GCRI · Systemic Risk Management
Scenario and stress testing
A stress test asks what happens to an organisation, system or plan under a severe but plausible event, before a real shock answers the question. Scenarios describe how such events might unfold; stress tests measure the effect on capital, capacity, services or people. This page sets out how scenarios are designed, how stress tests propagate shocks through dependencies, and how results serve the boards, ministries and supervisors that must act on them.
Scenario and stress testing on the record.
Scenario and stress testing: how the risk develops.
Challenging plans and assumptions. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Scenarios set the question
A scenario fixes the trigger, its severity, its timing and the conditions around it. Choosing only familiar or mild scenarios produces reassuring results that say little about real vulnerability.
Shocks travel through the model
The stress spreads through dependencies represented in the test: suppliers, counterparties, infrastructure and staff. What the model leaves out cannot fail, so omissions bias results toward resilience.
Responses change the outcome
Institutions react during a crisis by selling assets, cutting services or drawing on reserves, and those reactions affect others. Tests that hold behaviour fixed miss these second-round effects.
Reverse tests find breaking points
Reverse stress testing starts from failure and asks which combinations of events would cause it. It reveals vulnerabilities that forward scenarios chosen in advance may never reach.
Hazards and exposure in Asia.
Daytime surface heat, population density and flood water over Asia, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Hazards and exposure, for this page
About this layer
- Evidence
- Observed (persistent cloud still leaves gaps)
- Source
- VIIRS NOAA-20 Level 3, 8-day (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- 8-day; Satellite 8-day composite; persistent cloud still leaves gaps
- Newest at build
- 2026-09-14
- Resolution
- about 1 km grid
- Uncertainty
- Satellite skin temperature of the ground over the 8 days ending on the date shown, a heat exposure indicator; not air temperature, heat index or a heat warning.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Estimated
- Source
- Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
- Provider
- NASA SEDAC (CIESIN, Columbia University)
- Licence
- CC BY 4.0
- Vintage
- null; Census-based estimate, static (2020)
- Resolution
- about 1 km grid
- Uncertainty
- Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- VIIRS Global Flood Product (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite detection; clouds leave gaps
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite detection; clouds leave gaps. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS NOAA-20 corrected reflectance (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite image
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Estimated
- Source
- GPM IMERG (NASA GES DISC)
- Provider
- NASA GES DISC
- Licence
- NASA open data
- Vintage
- daily; Satellite estimate, near real time
- Newest at build
- 2026-10-12
- Resolution
- about 2 km grid
- Uncertainty
- Satellite estimate, near real time. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- GHRSST MUR25 (NASA JPL PO.DAAC)
- Provider
- NASA JPL PO.DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite analysis
- Newest at build
- 2026-10-12
- Resolution
- about 2 km grid
- Uncertainty
- Satellite analysis of how far sea temperature departs from its usual value for the date shown; a marine heat indicator, not a coral bleaching alert.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Hazards and exposure, framed on Asia.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · U.S. Geological Survey · NASA JPL OPERA DIST-ALERTSources and licences
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- GPM IMERG precipitation, NASA Precipitation Processing System; distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- GHRSST Level 4 MUR analyses, NASA Jet Propulsion Laboratory; distributed by the NASA Physical Oceanography DAAC (PO.DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- Live: U.S. Geological Survey Events are indicative, not authority warnings.
- Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- River discharge forecast, 30 days with range (GloFAS via Open-Meteo): Contains modified Copernicus Emergency Management Service information 2026; not a flood warning.
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (11 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Precipitation rate, daily mean: GPM IMERG, NASA GES DISC · 12 Oct 2026
- Sea surface temperature anomaly: GHRSST MUR25, NASA JPL PO.DAAC · 12 Oct 2026
Stress tests that cross domains test the joins, not the parts.
- EnergyCouplingEnergy price and supply shocks are standard stress cases.What to watchFuel and power supply assumptions
- FoodCouplingExport restrictions and price spikes test import-dependent food systems.What to watchImport dependence and stocks
- WaterCouplingMulti-year drought tests water, power and farm systems together.What to watchDrought duration in the scenario
Scenario work is designed for the Scenario and Stress-Test series, always labelled as scenario evidence. Financial supervisors use the NGFS scenarios for climate stress testing; national cross-sector tests need storylines built with each sector’s experts.
- Storylines with the assumptions each sector’s experts checked.
- Results of past stress tests a public body has published.
Stocks, flows, delays and a tipping threshold.
Two stocks, pressure and adaptive capacity, joined by a reinforcing growth loop, a delayed balancing response and damage when pressure outruns capacity; past the threshold the system can tip. Teaching model with stated illustrative values, not a prediction. The map on this page shows Asia; the model itself is not tied to a place.
Controls
- Growth of pressure
- 6.0% a year
- Efficacy of capacity in reducing pressure
- 4.0% a year
- Investment response to perceived pressure
- 20%
- Perception delay
- 4.0 years
- Tipping threshold of pressure
- 60 index
- Amplification past the threshold
- 2.0 times
- Years simulated
- 50 years
Readouts
- Peak pressure
- 113 index
- Capacity at the end
- 186 index
- Years over the threshold
- 15.8 years
Numbers, methods and sources
| Year | Pressure | Capacity | Perceived |
|---|---|---|---|
| 0 | 20.0 | 50.0 | 20.0 |
| 4 | 23.3 | 44.9 | 21.2 |
| 8 | 27.2 | 42.2 | 24.0 |
| 12 | 31.7 | 42.3 | 27.7 |
| 16 | 36.8 | 45.5 | 32.2 |
| 20 | 42.3 | 51.5 | 37.2 |
| 24 | 47.9 | 60.3 | 42.6 |
| 28 | 53.2 | 71.3 | 48.0 |
| 32 | 57.8 | 84.3 | 53.1 |
| 36 | 66.4 | 98.4 | 58.4 |
| 40 | 82.7 | 115.9 | 69.7 |
| 44 | 97.8 | 139.7 | 83.8 |
| 48 | 109.1 | 169.4 | 97.3 |
Methods
- System dynamics Forrester (1961), Industrial Dynamics, MIT Press; Sterman (2000), Business Dynamics
- Critical transitions and tipping points Scheffer and others (2009), Early-warning signals for critical transitions, Nature 461
- Causal loop diagram Sterman (2000), Business Dynamics, McGraw-Hill
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- 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.
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Systemic Risk Management in Foundry
- Tested at Systemic Risk Management in Universe
- Codified in Systemic Risks in Standards
- Published in Systemic Risk Management in Reports
- Taught in Faculty of Risk Science and Methods in Academy
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Scenarios tested per cycle | count by hazard type | annual | Stress testing programme records | REPORTED |
| Capacity or capital shortfall under stress | units of the tested resource | per scenario | Stress test outputs | MODELLED |
| Time to breach a critical threshold | days | per scenario | Stress test outputs | MODELLED |
| Dependencies represented in the test | percent of mapped critical dependencies | per test | Test design documentation | DECLARED |
| Actions taken after the test | count of management actions recorded | annual | Follow-up records | REPORTED |
| Reverse stress test conditions identified | count of failure conditions | per test | Reverse stress test records | MODELLED |
Model scenario and stress testing as a system. Run the cascade.
Assembled from the Nexus Standards area profile Global and systemic risk: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Dam failure
- Financial contagion
- River flood (set off downstream)
- Flash flood (set off downstream)
- Mass displacement (set off downstream)
- Widespread power outage (set off downstream)
- Water supply shortfall (set off downstream)
- Sovereign debt crisis (set off downstream)
- Currency crisis (set off downstream)
Systems and couplings
- Global and systemic risk
- Compound and cascading hazard standards Mechanism
- Financial stability and prudential risk Mechanism
- Supply chain and trade risk Cascade
- Future generations and long-term risk Common driver
Controls
NS-0Constitution and principlesNS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-14Conformance and crosswalk to external standardsNRS-H120Hazard family: Structural fragility of interconnected systems (NHS-1 sys.fragility)NRS-C6Systemic and cascading riskNRS-H61Hazard family: Cascading failure (NHS-1 sys.cascade)NRS-H62Hazard family: Compound and multi-hazard events (NHS-1 sys.compound)NRS-H94Hazard family: Systemic cyber events (NHS-1 cyber.systemic-cyber)NRS-H60Hazard family: Supply-chain and flow disruption (NHS-1 sys.supply-chain)
Observation
- INFORM Risk Index (listed for reference)
- World Bank Open Data and World Development Indicators API (listed for reference)
- IMF data services (WEO, IFS, FSI via SDMX) (listed for reference)
- Direct economic loss attributed to disasters in relation to global GDP
- Macroeconomic Dashboard
Example geography
- Bangladesh: 134 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Surface soil moisture (SMAP), NASA GIBS
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| NGFS climate scenarios | Reference climate transition and physical risk pathways for financial and policy stress tests | Open, attribution, NGFS terms of use |
| IPCC Shared Socioeconomic Pathways and the scenario database | Long-term socioeconomic and emissions pathways | Open, licence per database (to check) |
| Published supervisory stress test methodologies and results | Scenario design and aggregate outcomes from central banks and supervisors | Public records, terms per authority |
| National risk assessment reasonable worst-case scenarios | Government-published planning scenarios for civil contingencies | Public records, licence per government |
| UNDRR guidance on scenario-based exercises | Design guidance for exercises that test preparedness | Public, UN terms of use |
How scenario and stress testing is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
Model, equations and assumptions
The model structure used for scenario and stress testing, the assumptions it makes and how each is tested.
Ri = f(Hi, Ei, Vi, Ci)hazard, exposure, vulnerability and coping capacityEAL = ∑k pk · Lkover events k with annual probability pkxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| Assumption | Why it is made | How it is tested |
|---|---|---|
| Hazard and exposure are independent within an event | Keeps the loss integral tractable | Compare with a copula fit on joint records |
| Couplings are linear near the operating point | Lets shocks propagate through A | Perturb beyond the linear range and compare |
| Vulnerability curves transfer between places | Local curves are rarely available | Validate against observed losses where they exist |
- Inputs
- versioned datasets with provenance
- Outputs
- distributions, never single numbers
- Code
- released with the model card and tests
- Reproduction
- same data, code, environment and seed
Who decides, and what the evidence serves.
Systemic Risk Management prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Boards and executive committees | Whether buffers and plans are adequate for severe but plausible events | Shortfalls and times to breach by scenario; reverse stress conditions |
| Central banks and financial supervisors | Which scenarios to set for supervisory exercises | Scenario libraries; cross-sector dependency evidence |
| Civil contingency and emergency planners | Which planning assumptions to use for national capabilities | Reasonable worst-case scenarios and their basis |
| Ministries of finance | How much fiscal space to hold for contingent liabilities | Public sector stress tests with assumptions stated |
Outputs this topic produces
- Scenario libraries with triggers, severity and assumptions documented
- Stress test designs that represent cross-sector dependencies
- Reverse stress test protocols for public services and organisations
- Exercise reports prepared for review by the institution tested
What scenario designers ask.
- Is the scenario severe enough to be informative, yet plausible enough to be taken seriously?
- Does it combine shocks that are likely to occur together, rather than treating them as independent?
- Does it include the reactions of other actors, which can amplify or damp the shock?
- What would the organisation actually do, and is that response realistic under stress?
Why scenarios reveal what forecasts miss.
Forecasts aim for the most likely outcome and tend to understate extremes. Plans built on them fail when the unusual happens. Scenarios deliberately explore the space of outcomes, including combinations that historical data has not yet shown. Reverse stress testing works backwards: it starts from failure and asks what would have to happen to cause it, which often exposes vulnerabilities that forward scenarios miss.
Stress tests reveal not only losses but behaviour: which decisions would be needed, who would make them, what information they would need and how long it would take. Many failures in real crises come from slow or unclear decisions rather than from the size of the initial shock.
How scenarios and stress tests are built.
- 01
Narrative scenarios
Qualitative stories built by diverse groups around key uncertainties. Limits: can be shaped by group consensus and familiar fears.
- 02
Quantified stress scenarios
Consistent sets of shocks to model inputs, designed with scenario design and stress testing methods. Limits: model results depend on how the model represents behaviour under stress.
- 03
Reverse stress tests
Identify scenarios that would cause failure. Limits: many paths can lead to failure, and choosing which to study requires judgement.
- 04
Exercises
Tabletop and live exercises test decisions and coordination. Limits: participants know it is an exercise.
How it is studied, and what the methods cannot say.
- 01Scenario design and stress testingCross-sector scenario design · Reverse stress testing · Second-round effectsApplied here
- 02Network science and contagionDependency network mapping · Centrality and concentration · Failure propagation simulationApplied here
- 03Uncertainty quantification and model validationPropagating input uncertainty · Separating kinds of uncertainty · Deep uncertainty framingApplied here
- 04Probability and statisticsFrequency and severity modelling · Dependence between risks · Combining data and judgementApplied here
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
Questions and answers.
What is a stress test?
What is reverse stress testing?
Is a scenario a prediction?
Is a stress test a prediction?
How severe should a scenario be?
Can the platform design a stress test for my organisation?
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