Global Bank Stress Test-2021-11-08-CEF
You also want an ePaper? Increase the reach of your titles
YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.
The IMF’s <strong>Global</strong> <strong>Bank</strong> <strong>Stress</strong><br />
<strong>Test</strong> (GST)<br />
Dimitrios Laliotis on behalf of the GST Team<br />
<strong>CEF</strong> Financial Stability and <strong>Stress</strong> <strong>Test</strong>ing online course<br />
November 8, <strong>2021</strong><br />
GST Team: Xiaodan Ding, Ibrahim Ergen, Marco Gross, Ivo<br />
Krznar, Dimitrios Laliotis, Fabian Lipinsky, Pavel<br />
Lukyantsau, Srobona Mitra<br />
INTERNATIONAL MONETARY FUND 1
Presentation Outline<br />
1. The GST and its purpose<br />
2. Coverage in terms of countries and banks<br />
3. The underlying methodology<br />
4. Data inputs for the GST<br />
5. GST output<br />
6. The GST tool<br />
7. Further extensions<br />
8. The methodology’s strengths and weaknesses<br />
2
The GST and its Purpose<br />
• Conduct global bank solvency stress test conditional on macrofinancial<br />
scenarios, in a consistent manner across a large number of<br />
countries (advanced and emerging)<br />
• Operate with public bank-level data, with implications for model choices<br />
(simpler than in IMF FSAPs)<br />
• Enhance macrofinancial analysis, quantification of risks and<br />
vulnerabilities, and inform policy discussions, for example, regarding<br />
sufficiency of capital buffers<br />
3
GST Timeline & Milestones<br />
March 2020<br />
June 2020<br />
September 2020<br />
October 2020<br />
January <strong>2021</strong><br />
July <strong>2021</strong><br />
December <strong>2021</strong><br />
Start of the<br />
work<br />
Presentation to<br />
Management<br />
Informal Board<br />
Meeting<br />
GFSR Chapter 4<br />
Rollout of<br />
the tool<br />
1 st Update<br />
Cycle<br />
IMF<br />
Departmental<br />
GST paper<br />
Relevant past & future work:<br />
‣ GST tool developed and made available to country teams<br />
‣ User manual delivered + road-shows conducted<br />
‣ Tool update cycle: once a year (first update Jul - Aug <strong>2021</strong>)<br />
‣ Departmental paper: Illustration of policy impact analysis and sectorization; allows users to sectorize loan<br />
losses (household, corporates) and assess policy impact<br />
‣ Extensions on policy impact<br />
4
What is a <strong>Global</strong> <strong>Bank</strong> <strong>Stress</strong> test<br />
• <strong>Global</strong> bank solvency stress test of impact of pandemic shock- scenario-based stress test of<br />
individual banks in 33 major banking systems<br />
• Motivation: unprecedented pandemic shock<br />
• Publicly-available data: annual frequency (1995-2019); consolidate, less granular data<br />
• Methodology: simpler than usual in FSAPs: bank level econometric models that link main drivers<br />
of income statements to scenarios<br />
• Scenarios: Baseline WEO; Adverse scenarios based on RES adverse scenarios simulated using<br />
FSGM (any other global and complete adverse scenario model will also work)<br />
• No direct policy offset: Macro and sectoral policies to support borrowers captured in scenarios;<br />
the analysis is aggregate and does not explicitly capture the effects of sectoral policies on banks’<br />
balance sheets<br />
5
Country and <strong>Bank</strong> Sample Coverage<br />
• 33 countries major banking<br />
systems, covering 92% of<br />
global banking sector assets<br />
• Around 450 banks (ca. 350<br />
without subsidiaries)<br />
• Covering at least 80% of total<br />
banking system assets per<br />
country (>90% in many<br />
countries)<br />
• Choice between consolidated<br />
vs. locational perspective<br />
• UST: wider country coverage<br />
Africa Asia and Pacific Europe Middle East Western Hemisphere<br />
South Africa Australia Austria Saudi Arabia Brazil<br />
China Belgium Canada<br />
Hong Kong SAR Denmark Mexico<br />
India Finland United States<br />
Indonesia<br />
France<br />
Japan<br />
Germany<br />
Singapore Greece<br />
Republic of Korea Ireland<br />
Italy<br />
Luxembourg<br />
Netherlands<br />
Norway<br />
Portugal<br />
Russia<br />
Spain<br />
Sweden<br />
Switzerland<br />
Turkey<br />
United Kingdom<br />
6
Reminder: How do Solvency <strong>Stress</strong> <strong>Test</strong>s Work?<br />
Income statement<br />
Interest income<br />
Interest expense<br />
Provisions<br />
Noninterest income<br />
Noninterest expense<br />
Dividends<br />
Taxes<br />
Net income<br />
Macro<br />
scenarios<br />
Other comprehensive<br />
income (OCI)<br />
Balance sheet<br />
Assets<br />
Credit<br />
Liabilities<br />
CAPITAL t+1 = CAPITAL t + NET INCOME t+1 + OCI t+1<br />
CAPITAL RATIO t+1 = CAPITAL t+1 /RWA t+1<br />
Capital<br />
Risk-weighted assets<br />
(RWA)<br />
7
<strong>Stress</strong> <strong>Test</strong> Approach & Methodology<br />
Income Statement<br />
and Other Models<br />
Balance Sheet<br />
Projection<br />
Capital shortfalls<br />
• Panel regression models (except for<br />
trading income and other income)<br />
for each country<br />
• Sign restrictions, Bayesian model<br />
averaging<br />
• Combines all Income projections<br />
• Assumptions for taxes, dividends<br />
• Static or dynamic balance sheets<br />
• Risk weights (standardized vs.<br />
IRB approach, Basel formulas)<br />
• Focus on CET1 ratios<br />
• Threshold: 4.5 percent<br />
• PDs and LGDs from NLRs<br />
Left hand side variables<br />
Net Interest Margin<br />
Net Loan Loss Rates<br />
Net Fee and Commission Income<br />
Other Comprehensive Income<br />
Other Income/Expense<br />
Net Trading Income Ratio<br />
Right hand side variables<br />
GDP growth<br />
Unemployment rate<br />
Short term rates<br />
Term spread<br />
Stock price growth<br />
VIX<br />
Corporate spreads<br />
Constant<br />
Stand. deviation approach<br />
8
Econometric Model Components<br />
• Panel econometric models<br />
(BMA) for loss rates, NIMs,<br />
NFCI ratios, delta OCI, and loan<br />
growth<br />
Model Component<br />
Definition<br />
Net Interest Margin (NIM)<br />
NIM = NII(t) / (av(TEA(t)+PR(t)-NPL(t), TEA(t-<br />
1)+PR(t-1)-NPL(t-1)))<br />
TEA = Total Earning Assets net of loan loss<br />
provisions stocks (PR). NII = Net Interest Income.<br />
NPL = Nonperforming Loans.<br />
Net Loan Loss Ratio (NLR) NLR = NL(t) / (TEA(t-1)+PR(t-1)-NPL(t-1) )<br />
NL = Net Loan Loss flow.<br />
• Historical STD-based calibration<br />
for NTI<br />
• Residual which “closes the<br />
P&L”: constant<br />
P&L flows<br />
Net Trading Income Ratio (NTIR)<br />
Net Fee and Commission Income Ratio<br />
(NFCIR)<br />
Other Income/Expense (RESR)<br />
NTIR(t) = av(NTIR) -a(t) stdev(NTIR)<br />
NTIR(t) = NTI(t) / TA(t), the average and standard<br />
deviation taken over the last five years and the a(t)<br />
multiplier reflecting scenario-implied stress on<br />
positional risk and bank business.<br />
NFCIR(t) = NFCI(t) / av(TEA(t)+PR(t), TEA(t-<br />
1)+PR(t-1))<br />
RES = NI after tax + tax + NL – NII – NTI – NFCI.<br />
• Exposure-weighted right handside<br />
macro-financial variables<br />
for internationally active banks<br />
Delta OCI Ratio (DOCIR)<br />
RESR = RES / av(TEA(t)+PR(t), TEA(t-1)+PR(t-1))<br />
DOCIR = (OCI(t)-OCI(t-1)) / av(AFS(t), AFS(t-1))<br />
AFS = Available for Sale securities.<br />
9
Econometric Model Components (ctd)<br />
• Bayesian Model Averaging (BMA) for panel models, with sign constraints on long-run<br />
multipliers<br />
• Why BMA?<br />
‣ Avoid hand-picking models<br />
‣ Explicit account of model uncertainty → foster robustness<br />
• Automatization of estimation: important… estimating 150+ models manually based on partly noisy<br />
and lower-quality data not quite feasible<br />
• Sign constraints: to make sure that results “make sense”, i.e., coefficient signs in line with<br />
theory, and resulting forecasts conditional on scenarios meaningful<br />
10
Data Inputs<br />
• Publicly available data from Fitch (+ Bloomberg, S&P, banks’ annual reports)<br />
• P&L time series data for banks: annual frequency (1995-2019)<br />
• Balance sheet starting point data for banks<br />
• Other regulatory data from Pillar III reports: Risk weights, IRB portfolio shares<br />
• Exposure weights for GSIBs<br />
• Macro-financial variables for the right hand-side of the econometric models:<br />
Real GDP growth<br />
Unemployment rate<br />
Short-term interest rates<br />
Term spread<br />
Stock price growth<br />
VIX<br />
Corporate bond spread<br />
Oil price growth (for selected<br />
countries)<br />
<strong>11</strong>
Decomposing Loss Rates into PDs and LGDs<br />
• Why this decomposition:<br />
o<br />
o<br />
“Spillover” to interest income (drop) via less performing business<br />
PD PiT feeds to PD TTC, to obtain risk weight impact for IRB portfolios<br />
• Simple structural model, matching broad behavioral dynamics of LGDs:<br />
Frye and Jacobs (2012, JoCR)<br />
• More complex models found to not easily outperform this model<br />
• FED DFAST uses FJ approach to imply stressed LGDs from stressed PDs<br />
• Here: to decompose loss rates to PDs and LGDs<br />
12
RWA Modeling<br />
• Source loan book-total IRB portfolio shares and aggregate risk weight<br />
densities for all 450 banks<br />
• Apply Basel risk weight formulas for loan books’ IRB portion<br />
• Keep STA risk weights constant<br />
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).<br />
Source: <strong>Bank</strong>s’ Pillar III reports and IMF staff calculations.<br />
13
Results: Capital Depletion (drivers and delta between<br />
scenarios)<br />
Aggregated by:<br />
• <strong>Bank</strong><br />
• Group of <strong>Bank</strong>s<br />
• Country<br />
• Group of Countries<br />
• Any other weighted scheme<br />
14
Results as Published in October 2020 GFSR<br />
• <strong>Bank</strong>s in much stronger position to deal with impact of the pandemic than they were on the eve of the<br />
global financial crisis<br />
• Reaffirm efficacy of post-GFC Basel reforms<br />
• Important variation in resilience across banks and banking systems<br />
Source: IMF GFSR, October 2020, Chapter 4.<br />
15
Results: Impact on CET1 Ratio (by group of banks or<br />
countries)<br />
I. Capital Ratio Dynamics Over <strong>Stress</strong> <strong>Test</strong>ing Horizon<br />
CET1 Ratio<br />
(In percent)<br />
Baseline Adverse1 Adverse2<br />
III. Contributions Plot<br />
Scenario 1 Baseline OK<br />
Scenario 2 Adverse1 OK<br />
Year 3<br />
25<br />
20<br />
15<br />
10<br />
5<br />
0<br />
Y0 Y1 Y2 Y3 Y4 Y5<br />
Y0 Y1 Y2 Y3 Y4 Y5<br />
Baseline 14.5 12.7 14.7 16.3 17.8 19.3<br />
Adverse1 14.5 10.5 10.0 9.3 <strong>11</strong>.0 12.0<br />
Adverse2 14.5 10.0 9.0 9.8 12.0 14.0<br />
NII: N<br />
NTI:<br />
NFC<br />
OCI:<br />
Othe<br />
RWA<br />
Sources: IMF staff calculations.<br />
NII: Net interest income<br />
NTI: Net trading income<br />
NFCI: Net fees and commission income<br />
OCI: Other comprehensive income<br />
Other: Other income/expense<br />
RWA: Risk weighted assets 16
Results: Drivers of Changes in CET1 Ratios<br />
(Across regions, groups)<br />
Contributions to CET1 Ratio Differences: June 2020 Baseline - January 2020<br />
Baseline<br />
(In percentage points, June 2020 baseline minus January 2020 WEO baseline in 2020)<br />
2<br />
-2<br />
-6<br />
-10<br />
-14<br />
<strong>Global</strong> Western Hemisphere Asia Pacific and Africa Europe<br />
Loan losses NII NFCI<br />
NTI OCI Other<br />
Tax Dividends RWA<br />
CET1 ratio differences<br />
Sources: IMF staff calculations.<br />
NII: Net interest income<br />
NTI: Net trading income<br />
NFCI: Net fees and commission income<br />
OCI: Other comprehensive income<br />
RWA: Risk weighted assets 17
Results: Capital Shortfalls in Illustrative Scenario<br />
(Maximum capital shortfall over 2020-2022, as percent of 2019 GDP)<br />
• CET1 ratio threshold = 4.5 percent<br />
Capital Shortfalls, Initial and Minimum CET1 Ratio<br />
Percent of GDP<br />
3.0<br />
2.5<br />
2.0<br />
1.5<br />
1.0<br />
0.5<br />
Capital shortfall in percent of GDP<br />
Initial capital ratio (RHS)<br />
Capital ratio at trough (RHS)<br />
Percent<br />
25<br />
20<br />
15<br />
10<br />
5<br />
0.0<br />
1 2 3 4 5 6 7 8 9 10 <strong>11</strong> 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29<br />
0<br />
Sources: IMF staff calculations.<br />
18
Change in CET1 ratio \2<br />
Illustrative Results: CET1 Drivers<br />
GDP Shock and Change in CET1 Ratio<br />
(Baseline scenario, June vintage)<br />
0<br />
-1<br />
-2<br />
-3<br />
-4<br />
-5<br />
-6<br />
-7<br />
-8<br />
-9<br />
-10<br />
-14 -12 -10 -8 -6 -4 -2 0<br />
Change in GDP Growth \1<br />
\1 Defined as the minimum of the cumulative real GDP growth over the period 2020-22<br />
\2 Defined as the change of the CET1 ratio in year of minimum CET1 ratio and the CET1<br />
ratio in 2019.<br />
Sources: IMF staff calculations.<br />
19
Illustrative Results: Change in CET1 ratios,<br />
Individual <strong>Bank</strong>s<br />
(CET1 ratio at low/high point minus CET1 ratio at end-2019)<br />
CET1 Ratio Changes from Year 0 to Low/High<br />
Point, Unweighted <strong>Bank</strong>-level Distribution<br />
(Percentage points)<br />
20
The GST Tool<br />
• User friendly Excel-based Tool for Area Department Teams of 33 GST countries to<br />
strengthen macrofinancial surveillance<br />
• Instantaneous output: users provide relevant inputs and obtain results instantly<br />
• Flexibility: users can (i) override GST projections or regression coefficients, (ii) provide<br />
input for sectorization of loan losses and policy impact<br />
• Automatic data retrieval: Automatic download and processing of bank and macro data<br />
from centralized data files—data goldmine!<br />
• Differences from previous versions of the GST: (i) subsidiaries now included, (ii)<br />
dynamic vs. static balance sheet assumption<br />
21
Structure of the Tool<br />
Input<br />
Model parameters<br />
Model/Calculations<br />
Output<br />
• Country selection<br />
• Scenarios (baseline<br />
automatically uploaded;<br />
adverse by users)<br />
• Download of bank data (subs<br />
included; starting point)<br />
• Regulatory thresholds<br />
• Optional: Dynamic balance<br />
sheet growth inputs<br />
• Optional: Input external<br />
projections (overrides), PDs,<br />
LGDs for corporate and HH<br />
loan losses and policy impact<br />
• Exposure weights<br />
• Model coefficients<br />
• Other modeling<br />
parameters<br />
• Projections of P&L<br />
components and OCI<br />
• Frye-Jacobs method<br />
to decompose loss<br />
rates into PiT PDs and<br />
LGDs<br />
• Projections of RWAs:<br />
TTC PDs, DT LGDs,<br />
IRB formulas<br />
• Capital projections<br />
• Charts: (i)<br />
aggregate CET1, (ii)<br />
capital shortfall, (iii)<br />
contributions, (iv)<br />
scenarios, (v)<br />
individual bank<br />
historical data and<br />
projections<br />
• Tables: individual<br />
banks’ main<br />
projections<br />
22
Results: Impact on CET1 Ratio (by group of banks or<br />
countries)<br />
I. Capital Ratio Dynamics Over <strong>Stress</strong> <strong>Test</strong>ing Horizon<br />
CET1 Ratio<br />
(In percent)<br />
Baseline Adverse1 Adverse2<br />
III. Contributions Plot<br />
Scenario 1 Baseline OK<br />
Scenario 2 Adverse1 OK<br />
Year 3<br />
25<br />
20<br />
15<br />
10<br />
5<br />
0<br />
Y0 Y1 Y2 Y3 Y4 Y5<br />
Y0 Y1 Y2 Y3 Y4 Y5<br />
Baseline 14.5 12.7 14.7 16.3 17.8 19.3<br />
Adverse1 14.5 10.5 10.0 9.3 <strong>11</strong>.0 12.0<br />
Adverse2 14.5 10.0 9.0 9.8 12.0 14.0<br />
NII: N<br />
NTI:<br />
NFC<br />
OCI:<br />
Othe<br />
RWA<br />
Sources: IMF staff calculations.<br />
NII: Net interest income<br />
NTI: Net trading income<br />
NFCI: Net fees and commission income<br />
OCI: Other comprehensive income<br />
Other: Other income/expense<br />
RWA: Risk weighted assets 23
Illustrative Results: GST Tool Dashboard<br />
24
Current Work: Sectorization of Loan Losses, Assessing<br />
Policy Impact<br />
• HH and corporate stress test<br />
GST<br />
model suites involve household<br />
and firm micro data<br />
PDs,<br />
LGDs<br />
PDs,<br />
LGDs<br />
PDs,<br />
LGDs<br />
• Policy counterfactuals can be<br />
modeled<br />
Household<br />
stress test<br />
Corporate<br />
stress test<br />
• Link to GST for banks: scenarioconditional<br />
PDs and LGDs for HHs<br />
and corporates<br />
25
Extensions – Corporate and Household Sector Modules<br />
Objective: Allow for portfolio breakdown to enhance precision of scenario-conditional bank capital<br />
forecasts + for explicitly reflecting policy support measures such as moratoria (via PDs) and guarantees<br />
(primarily via LGDs).<br />
Micro-macro simulation model for households:<br />
• Structural model rooted in macro and micro data<br />
• Household micro data from HFCS for Europe + other<br />
micro survey databases for countries outside Europe<br />
• Obtain PDs and LGDs for households to link<br />
to banks’ retail portfolios<br />
Source: Gross, Tressel, Ding (<strong>2021</strong>, forthcoming).<br />
Micro-macro simulation model for corporates: Corporate ST presentation.<br />
26
Strengths and Weaknesses of the GST<br />
Strengths<br />
• Consistent methodology for a large number of countries<br />
• Comparably simple methodology: enhance robustness<br />
• Econometric model methodology (BMA): account for model uncertainty<br />
Weaknesses<br />
• Publicly available data limits depth of the model/analysis<br />
• Most notably: currently no portfolio breakdowns<br />
27
advanced stress testing methodologies<br />
advanced stress testing methodologies<br />
Pros and Cons of the Methodology<br />
PROS<br />
CONS<br />
• Cross-country stress test based on<br />
globally consistent macro scenarios<br />
• Main drivers of banks’ financial<br />
statements are modeled in a<br />
consistent manner<br />
• Accounts for model uncertainty<br />
• Increased scalability of the core<br />
engine + computational efficiency<br />
• Absence of granular supervisory,<br />
data limits sophistication of stress<br />
testing methodology<br />
• Aggregate approach limits explicitly<br />
modeling the effects of<br />
sectoral/borrower level policies<br />
• Static balance sheet assumption (as<br />
a starting point)<br />
28
Additional slides: Behind the Scenes<br />
Making the file simple<br />
29
From User Inputs to Outputs<br />
User Settings:<br />
Country, regulatory<br />
capital thresholds,<br />
etc.<br />
Econometric<br />
Model-Based<br />
Projections<br />
Latest WEO Baseline<br />
Scenario Retrieval;<br />
Automated via DMX<br />
Plug-In (Push a<br />
Button)<br />
Frye-Jacobs<br />
Module to<br />
Decompose Loss<br />
Rates to PDs and<br />
LGDs<br />
Balance Sheet<br />
Module<br />
Reporting: Capital<br />
Ratios, Capital<br />
Shortfalls<br />
Up to Two Additional<br />
Adverse Scenarios:<br />
User Input<br />
Risk Weight<br />
Projection Module<br />
30
<strong>Bank</strong> Data Cleaning & Preparation – Process Flow Diagram<br />
Raw data<br />
Vendor data<br />
Flat file<br />
Filter all data for<br />
banks, reporting<br />
years, consolidation<br />
level and accounting<br />
standard within<br />
scope<br />
Collapse accounting<br />
standard dimension<br />
to fill gaps<br />
(permutations or<br />
waterfall approach)<br />
Historical<br />
dataset ready<br />
for further<br />
cleaning<br />
All<strong>Bank</strong>s_cleaned.csv<br />
Historical Data Set<br />
Data Exploration<br />
Post-processing,<br />
filtering & overlays<br />
Quality<br />
Approval<br />
Check<br />
Manually fill<br />
missing data<br />
points<br />
(optional)<br />
Automated Checks<br />
for “critical”<br />
variables<br />
Generation of Data<br />
“flags”<br />
Starting Point<br />
Data Set<br />
B/S & Capital<br />
Projections<br />
Model<br />
estimation<br />
Aggregation & Weighting<br />
Output for Historical<br />
Dashboard<br />
all_check.csv<br />
bank_hist_stats.csv<br />
cnt_hist_stats_aggr<br />
egates.csv<br />
Finalize <strong>Bank</strong> SP Data<br />
Set. List of<br />
Approved <strong>Bank</strong>s and<br />
starting years<br />
Automated postprocessing<br />
edits, empty<br />
data point filling, final SP<br />
post-processing<br />
Final SP Data Set<br />
delivered to the Tool<br />
for BS & Capital<br />
projections<br />
sp.csv<br />
GST<br />
Tool
Scenario Preparation – Process Flow Diagram<br />
Download latest<br />
WEO baseline from<br />
live/published WEO<br />
database<br />
Upload downloaded<br />
WEO data into local<br />
DMX database<br />
Retrieve baseline<br />
projection from<br />
DMX into GST tool<br />
Scenario ready for<br />
further processing<br />
Up to Two Additional<br />
Adverse Scenarios:<br />
User manual input in<br />
GST tool
Econometric Methodology: Panel BMA<br />
• Bayesian Model Averaging (BMA) for panel models, with sign constraints on long-run<br />
multipliers<br />
• Why BMA?<br />
‣ Avoid hand-picking models<br />
‣ Explicit account of model uncertainty → conditional forecasts more robust<br />
• Automatization of estimation: important… estimating 150+ models manually based on noisy<br />
rather low-quality data not quite feasible<br />
• Sign constraints: to make sure that results “make sense”, i.e., coefficient signs in line with<br />
theory, and resulting forecasts conditional on scenarios meaningful<br />
33
Implying PDs and LGDs from NLRs<br />
• Basis: Frye and Jacobs (2012, JoCR)<br />
• Simple model, matching broad behavioral dynamics of LGDs<br />
• More complex models found to not easily outperform this simple model<br />
• FED DFAST uses FJ approach to imply stressed LGDs from stressed<br />
PDs<br />
• In G-ST used instead to decompose (N)LRs to PDs and LGDs