Spatial financeWhere capital meets hazard, exposure and transition
Scenario design and stress testing for public finance and sovereign debt
Scenario analysis and stress testing show how public debt and fiscal balances would evolve under adverse conditions. On this platform they explain the stress tests indebt sustainabilityframeworks,contingent liabilityshocks and climate scenarios for public finances.
What scenario design and stress testing does here.
Scenario design and stress testing construct plausible, severe future conditions to examine howسیستەمەکانand institutions would perform. They complement probabilistic analysis where data are scarce or futures are deeply uncertain. In sovereign and public finance, development finance and disaster and crisis, stress tests reveal vulnerabilities that arise when several risks materialise together. They inform capital, contingency and coordination plans across sectors. The canonical description is onthe STEM Commons.
Standard DSA stress tests
Shocks to growth, interest rates, exchange rates and the primary balance are applied to debt projections, as in the IMF and World Bank frameworks.
Contingent liability shocks
Scenarios where guarantees are called or state enterprises need support are added to debt paths to show their potential effect.
Climate and disaster scenarios
Disaster shocks and transition paths are introduced into fiscal projections, with assumptions and uncertainty documented for users.
03چاودێری
Scenario stress.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Scenario stress
Public finance and sovereign debt · Scenario design and stress testing · Run R-2370
Severe case breaches limitGRA · Public Finance and Sovereign Debt
Scenario set
بنەڕەت
Central
22
لەناو
Adverse
1-in-25
49
داخستن
توند
1-in-100
76
Breach
Reverse stress
Find the break
100
Defines edge
Method card
ڕێگا
دیزاینی سیناریۆ و تاقیکردنەوەی گوشار
گێڕانەوە
compound shock
Horizon
3 years
ناونیشان
مۆدێلکراو
Impact build-up, severe case
Scenario stress: the screen’s structure, terms and states for Public finance and sovereign debt.ناو و بەهای نموونەیی؛ دیزاینێکە، نەک تۆمار یان سیستەمی ڕاستەوخۆ.
04داتا
What the method consumes.
Datasets and records used by scenario design and stress testing on this platform, with the role each plays and the label its values carry. Sources are listed inthe source register.
Sovereign debt paths and the odds of breaching a threshold.
The distribution of debt paths, and the share above a threshold of concern at the horizon, that sovereign investors and rating analysts read from a fan chart. Teaching model with stated illustrative values, not a prediction.
Median, 25% to 75% and 10% to 90% bands
No shocks
کۆنترۆڵەکان
Debt today
70% of GDP
Effective nominal interest rate
5.0%
Nominal GDP growth
4.5%
Primary balance
−0.5% of GDP
Volatility of growth
2.0 points
Volatility of the interest rate
1.0 points
ساڵەکانی داهاتوو
10 years
Threshold of concern
90% of GDP
خوێندنەوەکان
Median debt at the horizon
79% of GDP
Paths above the threshold at the horizon
3%
Primary balance that stabilises debt
0.3% of GDP
ژمارە، ڕێگا و سەرچاوەکان
ساڵ
10%
25%
Median
75%
90%
0
70.0
70.0
70.0
70.0
70.0
1
68.8
69.8
70.9
71.9
72.8
2
68.7
70.2
71.6
73.4
74.9
3
69.2
70.8
72.5
74.7
76.5
4
69.3
71.3
73.2
75.4
77.9
5
69.8
71.6
74.2
76.6
79.0
6
70.3
72.5
74.9
77.8
80.3
7
70.5
73.1
75.7
79.0
82.0
8
71.0
73.6
76.6
80.0
83.1
9
71.6
74.2
77.8
81.0
84.7
10
72.0
75.0
78.7
82.4
85.9
ڕێگاکان
Debt dynamics and the interest-growth differentialBlanchard (2019), Public debt and low interest rates, American Economic Review 109
Stochastic fan chartsCelasun, Debrun and Ostry (2006), IMF Staff Papers 53
Sovereign risk and debt sustainability frameworkIMF (2022), Staff Guidance Note on the SRDSF
داتا
No observed data enters this model: it runs on the teaching values set by the controls, to be replaced with a programme’s own evidence.
Sovereign debt paths and the odds of breaching a threshold. Illustrative teaching model: it runs on the values set by the controls, not on measured data. Methods: Debt dynamics and the interest-growth differential (Blanchard (2019), Public debt and low interest rates, American Economic Review 109); Stochastic fan charts (Celasun, Debrun and Ostry (2006), IMF Staff Papers 53); Sovereign risk and debt sustainability framework (IMF (2022), Staff Guidance Note on the SRDSF).
Scenarios are reviewed for plausibility, internal consistency and severity relative to history. Exercise outcomes are compared with actual events when they occur.
بپشکنە
How it is done
Severity calibration
Shocks are calibrated to historical experience
Combined shocks
Combined scenarios are run, not only single shocks
Conditional framing
Results are presented as conditional, not forecasts
Scenario choice can reflect prevailing assumptions and miss novel combinations. Results depend on how participants and models represent responses.
07Exposure, loss and capital
What it means for exposure, loss and capital.
The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.
Capital, solvency and fiscal indicators
The indicators a risk officer, supervisor or ministry of finance reads first on scenario design and stress testing for public finance and sovereign debt.
Indicator
Unit
Who reads it
Framework
Public debt
% of GDP
Ministries of finance, IMF
Debt Sustainability Framework
Gross financing needs
% of GDP per year
Ministries of finance, investors
Debt Sustainability Framework
Contingent liabilities
% of GDP, by source
Ministries of finance
Fiscal risk statements
Interest to revenue
% of revenue
Ministries of finance, rating users
Public finance statistics
Sovereign spread
basis points over a benchmark
Investors, debt managers
Market data
CET1 ratio
% of risk-weighted assets
Banks, supervisors
Basel framework
Liquidity coverage ratio
% of stressed outflows
Banks, central banks
Basel framework
Source: the frameworks named in each row. Indicator names, units and frameworks only; values belong to each institution’s own reporting and are never shown here.
NGFS climate scenarios · Network for Greening the Financial SystemIFRS S2 Climate-related Disclosures · ISSBTCFD recommendations · Financial Stability Board TCFDPrinciples for the effective management of climate-related financial risks · Basel Committee on Banking Supervision
What are the limits of scenario design and stress testing in this field?
Scenario choice can reflect prevailing assumptions and miss novel combinations. Results depend on how participants and models represent responses.
How are results checked?
Scenarios are reviewed for plausibility, internal consistency and severity relative to history. Exercise outcomes are compared with actual events when they occur.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Reportsis the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
Global Risks Allianceداراییی گشتی و قەرزی سەروەرانەئامادەیی دارایی و بیمەمەترسی و خۆڕاگریی سیستەمی وڵات
Editors and the expert reviewers named on each article decide what we publish. Funders and partners do not review or approve articles before publication.
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