Liabilities
CAPITAL t+1 = CAPITAL t + NET INCOME t+1 + OCI t+1
CAPITAL RATIO t+1 = CAPITAL t+1 /RWA t+1
Capital
Risk-weighted assets
(RWA)
7

Stress Test Approach & Methodology
Income Statement
and Other Models
Balance Sheet
Projection
Capital shortfalls
• Panel regression models (except for
trading income and other income)
for each country
• Sign restrictions, Bayesian model
averaging
• Combines all Income projections
• Assumptions for taxes, dividends
• Static or dynamic balance sheets
• Risk weights (standardized vs.
IRB approach, Basel formulas)
• Focus on CET1 ratios
• Threshold: 4.5 percent
• PDs and LGDs from NLRs
Left hand side variables
Net Interest Margin
Net Loan Loss Rates
Net Fee and Commission Income
Other Comprehensive Income
Other Income/Expense
Net Trading Income Ratio
Right hand side variables
GDP growth
Unemployment rate
Short term rates
Term spread
Stock price growth
VIX
Corporate spreads
Constant
Stand. deviation approach
8

Econometric Model Components
• Panel econometric models
(BMA) for loss rates, NIMs,
NFCI ratios, delta OCI, and loan
growth
Model Component
Definition
Net Interest Margin (NIM)
NIM = NII(t) / (av(TEA(t)+PR(t)-NPL(t), TEA(t-
1)+PR(t-1)-NPL(t-1)))
TEA = Total Earning Assets net of loan loss
provisions stocks (PR). NII = Net Interest Income.
NPL = Nonperforming Loans.
Net Loan Loss Ratio (NLR) NLR = NL(t) / (TEA(t-1)+PR(t-1)-NPL(t-1) )
NL = Net Loan Loss flow.
• Historical STD-based calibration
for NTI
• Residual which “closes the
P&L”: constant
P&L flows
Net Trading Income Ratio (NTIR)
Net Fee and Commission Income Ratio
(NFCIR)
Other Income/Expense (RESR)
NTIR(t) = av(NTIR) -a(t) stdev(NTIR)
NTIR(t) = NTI(t) / TA(t), the average and standard
deviation taken over the last five years and the a(t)
multiplier reflecting scenario-implied stress on
positional risk and bank business.
NFCIR(t) = NFCI(t) / av(TEA(t)+PR(t), TEA(t-
1)+PR(t-1))
RES = NI after tax + tax + NL – NII – NTI – NFCI.
• Exposure-weighted right handside
macro-financial variables
for internationally active banks
Delta OCI Ratio (DOCIR)
RESR = RES / av(TEA(t)+PR(t), TEA(t-1)+PR(t-1))
DOCIR = (OCI(t)-OCI(t-1)) / av(AFS(t), AFS(t-1))
AFS = Available for Sale securities.
9

Econometric Model Components (ctd)
• Bayesian Model Averaging (BMA) for panel models, with sign constraints on long-run
multipliers
• Why BMA?
‣ Avoid hand-picking models
‣ Explicit account of model uncertainty → foster robustness
• Automatization of estimation: important… estimating 150+ models manually based on partly noisy
and lower-quality data not quite feasible
• Sign constraints: to make sure that results “make sense”, i.e., coefficient signs in line with
theory, and resulting forecasts conditional on scenarios meaningful
10

Data Inputs
• Publicly available data from Fitch (+ Bloomberg, S&P, banks’ annual reports)
• P&L time series data for banks: annual frequency (1995-2019)
• Balance sheet starting point data for banks
• Other regulatory data from Pillar III reports: Risk weights, IRB portfolio shares
• Exposure weights for GSIBs
• Macro-financial variables for the right hand-side of the econometric models:
Real GDP growth
Unemployment rate
Short-term interest rates
Term spread
Stock price growth
VIX
Corporate bond spread
Oil price growth (for selected
countries)
11

Decomposing Loss Rates into PDs and LGDs
• Why this decomposition:
o
o
“Spillover” to interest income (drop) via less performing business
PD PiT feeds to PD TTC, to obtain risk weight impact for IRB portfolios
• Simple structural model, matching broad behavioral dynamics of LGDs:
Frye and Jacobs (2012, JoCR)
• More complex models found to not easily outperform this model
• FED DFAST uses FJ approach to imply stressed LGDs from stressed PDs
• Here: to decompose loss rates to PDs and LGDs
12

RWA Modeling
• Source loan book-total IRB portfolio shares and aggregate risk weight
densities for all 450 banks
• Apply Basel risk weight formulas for loan books’ IRB portion
• Keep STA risk weights constant
Note: IRB portfolio shares and risk weight densities as of end-2019 for an underlying bank sample of about 450 banks (cross-bank medians per country).
Source: Banks’ Pillar III reports and IMF staff calculations.
13

Results: Capital Depletion (drivers and delta between
scenarios)
Aggregated by:
• Bank
• Group of Banks
• Country
• Group of Countries
• Any other weighted scheme
14

Results as Published in October 2020 GFSR
• Banks in much stronger position to deal with impact of the pandemic than they were on the eve of the
global financial crisis
• Reaffirm efficacy of post-GFC Basel reforms
• Important variation in resilience across banks and banking systems
Source: IMF GFSR, October 2020, Chapter 4.
15

Results: Impact on CET1 Ratio (by group of banks or
countries)
I. Capital Ratio Dynamics Over Stress Testing Horizon
CET1 Ratio
(In percent)
Baseline Adverse1 Adverse2
III. Contributions Plot
Scenario 1 Baseline OK
Scenario 2 Adverse1 OK
Year 3
25
20
15
10
5
0
Y0 Y1 Y2 Y3 Y4 Y5
Y0 Y1 Y2 Y3 Y4 Y5
Baseline 14.5 12.7 14.7 16.3 17.8 19.3
Adverse1 14.5 10.5 10.0 9.3 11.0 12.0
Adverse2 14.5 10.0 9.0 9.8 12.0 14.0
NII: N
NTI:
NFC
OCI:
Othe
RWA
Sources: IMF staff calculations.
NII: Net interest income
NTI: Net trading income
NFCI: Net fees and commission income
OCI: Other comprehensive income
Other: Other income/expense
RWA: Risk weighted assets 16

Results: Drivers of Changes in CET1 Ratios
(Across regions, groups)
Contributions to CET1 Ratio Differences: June 2020 Baseline - January 2020
Baseline
(In percentage points, June 2020 baseline minus January 2020 WEO baseline in 2020)
2
-2
-6
-10
-14
Global Western Hemisphere Asia Pacific and Africa Europe
Loan losses NII NFCI
NTI OCI Other
Tax Dividends RWA
CET1 ratio differences
Sources: IMF staff calculations.
NII: Net interest income
NTI: Net trading income
NFCI: Net fees and commission income
OCI: Other comprehensive income
RWA: Risk weighted assets 17

Results: Capital Shortfalls in Illustrative Scenario
(Maximum capital shortfall over 2020-2022, as percent of 2019 GDP)
• CET1 ratio threshold = 4.5 percent
Capital Shortfalls, Initial and Minimum CET1 Ratio
Percent of GDP
3.0
2.5
2.0
1.5
1.0
0.5
Capital shortfall in percent of GDP
Initial capital ratio (RHS)
Capital ratio at trough (RHS)
Percent
25
20
15
10
5
0.0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
0
Sources: IMF staff calculations.
18

Change in CET1 ratio \2
Illustrative Results: CET1 Drivers
GDP Shock and Change in CET1 Ratio
(Baseline scenario, June vintage)
0
-1
-2
-3
-4
-5
-6
-7
-8
-9
-10
-14 -12 -10 -8 -6 -4 -2 0
Change in GDP Growth \1
\1 Defined as the minimum of the cumulative real GDP growth over the period 2020-22
\2 Defined as the change of the CET1 ratio in year of minimum CET1 ratio and the CET1
ratio in 2019.
Sources: IMF staff calculations.
19

Illustrative Results: Change in CET1 ratios,
Individual Banks
(CET1 ratio at low/high point minus CET1 ratio at end-2019)
CET1 Ratio Changes from Year 0 to Low/High
Point, Unweighted Bank-level Distribution
(Percentage points)
20

The GST Tool
• User friendly Excel-based Tool for Area Department Teams of 33 GST countries to
strengthen macrofinancial surveillance
• Instantaneous output: users provide relevant inputs and obtain results instantly
• Flexibility: users can (i) override GST projections or regression coefficients, (ii) provide
input for sectorization of loan losses and policy impact
• Automatic data retrieval: Automatic download and processing of bank and macro data
from centralized data files—data goldmine!
• Differences from previous versions of the GST: (i) subsidiaries now included, (ii)
dynamic vs. static balance sheet assumption
21

Structure of the Tool
Input
Model parameters
Model/Calculations
Output
• Country selection
• Scenarios (baseline
automatically uploaded;
adverse by users)
• Download of bank data (subs
included; starting point)
• Regulatory thresholds
• Optional: Dynamic balance
sheet growth inputs
• Optional: Input external
projections (overrides), PDs,
LGDs for corporate and HH
loan losses and policy impact
• Exposure weights
• Model coefficients
• Other modeling
parameters
• Projections of P&L
components and OCI
• Frye-Jacobs method
to decompose loss
rates into PiT PDs and
LGDs
• Projections of RWAs:
TTC PDs, DT LGDs,
IRB formulas
• Capital projections
• Charts: (i)
aggregate CET1, (ii)
capital shortfall, (iii)
contributions, (iv)
scenarios, (v)
individual bank
historical data and
projections
• Tables: individual
banks’ main
projections
22

Results: Impact on CET1 Ratio (by group of banks or
countries)
I. Capital Ratio Dynamics Over Stress Testing Horizon
CET1 Ratio
(In percent)
Baseline Adverse1 Adverse2
III. Contributions Plot
Scenario 1 Baseline OK
Scenario 2 Adverse1 OK
Year 3
25
20
15
10
5
0
Y0 Y1 Y2 Y3 Y4 Y5
Y0 Y1 Y2 Y3 Y4 Y5
Baseline 14.5 12.7 14.7 16.3 17.8 19.3
Adverse1 14.5 10.5 10.0 9.3 11.0 12.0
Adverse2 14.5 10.0 9.0 9.8 12.0 14.0
NII: N
NTI:
NFC
OCI:
Othe
RWA
Sources: IMF staff calculations.
NII: Net interest income
NTI: Net trading income
NFCI: Net fees and commission income
OCI: Other comprehensive income
Other: Other income/expense
RWA: Risk weighted assets 23

Illustrative Results: GST Tool Dashboard
24

Current Work: Sectorization of Loan Losses, Assessing
Policy Impact
• HH and corporate stress test
GST
model suites involve household
and firm micro data
PDs,
LGDs
PDs,
LGDs
PDs,
LGDs
• Policy counterfactuals can be
modeled
Household
stress test
Corporate
stress test
• Link to GST for banks: scenarioconditional
PDs and LGDs for HHs
and corporates
25

Extensions – Corporate and Household Sector Modules
Objective: Allow for portfolio breakdown to enhance precision of scenario-conditional bank capital
forecasts + for explicitly reflecting policy support measures such as moratoria (via PDs) and guarantees
(primarily via LGDs).
Micro-macro simulation model for households:
• Structural model rooted in macro and micro data
• Household micro data from HFCS for Europe + other
micro survey databases for countries outside Europe
• Obtain PDs and LGDs for households to link
to banks’ retail portfolios
Source: Gross, Tressel, Ding (2021, forthcoming).
Micro-macro simulation model for corporates: Corporate ST presentation.
26

Strengths and Weaknesses of the GST
Strengths
• Consistent methodology for a large number of countries
• Comparably simple methodology: enhance robustness
• Econometric model methodology (BMA): account for model uncertainty
Weaknesses
• Publicly available data limits depth of the model/analysis
• Most notably: currently no portfolio breakdowns
27

advanced stress testing methodologies
advanced stress testing methodologies
Pros and Cons of the Methodology
PROS
CONS
• Cross-country stress test based on
globally consistent macro scenarios
• Main drivers of banks’ financial
statements are modeled in a
consistent manner
• Accounts for model uncertainty
• Increased scalability of the core
engine + computational efficiency
• Absence of granular supervisory,
data limits sophistication of stress
testing methodology
• Aggregate approach limits explicitly
modeling the effects of
sectoral/borrower level policies
• Static balance sheet assumption (as
a starting point)
28

Additional slides: Behind the Scenes
Making the file simple
29

From User Inputs to Outputs
User Settings:
Country, regulatory
capital thresholds,
etc.
Econometric
Model-Based
Projections
Latest WEO Baseline
Scenario Retrieval;
Automated via DMX
Plug-In (Push a
Button)
Frye-Jacobs
Module to
Decompose Loss
Rates to PDs and
LGDs
Balance Sheet
Module
Reporting: Capital
Ratios, Capital
Shortfalls
Up to Two Additional
Adverse Scenarios:
User Input
Risk Weight
Projection Module
30

Bank Data Cleaning & Preparation – Process Flow Diagram
Raw data
Vendor data
Flat file
Filter all data for
banks, reporting
years, consolidation
level and accounting
standard within
scope
Collapse accounting
standard dimension
to fill gaps
(permutations or
waterfall approach)
Historical
dataset ready
for further
cleaning
AllBanks_cleaned.csv
Historical Data Set
Data Exploration
Post-processing,
filtering & overlays
Quality
Approval
Check
Manually fill
missing data
points
(optional)
Automated Checks
for “critical”
variables
Generation of Data
“flags”
Starting Point
Data Set
B/S & Capital
Projections
Model
estimation
Aggregation & Weighting
Output for Historical
Dashboard
all_check.csv
bank_hist_stats.csv
cnt_hist_stats_aggr
egates.csv
Finalize Bank SP Data
Set. List of
Approved Banks and
starting years
Automated postprocessing
edits, empty
data point filling, final SP
post-processing
Final SP Data Set
delivered to the Tool
for BS & Capital
projections
sp.csv
GST
Tool

Scenario Preparation – Process Flow Diagram
Download latest
WEO baseline from
live/published WEO
database
Upload downloaded
WEO data into local
DMX database
Retrieve baseline
projection from
DMX into GST tool
Scenario ready for
further processing
Up to Two Additional
Adverse Scenarios:
User manual input in
GST tool

Econometric Methodology: Panel BMA
• Bayesian Model Averaging (BMA) for panel models, with sign constraints on long-run
multipliers
• Why BMA?
‣ Avoid hand-picking models
‣ Explicit account of model uncertainty → conditional forecasts more robust
• Automatization of estimation: important… estimating 150+ models manually based on noisy
rather low-quality data not quite feasible
• Sign constraints: to make sure that results “make sense”, i.e., coefficient signs in line with
theory, and resulting forecasts conditional on scenarios meaningful
33

Implying PDs and LGDs from NLRs
• Basis: Frye and Jacobs (2012, JoCR)
• Simple model, matching broad behavioral dynamics of LGDs
• More complex models found to not easily outperform this simple model
• FED DFAST uses FJ approach to imply stressed LGDs from stressed
PDs
• In G-ST used instead to decompose (N)LRs to PDs and LGDs