GRA · Financial Stability
Spatial financeWhere capital meets hazard, exposure and transition
Analysis and uncertainty
Contagion and amplification are studied with network, fire-sale and agent-based models, each of which captures one mechanism and misses others. This page sets out what each model captures, what it leaves out and why reconstructed networks need a warning label when bilateral data is not public.
Analysis and uncertainty on the record.
- 02STEM methods5 canonical methods applied
- 03Topics8 subject pages
- 04Counterparts6 platforms in shared fields
- 05PublisherThe Global Risks Alliance
The methods in use.
- 01Financial economics and macro-financeRun incentives in funds and banks · Leverage cycles · Implicit guaranteesApplied here
- 02Network science and contagionInterbank contagion simulation · Common exposure networks · Multilayer networksApplied here
- 03Complex systems and agent-based modellingAgent-based market models · Early warning of regime shifts · Feedback loop mappingApplied here
- 04Scenario design and stress testingMacro-financial scenario design · System-wide exercises · Reverse stress testsApplied here
- 05EconometricsEarly warning indicators · Quantile regression for growth-at-risk · Spillover estimationApplied here
Contagion models and their honesty limits.
| Model | Captures | Misses |
|---|---|---|
| Eisenberg-Noe clearing | Default cascades through bilateral obligations | Liquidity, fire sales, behaviour |
| DebtRank-type centrality | Systemic importance from network position | Loss magnitudes in stress |
| Common-asset fire-sale models | Losses through overlapping portfolios | Strategic behaviour of sellers |
| Agent-based market models | Heterogeneous behaviour and feedback | Calibration is weak |
How the process runs.
Process explorer · Process
Analysis and uncertainty: Catastrophe model pipeline
10 steps·5 stages
Process
Analysis and uncertainty: Catastrophe model pipeline
Catastrophe models usually have hazard, vulnerability, exposure and financial modules, and produce loss tables, average annual loss, exceedance curves, PML and TVaR.
01 Hazard
- 01Event catalogueDataset · Model developerThe hazard module holds a large catalogue of simulated events that represents a wide range of plausible scenarios.
- 03Hazard moduleModel · Model developerFor each event the hazard module gives the local hazard intensity that the vulnerability module reads.
02 Exposure and damage
- 02ExposureDataset · Ministry of financeThe exposure module houses the portfolio data, such as location-specific information.
- 04Vulnerability moduleModel · Model developerThe vulnerability module quantifies the expected damage to the exposures from an event.
03 Financial
- 05Financial moduleModel · Ministry of financeThe financial module calculates losses from all event scenarios. Ground-up, gross and net losses differ as shown below.
- 06Event loss tableDataset · Ministry of financeAn event loss table lists each event with its annual occurrence probability and its loss for the portfolio.
- 07Year loss tableDataset · Ministry of financeSome models also sample simulated years from event losses. This outline does not describe their format.
04 Metrics
- 08Exceedance probability curvesModel · Ministry of financeThe occurrence curve gives the chance that the largest loss in a year exceeds an amount. The aggregate curve does so for the sum of losses.
- 09AAL, PML and TVaRModel · Ministry of financeAverage annual loss sums event probability times loss. PML is one point on the occurrence curve.
05 Use of results
- 10Use of resultsDecision · Ministry of financeThe tail metrics are used when considering a portfolio's exposure and the level of catastrophe reinsurance an insurer requires.
Outline of an external standard or method, from its public sources. It describes what the framework says, not a record of any run. It does not show that Nexus or any partner performs, certifies or approves a step. A gate is a step of the method, never a Nexus approval.
Reconstructed networks need a warning label.
Where bilateral data is unavailable, networks are reconstructed from aggregates by maximum entropy or similar methods, which tend to understate concentration. Results are labelled MODELLED and tested for sensitivity to the reconstruction method. Methods at GCRI network science.
How uncertainty is reported.
- 01
State assumptions
Each analysis lists its assumptions and the data it depends on.
- 02
Quantify where possible
Ranges and confidence are reported alongside central estimates.
- 03
Validate and compare
Models are checked against observations and, where possible, against other models.
- 04
Publish limits
What an analysis cannot show is stated beside what it can.
Where this page's references lead.
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Questions and answers.
Are the methods open?
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