Methods and modelling covers the quantitative toolkit for systemic risk: probability and uncertainty, networks and complex systems, scenarios and stress tests, optimisation and decision analysis, and the discipline of validating each.
Methods and modelling as a field covers the shared mathematical, statistical and engineering methods used to model coupled systems, test them and state their uncertainty. In the Nexus Consortium it is studied as science at GCRI by Nexus Labs and Nexus Foundry as well as understood financially at GRA by Actuarial and Quantitative Risk; this gateway links those platforms and the methods they share.
Nexus Labs, Nexus Foundry, Systemic Risk Management, Systems Science and Modelling
No platform yet in this field.
Systemic risk is tail risk with dependence: the losses that matter come from rare combinations. Methods that assume independence or thin tails understate it. The field's working questions are how to represent dependence (copulas, networks, agent interactions), how to explore deep uncertainty (scenario discovery, robust decision-making), and how to validate models of events that have not happened.
| Problem | Method family | STEM Commons page |
|---|---|---|
| Tail dependence | Extreme value theory, copulas | Extreme value |
| Contagion | Network models | Network science |
| Deep uncertainty | Scenario discovery, robust decision-making | Scenario and stress testing |
| Parameter uncertainty | Bayesian inference, ensembles | Uncertainty quantification |
| Framework or body | What it governs or supplies |
|---|---|
| IEC 31010 | Selection of risk assessment techniques |
| Model risk management practice in finance (for example supervisory model risk guidance) | Model inventory, validation and governance |
| EQUATOR reporting guidelines including TRIPOD | Transparent reporting of prediction models |
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Model | A simplified representation of a system used to understand it or explore its behaviour under stated assumptions. |
| Verification | Checking that a model's code correctly solves the equations and logic intended. |
| Validation | Assessing how accurately a model represents the real system for its intended use. |
| Uncertainty quantification | Characterising and propagating the uncertainties in a model to state confidence in its outputs. |
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