Spatial financeWhere capital meets hazard, exposure and transition
Network science and contagion for financial stability
Network science represents the financial system as institutions linked by exposures, funding and shared infrastructure. On this platform it maps who is connected to whom, finds nodes whose failure would spread furthest, and tests how losses travel under assumed shocks.
Network science and contagion study how the structure of connections among entities shapes the spread of failures, information and disease. It provides tools to analyse interdependent infrastructure, supply chains and financial systems. In financial stability, risk management and systemic risk and capital markets and investment, network science identifies critical nodes and pathways through which shocks propagate across sectors. It informs targeted protection and diversification to limit cascades. The canonical description is on the STEM Commons.
Interbank contagion simulation
Default cascades are simulated on exposure networks to show how losses travel and which links matter most, with the treatment of missing data stated.
Common exposure networks
Institutions are linked by overlapping portfolios to estimate fire-sale spillovers between holders of the same assets.
Multilayer networks
Lending, derivatives and payment links are combined in layered networks to show how stress moves between channels.
03Spatial finance
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.
Sixteen financial centres, and how a failure at one spreads through settlement links.Design links, not the real dependencies of any infrastructure.
04Data
What the method consumes.
Datasets and records used by network science and contagion on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
Data
Role
Label
Large exposure returns
Direct links
Published statistics
Securities holdings statistics
Common exposures
Published statistics
Payment system data
Liquidity flows
OBSERVED
Derivatives trade repository data
Counterparty links
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.
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).
Models are checked by reproducing observed cascades and outbreaks. Robustness of results to missing or uncertain links is tested.
Check
How it is done
Missing link sensitivity
Results are compared across methods for filling unobserved exposures
Behavioural assumptions
Recovery rates and reactions are varied and their effect reported
Stability of rankings
Centrality rankings are checked for stability across periods
Network data are often incomplete or confidential. Static network models may miss adaptive behaviour during cascades.
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.
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 network science and contagion 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
10Stewardship
Who maintains the method.
Systems Science and Modelling stewards network science and contagion for the Consortium and reviews how it is applied across platforms.
What are the limits of network science and contagion in this field?
Network data are often incomplete or confidential. Static network models may miss adaptive behaviour during cascades.
How are results checked?
Models are checked by reproducing observed cascades and outbreaks. Robustness of results to missing or uncertain links is tested.
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.
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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