Payment flow networks
Interbank payment flows are analysed as networks to find participants whose delays would most affect others’ liquidity.
GRA · 支付与市场基础设施
空间金融资本与危害、风险暴露和转型的交汇之处
面向支付与市场基础设施的网络科学与传导
Network science represents payment and market infrastructure as nodes and links: participants, 系统, settlement banks and providers. On this platform it maps dependencies, identifies critical nodes and simulates how a failure spreads.
02申请
What network science and contagion does here.
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 系统. In financial stability, cyber and digital resilience and digital public infrastructure, 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 STEM Commons.
Shared provider mapping
Dependencies of institutions on cloud, messaging and software providers are mapped to show concentration that individual views miss.
CCP membership networks
Common membership across CCPs is mapped to show how one member default could affect several clearing houses at once.
03监测
Network analysis.
团队遵循该方法时会使用的界面：其指标、状态与决策。名称为示例，数值仅作示意。
网络分析
Payments and market infrastructure · Network science and contagion · Run R-7933
单点故障GRA · 支付与市场基础设施
依赖关系图
nodes: assets · edges: dependencies
Most central nodes
| N3 | 0.16 | 11 | Critical |
| N4 | 0.31 | 14 | 正常 |
| N5 | 0.32 | 9 | 正常 |
| N6 | 0.41 | 14 | 正常 |
Cascade size under removal
04数据
该方法使用的输入。
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 来源登记册.
| 数据 | 角色 | 标签 |
|---|---|---|
| Payment system transaction data | Flow networks | 观测 |
| Third-party registers | Provider dependencies | 已报告 |
| CCP membership lists | Clearing networks | 已发布统计 |
控制措施
- 外部资产下降
- 2.0%
- 杠杆率（资产与权益之比）
- 10 倍
- 银行间资产占比
- 10%
- 银行间债权的回收
- 40%
- 体系内的银行
- 20
- 核心银行
- 占银行的 20%
读数
- 初始冲击造成的权益损失
- 18.0%
- 传染后的权益损失
- 45.0%
- 传染新增（DebtRank）
- 27.0 个点
- 违约银行
- 0
数字、方法与来源
| 轮次 | 系统权益损失（%） | 违约银行 |
|---|---|---|
| 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 |
方法
- DebtRank Battiston 等（2012），Scientific Reports 2:541
- 差异化 DebtRank Bardoscia and others (2015), PLOS ONE 10
- 金融网络中的传染 Gai and Kapadia (2010), Proc. R. Soc. A 466
- 金融网络中的清算 Eisenberg 和 Noe（2001），Management Science 47
- 不确定性扇形图（参数蒙特卡罗） Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- 单因素敏感性（龙卷风图） Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
数据
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.
贯穿六大支柱，由 Nexus Consortium 牵头
06验证
结果如何核查。
Models are checked by reproducing observed cascades and outbreaks. Robustness of results to missing or uncertain links is tested.
| 核查 | 如何开展 |
|---|---|
| Data coverage | The share of activity covered by the network data is stated |
| 稳定 | Critical node rankings are checked across periods |
| Scenario realism | Simulated failures reflect plausible operational events |
Network data are often incomplete or confidential. Static network models may miss adaptive behaviour during cascades.
07风险暴露、损失与资本
What it means for exposure, loss and capital.
风险官、监管者或财政部会要求的风险暴露、损失、情景和资本视图，以及它们据以报告的框架。结构遵循监管和精算实务。数值为说明性：绝非价格、评级或建议。
传导渠道
How a shock in network science and contagion for payments and market infrastructure reaches credit, market, underwriting, liquidity and operational risk, and feeds back.
NGFS 气候情景 · 央行与监管机构绿色金融网络IFRS S2 气候相关披露 · ISSBTCFD 建议 · 金融稳定理事会 TCFD有效管理气候相关金融风险的原则 · 巴塞尔银行监管委员会
09管理责任
由谁维护该方法。
系统科学与建模 stewards network science and contagion for the Consortium and reviews how it is applied across platforms.
10已关联
本页在整个联合体中通向何处。
11问题
问题与解答。
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.
结果如何核查？
Models are checked by reproducing observed cascades and outbreaks. Robustness of results to missing or uncertain links is tested.
本页面上的数值是真实的吗？
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.
该方法还应用于何处？
维持社会运转的各个 规范方法页面 列出应用该方法的每个平台。
报告贡献
从本页贡献证据。
报告 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.
全球风险联盟（Global Risks Alliance）支付与市场基础设施战略系统情景与压力测试
- 电子邮件门户 GW-GRA从本页发送证据、更正、案例、方法、审查、翻译或概况投稿。它会带上本页标签送达，由负责的编辑接收。
- Zenodo开放存缴存缴并提交到社区用您的 ORCID iD 在 Zenodo 上发布您自己的记录，然后提交到那里的 Reports 社区。由策展人审核；DOI 仍归您所有。
- 报告全球风险联盟（Global Risks Alliance）查看所有贡献方式贡献类型、各自的评审方式、您获得的署名、许可协议，以及绝不接受的内容。
切勿发送个人数据、敏感基础设施细节或您无权分享的任何内容。贡献在使用前会经过审查；发送贡献不赋予署名权或编辑权，赞助也绝不能换取研究结论。
法律
法律文件。
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