Agent-based market models
Heterogeneous traders, funds and dealers are simulated to study how margin rules, leverage limits and liquidity tools change the dynamics of stress.
GRA · Financial Stability
Spatial financeWhere capital meets hazard, exposure and transition
Complex systems and agent-based modelling for financial stability
Complex systems science studies how interactions among many agents produce system behaviour that no single agent intends: tipping points, feedback loops and sudden regime shifts. On this platform it explains why the financial system can move from calm to crisis quickly.
01Register
Complex systems and agent-based modelling for financial stability on the record.
Method recordSTEM method
- MethodComplex systems and agent-based modelling
- Applied inFinancial Stability
- Data3 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
02Application
What complex systems and agent-based modelling does here.
Complex systems and agent-based modelling study systems whose behaviour emerges from interactions among many components. They help explain nonlinear dynamics, tipping points and collective behaviour. In financial stability, risk management and systemic risk and capital markets and investment, complex systems approaches explore how interacting risks can produce sudden, systemic shifts. They support stress testing of policies against emergent behaviour. The canonical description is on the STEM Commons.
Early warning of regime shifts
Indicators such as rising autocorrelation and variance are tested for whether they precede transitions in financial time series.
Feedback loop mapping
Causal loop diagrams show how leverage, prices, margins and funding reinforce each other, as a basis for scenario design.
03Geography
Activity and exposure in Europe.
Population density, night lights and flood water over Europe, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Activity and exposure, for this page
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.
Activity and exposure, framed on Europe.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 · CIESIN/Columbia, NASA SEDAC HBASESources 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
- 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
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. 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
- NASA Fire Information for Resource Management System (FIRMS), VIIRS 375 m active fire product, NASA LANCE; vector tiles via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- 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
- 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.
- Context: CIESIN/Columbia, NASA SEDAC HBASE. Center for International Earth Science Information Network (CIESIN), Columbia University, and NASA Socioeconomic Data and Applications Center (SEDAC): Global Human Built-up And Settlement Extent (HBASE) 2010; Seto, Guneralp and Hutyra 2012, Global Grid of Probabilities of Urban Expansion to 2030; Global Urban Heat Island (UHI) Data Set, 2013.
- 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).
- Optional layers (5 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
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
- Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
- Active fires and thermal anomalies: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
- Precipitation rate, daily mean: GPM IMERG, NASA GES DISC · 12 Oct 2026
04Data
What the method consumes.
Datasets and records used by complex systems and agent-based modelling on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Market microstructure data | Model calibration | OBSERVED |
| Historical crisis episodes | Validation | REPORTED |
| Supervisory data | Agent parameters | Published statistics |
05Stress 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. The map on this page shows Europe; the model itself is not tied to a place.
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
| Round | System equity lost (%) | Banks in default |
|---|---|---|
| 0 | 18.00 | 0 |
| 1 | 28.80 | 0 |
| 2 | 35.28 | 0 |
| 3 | 39.17 | 0 |
| 4 | 41.50 | 0 |
| 5 | 42.90 | 0 |
| 6 | 43.74 | 0 |
| 7 | 44.24 | 0 |
| 8 | 44.55 | 0 |
| 9 | 44.73 | 0 |
| 10 | 44.84 | 0 |
| 11 | 44.90 | 0 |
| 12 | 44.94 | 0 |
| 13 | 44.96 | 0 |
| 14 | 44.98 | 0 |
| 15 | 44.99 | 0 |
| 16 | 44.99 | 0 |
| 17 | 45.00 | 0 |
| 18 | 45.00 | 0 |
| 19 | 45.00 | 0 |
| 20 | 45.00 | 0 |
| 21 | 45.00 | 0 |
| 22 | 45.00 | 0 |
| 23 | 45.00 | 0 |
| 24 | 45.00 | 0 |
| 25 | 45.00 | 0 |
| 26 | 45.00 | 0 |
| 27 | 45.00 | 0 |
| 28 | 45.00 | 0 |
| 29 | 45.00 | 0 |
| 30 | 45.00 | 0 |
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
- Clearing in financial networks Eisenberg and Noe (2001), Management Science 47
- 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 Financial Stability in Foundry
- Tested at Financial Stability in Universe
- Codified in Finance, Insurance and the Economy in Standards
- Published in Financial Stability in Reports
- Taught in Faculty of Finance and Economics in Academy
06Validation
How results are checked.
Models are evaluated by matching multiple stylised facts and patterns at different scales. Sensitivity and robustness analysis test dependence on assumptions.
| Check | How it is done |
|---|---|
| Stylised facts | Models must reproduce known features such as fat tails and volatility clustering |
| Parameter sensitivity | Results are reported across plausible parameter ranges |
| Purpose stated | Each model states whether it explores mechanisms or supports quantitative decisions |
Many plausible models can reproduce the same patterns. Calibration and validation data are often limited.
07Twin and cascade simulator
Trace a shock to the balance sheet.
Assembled from the Nexus Standards area profile Financial stability and prudential risk: the shocks it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Pick an initiating shock and step through the cascade to see which exposures it reaches and where losses could concentrate. The Map view places the run on an example geography with open data. Every weight is a stated assumption. An exercise, not a forecast, a valuation, a rating or investment advice. To model a place of your own choosing, open the full System Builder.
Hazards
- Dam failure
- 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)
Systems and couplings
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-H62Hazard family: Compound and multi-hazard events (NHS-1 sys.compound)NRS-H113Hazard family: Financial-system fragility (NHS-1 soc.financial-system)NRS-H118Hazard family: Organised crime and illicit economies (NHS-1 soc.crime)NRS-H79Hazard family: Mental health crises (NHS-1 health.mental)NRS-U9Universe platform profile: Financial Regulation NexusNRS-C5Scenarios and stress testing
Observation
- BIS Data Portal (credit, debt service, property prices, banking statistics) (listed for reference)
- IMF data services (WEO, IFS, FSI via SDMX) (listed for reference)
- OECD Data Explorer (SDMX API) (listed for reference)
- Financial Soundness Indicators
- Macroeconomic Dashboard
Example geography
- Western Europe: 152 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
08Connections
Topics that rely on this method.
09Exposure, 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 complex systems and agent-based modelling for financial stability reaches credit, market, underwriting, liquidity and operational risk, and feeds back.
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
10Stewardship
Who maintains the method.
Systems Science and Modelling stewards complex systems and agent-based modelling for the Consortium and reviews how it is applied across platforms.
11Connected
Where this page leads across the Consortium.
12Questions
Questions and answers.
What are the limits of complex systems and agent-based modelling in this field?
Many plausible models can reproduce the same patterns. Calibration and validation data are often limited.
How are results checked?
Models are evaluated by matching multiple stylised facts and patterns at different scales. Sensitivity and robustness analysis test dependence on assumptions.
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
Contribute evidence from this page.
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.
Global Risks AllianceFinancial StabilityScenario and Stress-TestGlobal Systems Risk and Resilience
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Legal
Legal documents.
Maps, data and figures on this site are shown for context and general information. They are not advice, forecasts or warnings; the limits of the information are set out in the Disclaimers and Data Notices.