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Global Risks Index

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

시나리오 또는 스트레스 테스트 실행

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
페이지 GRX-JRN-MODEL-SCENARIO판 2026 기준판근거 마감 2026-09-30일자리 7값 미게시방법 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

01모델링

시나리오 또는 스트레스 테스트 실행

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

시나리오 또는 스트레스 테스트 실행

In progressTriggered by Risk analyst7 jobs1 approval gate1 matrix job

그래프
01Design
02Design
03Run
04Propagate
05읽기
06읽기

100%
타임라인
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.

단계
  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
    의존 네트워크시험을 거친 모델 커널
  5. Tail and exceedance

    Propagate · Model Lab · 1 hour
    Exceedance curves · 1-in-100 and 1-in-250 losses · Expected shortfall
    몬테카를로 엔진
  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 원장(행 수준 보안)
산출물
산출물 작업 담당 단계
Scenario spec Choose the scenario family 나 Design
Run configuration Configure shocks and horizon 나 Design
Run record with seed and digest Model runs Model Lab Run
Cascade paths Coupling propagation 의존 네트워크 Propagate
Tail report Tail and exceedance Model Lab Propagate
Results pack Results and drivers 나 읽기
Sign-off Analyst sign-off Your reviewer 읽기
완료진행 중승인 대기 중대기 중승인 게이트매트릭스 작업현재 위치
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.
02대상

Solutions that use this journey

공공 부문

다자기구 및 개발은행

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

See the solution Public sector

중앙은행과 감독기관

감독 시나리오를 설계하고, 시스템 결합을 확인하며, 방법을 재현합니다.

See the solution Enterprise

보험사와 재보험사

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

See the solution Public sector

도시와 인프라 운영자

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

See the solution

03기술

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.

문의

상태

Global Risks Index의 방법론, 구조, 정의는 검토와 의견 수렴을 위해 배포된 예비 작업 초안(2026)이며 이행용이 아닙니다. 관리되는 근거 마감에 따라 판이 공개되기 전까지는 어떠한 값도 표시되지 않으며, 공개 후 값에는 구간, 등급, 출처, 정정 경로가 함께 표시됩니다. 시뮬레이터는 귀하가 선택한 숫자에 공개된 방법을 적용할 뿐이며 NXS-GRX 값을 표시하지 않습니다.

NXS-GRX가 아닌 것

NXS-GRX는 연구 및 리스크 분석 정보입니다. 신용등급이나 ESG 등급이 아니며 등급 척도를 사용하지 않고, 금융상품의 벤치마크도, 공식 통계도, 긴급 경보나 공식 분류도, 자문도 아닙니다. 자연인 평가에 사용해서는 안 됩니다. 리스크 등급은 공개된 척도 위의 구간이며, 국가나 사람의 순위가 결코 아닙니다.

기관과 출처

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.

모든 값에는 출처 표시가 함께 제공되며, 각 출처는 고유한 라이선스를 유지합니다. 외부 명칭은 상호운용성을 위해서만 인용됩니다. Nexus가 작성한 텍스트는 공개 시 CC BY 4.0 적용을 예정합니다.

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.