Skip to content

Foundry

Global Risks Index

The Global Risks Index Models · Model · on: scenario run requested

运行情景或压力测试

Configure a pathway or a structured shock, run it across entities and draws in Model Lab, propagate it through the coupling network and read the tails.

Follow the run All journeys
页面 GRX-JRN-MODEL-SCENARIO版本 2026 基线版证据截止 2026-09-30就业 7数值 尚未发布方法 v1.0.0-draft
on: scenario run requested · Run a scenario or stress test7 jobs · 1 gate
  1. Choose the scenario family instant
  2. Configure shocks and horizon self-serve
  3. Model runs 3 hours
  4. Coupling propagation 1 hour
  5. Tail and exceedance 1 hour
  6. Results and drivers instant
  7. Analyst sign-off 2 days

01建模

运行情景或压力测试

Configure a pathway or a structured shock, run it across entities and draws in Model Lab, propagate it through the coupling network and read the tails.

on: scenario run requested

运行情景或压力测试

In progressTriggered by Risk analyst7 jobs1 approval gate1 matrix job

流程图
01Design
02Design
03Run
04Propagate
05阅读
06阅读

100%
时间线
Job0 h10 h20 h30 h40 h50 hTarget
Choose the scenario familyYouinstant
Configure shocks and horizonYouinstant
Model runsModel Lab3 h
Coupling propagationDependency network1 h
Tail and exceedanceModel Lab1 h
Results and driversYouinstant
Analyst sign-offYour reviewer2 d

End to end on the longest path: about 2.2 d of proposed target service levels. Gates wait for the people named; bars with a fading end have no fixed length.

步骤
  1. Choose the scenario family

    Design · You · instant
    Warming, socio-economic or transition families · Or a structured shock: fiscal, compound hazard, supply
    Model Lab
  2. Configure shocks and horizon

    Design · You · self-serve
    Shock size, timing and entry points · Horizon to 2030, 2050 or 2100
    Model LabTested model kernels
  3. Model runs matrix · entities × scenarios × draws

    Run · Model Lab · 3 hours
    Kernels from the method library · Seeds recorded per run
    Model LabTested model kernelsMonte Carlo engine
  4. Coupling propagation

    Propagate · Dependency network · 1 hour
    DebtRank and threshold cascades · Cross-border spillovers
    依赖网络经测试的模型内核
  5. Tail and exceedance

    Propagate · Model Lab · 1 hour
    Exceedance curves · 1-in-100 and 1-in-250 losses · Expected shortfall
    蒙特卡罗引擎
  6. Results and drivers

    Read · You · instant
    Fan charts against class bounds · Driver decomposition · Export with citation
    Model LabProvenance (W3C PROV)
  7. Analyst sign-off approval

    Read · Your reviewer · 2 days
    Assumptions and limits recorded
    PostgreSQL 账本（行级安全）
产出物
产出物 作业 执行方 阶段
Scenario spec Choose the scenario family 您 Design
Run configuration Configure shocks and horizon 您 Design
Run record with seed and digest Model runs Model Lab Run
Cascade paths Coupling propagation 依赖网络 Propagate
Tail report Tail and exceedance Model Lab Propagate
Results pack Results and drivers 您 阅读
Sign-off Analyst sign-off Your reviewer 阅读
已完成进行中等待批准排队中审批关口矩阵作业当前位置
Configure a pathway or a structured shock, run it across entities and draws in Model Lab, propagate it through the coupling network and read the tails. The canvas shows where a request stands at the marked job; it is illustrative, not the status of a real request. Durations are proposed target service levels. Select a job for its steps, the people who act, the technology it runs on and what it produces.
02面向谁

Solutions that use this journey

公共部门

多边机构和开发银行

Screen country and project context, read fiscal resilience and protection gaps, and design pre-arranged finance.

See the solution Public sector

中央银行与监管机构

设计监管情景，解读系统性耦合，复现方法。

See the solution Enterprise

保险与再保险公司

Read hazard, exposure and protection gaps, run tails and study parametric index design.

See the solution Public sector

城市与基础设施运营方

Read hexagon-level hazard and dependency layers for planning, investment and continuity.

See the solution

03技术

6 components

Each job names the component it runs on; open a job on the canvas for its steps, checks and artefacts.

Model LabTested model kernelsMonte Carlo engineDependency networkProvenance (W3C PROV)PostgreSQL ledger (row-level security)

Part of NXS-GRX (canonical data, provenance and risk) Models: Scenario pathways, stress tests, tail and exceedance analytics, coupling contagion and forecasting on the open method library, with tests and model cards.

Start this journey

Talk to the Secretariat to start this journey with your team.

联系

状态

The Global Risks Index 的方法、结构和定义为预备性工作草案（2026），供审阅和评论，不供实施。在从受治理的证据截取中发布版本之前，不会出现任何数值；届时数值将附有区间、类别、来源及其更正途径。模拟器以您选择的数字运行已发布的方法，绝不显示 NXS-GRX 数值。

NXS-GRX 不是什么

NXS-GRX 是研究和风险分析信息。它不是信用评级或 ESG 评级，不使用任何评级尺度；它不是金融工具的基准，不是官方统计，不是紧急警报或官方分类，也不是建议。不得用于评估自然人。风险等级是已发布尺度上的区间，绝不是对国家或个人的排名。

机构与来源

The Nexus Consortium is stewarded by three independent not-for-profit institutions: The Global Centre for Risk and Innovation (GCRI), through GCRI US in Washington, DC and GCRI Canada in Ontario, with GCRI Canada as Lead Institution, Consortium Secretariat and lead contracting organisation; The Global Risks Forum (GRF); and The Global Risks Alliance (GRA). Technology, infrastructure and service providers work with the Consortium at arm’s length under written agreements, like every other external party in the public-private-planet model, and hold no role in its stewardship.

每个数值均附有其来源的署名，这些来源保留各自的许可。外部名称仅为互操作性而引用。Nexus 撰写的文本拟在发布时采用 CC BY 4.0。

Avis en français

La méthodologie et les définitions du NXS-GRX sont un projet de travail préparatoire (2026). Le NXS-GRX est une information de recherche et d’analyse des risques : ni notation, ni indice de référence financier, ni statistique officielle, ni alerte, ni conseil. Les classes de risque sont des bandes sur une échelle publiée, non des classements.