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GRA · Financial Stability

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Scenario design and stress testing for financial stability

Scenario analysis and stress testing explore how institutions and the system would fare under severe but plausible conditions. On this platform they are explained as tools for testing resilience, with attention to scenario choice, behavioural responses and second-round effects.

01Register

Scenario design and stress testing for financial stability on the record.

Method recordSTEM method
  1. MethodScenario design and stress testing
  2. Applied inFinancial Stability
  3. Canonical pageSTEM Commons
02Application

What scenario design and stress testing does here.

Scenario design and stress testing construct plausible, severe future conditions to examine how systems and institutions would perform. They complement probabilistic analysis where data are scarce or futures are deeply uncertain. In financial stability, risk management and systemic risk and capital markets and investment, stress tests reveal vulnerabilities that arise when several risks materialise together. They inform capital, contingency and coordination plans across sectors. The canonical description is on the STEM Commons.

Macro-financial scenario design

Adverse scenarios set paths for growth, unemployment, rates and asset prices, with the narrative and historical calibration stated.

System-wide exercises

Banks, funds, insurers and central counterparties respond to a common shock, and their combined actions are traced to market prices.

Reverse stress tests

Firms identify scenarios that would breach capital or liquidity thresholds, revealing vulnerabilities that standard scenarios miss.

03Stress test

How a shock to assets becomes a system loss.

The equity the system loses after interbank contagion, against the initial hit, as leverage, interconnection and recovery rates change, the core of a macroprudential stress test. Teaching model with stated illustrative values, not a prediction.

Controls

Fall in external assets
2.0%
Leverage (assets over equity)
10 times
Interbank share of assets
10%
Recovery on interbank claims
40%
Banks in the system
20
Core banks
20% of banks

Readouts

Equity lost to the initial shock
18.0%
Equity lost after contagion
45.0%
Added by contagion (DebtRank)
27.0 points
Banks in default
0
Numbers, methods and sources
RoundSystem equity lost (%)Banks in default
018.000
128.800
235.280
339.170
441.500
542.900
643.740
744.240
844.550
944.730
1044.840
1144.900
1244.940
1344.960
1444.980
1544.990
1644.990
1745.000
1845.000
1945.000
2045.000
2145.000
2245.000
2345.000
2445.000
2545.000
2645.000
2745.000
2845.000
2945.000
3045.000

Methods

  1. DebtRank Battiston and others (2012), Scientific Reports 2:541
  2. Differential DebtRank Bardoscia and others (2015), PLOS ONE 10
  3. Contagion in financial networks Gai and Kapadia (2010), Proc. R. Soc. A 466
  4. Clearing in financial networks Eisenberg and Noe (2001), Management Science 47
  5. Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
  6. One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley

Data

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.

How a shock to assets becomes a system loss. Illustrative teaching model: it runs on the values set by the controls, not on measured data. Methods: DebtRank (Battiston and others (2012), Scientific Reports 2:541); Differential DebtRank (Bardoscia and others (2015), PLOS ONE 10); Contagion in financial networks (Gai and Kapadia (2010), Proc. R. Soc. A 466).
04Data

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 in the source register.

DataRoleLabel
Supervisory scenariosShock pathsPublished statistics
Bank and insurer balance sheetsStarting positionsPublished statistics
Satellite modelsLoss translationMODELLED
NGFS scenariosClimate pathwaysMODELLED
05Exposure, 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.

Transmission channels

How a shock in scenario design and stress testing for financial stability reaches credit, market, underwriting, liquidity and operational risk, and feeds back.

Channel structure after the Basel Committee's work on climate-related risk drivers and transmission channels; links shown are typical paths, not estimated exposures.
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
06Validation

How results are checked.

Scenarios are reviewed for plausibility, internal consistency and severity relative to history. Exercise outcomes are compared with actual events when they occur.

CheckHow it is done
Severity benchmarkingScenarios are compared with historical stress episodes
Model challengeLoss models are independently reviewed and back-tested
Conditional framingResults are presented as conditional on the scenario, never as forecasts

Scenario choice can reflect prevailing assumptions and miss novel combinations. Results depend on how participants and models represent responses.

08Stewardship

Who maintains the method.

Systemic Risk Management stewards scenario design and stress testing for the Consortium and reviews how it is applied across platforms.

10Questions

Questions and answers.

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.
Where else is this method applied?
The canonical method page lists every platform that applies it.
ReportsContribute

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Reports is 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.

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