GRA · Financial Stability
Spatial financeWhere capital meets hazard, exposure and transition
Systemic interconnectedness
Financial institutions are linked by loans, deposits, derivatives, securities holdings, common service providers and shared investors. These links spread risk in calm times and transmit losses in stress. Systemic interconnectedness is the property that lets one failure become many. This page sets out how interconnectedness develops and transmits stress, how it is measured, and which authorities and boards use that evidence.
Systemic interconnectedness on the record.
Systemic interconnectedness: how the risk develops.
Banks, insurers and nonbanks linked through exposures. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Direct exposures transmit losses
When a counterparty defaults, its creditors lose. Concentrated bilateral exposures, in interbank loans or derivatives, can push a creditor below its capital buffer and start a chain.
Common exposures correlate losses
Institutions that hold the same assets or lend to the same sectors lose together even without direct links. Diversification at firm level can produce uniformity at system level.
Shared infrastructure concentrates
Central counterparties, custodians, cloud providers and payment systems serve many firms. A failure at one node affects all its users at once.
Opacity amplifies doubt
When counterparties cannot see each other's exposures, rumours of losses lead to withdrawal from all suspected firms. Uncertainty about links acts like contagion even where links are small.
Settlement dependencies, and what fails with a node.
How a failure at one financial centre can spread through settlement links. Design links, not real dependencies.
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 |
|---|---|---|---|---|
| Interbank exposures | currency, by counterparty sector | quarterly | Supervisory large exposure returns | Published statistics |
| G-SIB interconnectedness score | score from intra-financial assets and liabilities and securities outstanding | annual | BCBS G-SIB assessment data | Published statistics |
| Network centrality of institutions | centrality measure, dimensionless | quarterly | Exposure networks | MODELLED |
| Portfolio overlap between institutions | cosine similarity of holdings | quarterly | Securities holdings statistics | ESTIMATED |
| Bank claims on nonbank financial institutions | currency | quarterly | BIS and central bank statistics | Published statistics |
| Concentration of critical service providers | share of firms using top providers | annual | Supervisory surveys | REPORTED |
Systemic risk dashboard.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Systemic risk dashboard
Systemic interconnectedness · Financial system · Committee meeting 3
Vulnerabilities heat map
percentile vs history, 0 to 100
Exposure network
red: most connected node
Credit-to-GDP gap
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 |
|---|---|---|
| BCBS data on the banks in the G-SIB assessment sample | Indicator values used in the G-SIB methodology | Free, BIS terms |
| BIS international banking statistics | Cross-border bank claims by sector and country | Free, BIS terms |
| ECB securities holdings statistics | Holdings of securities by sector in the euro area | Free, ECB reuse terms |
| FFIEC Consolidated Reports of Condition and Income (Call Reports) | United States bank balance sheets | Public domain |
| IMF Global Financial Stability Report | Assessment of system-wide vulnerabilities | Free, IMF terms |
| Office of Financial Research financial stability monitor | Indicators of United States financial stability | Public domain |
Transition and physical risk on a portfolio.
Three scenario families after the NGFS framework move a sector portfolio through carbon costs and warming damages; the transmission view follows the losses to lenders, investors, public finances and households. Scenario: named assumptions about the future, not a forecast. The map on this page shows Europe; the model itself is not tied to a place.
Controls
- Carbon price at the horizon, orderly path
- 200 per tCO2
- Delay before a disorderly transition
- 50% of horizon
- Cost passed to customers
- 50%
- Warming today above pre-industrial
- 1.30°C
- Warming per decade, current policies
- 0.25°C
- Damage coefficient
- 0.24% per °C²
- Years ahead
- 25 years
Readouts
- Worst scenario at the horizon
- −33.6% of value
Numbers, methods and sources
| Year | Orderly transition (% value) | Delayed, disorderly transition (% value) | Current policies (% value) |
|---|---|---|---|
| 0 | −0.38 | −0.38 | −0.38 |
| 3 | −2.87 | −0.53 | −0.67 |
| 6 | −5.36 | −0.68 | −0.97 |
| 9 | −7.84 | −0.84 | −1.27 |
| 12 | −10.33 | −0.99 | −1.57 |
| 15 | −12.82 | −7.12 | −1.87 |
| 18 | −15.31 | −15.05 | −2.17 |
| 21 | −17.80 | −23.00 | −2.48 |
| 24 | −20.29 | −30.94 | −2.79 |
Methods
- Climate scenario families NGFS (2023), Climate Scenarios for central banks and supervisors, Phase IV
- Climate stress test of the financial system Battiston and others (2017), Nature Climate Change 7
- Damage function Nordhaus (2017), Revisiting the social cost of carbon, PNAS 114
- Transmission channels BCBS (2021), Climate-related risk drivers and their transmission channels
- 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
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page's map can draw:
- Urban extents Natural Earth
- Seaports Natural Earth
- Past year, daily temperature (ERA5 via Open-Meteo) Open-Meteo.com (CC BY 4.0), Copernicus ERA5 (C3S/ECMWF) · register open-meteo.public-pages
- Surface air temperature, daytime (AIRS) NASA AIRS · register nasa.gibs.context-live-layers
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
Who decides, and what the evidence serves.
Financial Stability 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 |
|---|---|---|
| Prudential supervisors | Which exposures need large exposure limits or extra capital | Exposure data and network measures with sources |
| Macroprudential authorities | Whether interconnectedness justifies systemic buffers | System-wide exposure and overlap measures |
| Bank and insurer boards | How much concentration to accept in counterparties and providers | Counterparty and provider dependence evidence |
| The Basel Committee and FSB | How the G-SIB framework and lists are set | Indicator data and methodology reviews |
Outputs this topic produces
- Guide to direct, common-exposure and infrastructure channels of contagion
- Method note on network measures and their data requirements
- Brief on reading G-SIB indicators without mistaking them for risk forecasts
- Questions for boards on counterparty and provider concentration
Trace a shock in systemic interconnectedness 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
Shared questions the industry asks.
- 01
What does the network look like?
Who is exposed to whom, through which instruments, and how concentrated the links are.
- 02
Which institutions are central?
Which nodes, if they failed, would affect the most others.
- 03
Do connections stabilise or destabilise?
When links share risk and when they spread it.
- 04
What links are hidden?
Indirect connections through common holdings, shared providers and correlated strategies.
What systemic interconnectedness 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 systemic interconnectedness reaches credit, market, underwriting, liquidity and operational risk, and feeds back.
How the risk arises and transmits.
Network research shows that connectivity has two faces. In normal conditions, links diversify risk and absorb small shocks. Beyond a threshold, the same links transmit large shocks widely, a property sometimes described as robust yet fragile.
Central institutions matter disproportionately. Large dealers, clearing houses and major funding providers sit at the core of many networks; their distress affects many counterparties at once.
Indirect links can exceed direct ones. Institutions that hold similar assets are connected through prices even with no bilateral exposure: if one sells, the others lose.
Shared service providers, such as custodians, cloud platforms and data vendors, create operational links that do not appear in financial exposure data.
Networks change in stress. Counterparties cut links precisely when they are needed, which can isolate institutions and accelerate failure.
How evidence is gathered, and where it stops.
| Method or data | What it shows | Its limit |
|---|---|---|
| Exposure network data | Bilateral links between institutions | Often confidential and incomplete |
| Common holdings analysis | Overlap in portfolios | Holdings data may lag |
| Network metrics | Centrality and concentration | Metrics simplify complex behaviour |
| Simulation | Contagion under assumed shocks | Results depend on behavioural assumptions |
How it is studied, and what the methods cannot say.
- 01Network science and contagionInterbank contagion simulation · Common exposure networks · Multilayer networksApplied here
- 02Financial economics and macro-financeRun incentives in funds and banks · Leverage cycles · Implicit guaranteesApplied here
- 03Complex systems and agent-based modellingAgent-based market models · Early warning of regime shifts · Feedback loop mappingApplied here
Questions and answers.
What makes a bank systemically important?
Why do common exposures matter if banks are not linked?
Can network models predict crises?
Does GRA map any firm's counterparties?
Is more interconnection good or bad?
Why include service providers?
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