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GRA · Actuarial and Quantitative Risk

Spatial financeWhere capital meets hazard, exposure and transition

High-performance, cloud and edge computing for actuarial and quantitative risk

Computing makes quantitative risk work possible and introduces its own risks. On this platform it covers simulation, reproducible code, version control and the controls that keep spreadsheets and models from producing silent errors.

پرسیارێک بنێرە Back to STEM foundation of Actuarial and Quantitative Risk
01تۆمارکردن

High-performance, cloud and edge computing for actuarial and quantitative risk on the record.

Method recordSTEM method
  1. ڕێگاکۆمپیوتەری کارایی بەرز، هەور و لێوار
  3. Applied inمەترسی ئەکچوەری و بڕی
  4. داتا3 inputs
  6. Canonical pageSTEM Commons
02بەکارهێنان

What high-performance, cloud and edge computing does here.

High-performance, cloud and edge computing provide the processing and storage capacity for simulation, analytics and digital services. They are both an enabler of risk analysis and a critical dependency for society. In insurance and catastrophe, methods and modelling and capital markets and investment, computing capacity determines how fast and at what resolution risks can be modelled. Its concentration is itself a systemic dependency for finance, health and government services. The canonical description is on the STEM Commons.

Monte Carlo simulation

Large simulations estimate aggregate losses and capital, with convergence tested and random seeds recorded so results can be reproduced.

Version control and reproducibility

Model code, data and parameters are held under version control, so any result can be traced and rerun by a reviewer.

End-user computing controls

Spreadsheets used in reserving and capital are inventoried, tested and reviewed, since errors in them have caused material misstatements.

03چاودێری

Compute workload.

The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.

Compute workload

Actuarial and quantitative risk · High-performance, cloud and edge · Run R-8719

  • Channels reporting
    99of 100ئاسایی
  • Median latency
    343ms
  • Records failing checks
    2.8per 1,000چاودێری
  • Backlog
    15کەمترین
Gaps on 5 channelsGRA · Actuarial and Quantitative Risk

Latency, p95

ڕووداوەکان

  1. 06:25ئاگادارکردنەوەChannel C-17 stale
  2. 07:37زانیاریSchema v2 accepted
  3. 08:19ErrorChecksum mismatch, batch quarantined
  4. 08:31تەواو بووReplay complete
Compute workload: the screen’s structure, terms and states for Actuarial and quantitative risk.ناو و بەهای نموونەیی؛ دیزاینێکە، نەک تۆمار یان سیستەمی ڕاستەوخۆ.
04داتا

What the method consumes.

Datasets and records used by high-performance, cloud and edge computing on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.

داتاڕۆڵناونیشان
Model code repositoriesجێبەجێکردنDECLARED
Simulation outputsئەنجامەکانمۆدێلکراو
Spreadsheet inventoriesEnd-user computingDECLARED
05Loss model

Loss exceedance, average annual loss and probable maximum loss.

The loss a portfolio in the footprint can expect per year and at the 1 in 100 and 1 in 250 levels that insurers and lenders price, from the same three blocks every catastrophe model uses. Teaching model with stated illustrative values, not a prediction.

کۆنترۆڵەکان

Typical annual hazard intensity (location)
1.00 intensity index
بڵاوبوونەوەی لێکەوتە توندەکان (پێوەر)
0.80 intensity index
توندییەک کە تێیدا نیوەی بەها لەدەست دەچێت
4.00 intensity index
بڵاوبوونەوەی لاوازی
0.55
بەشی بەها لە شوێنپێی مەترسیدەردا
40%
ستانداردی پاراستن (ماوەی گەڕانەوە)
50 ساڵ

خوێندنەوەکان

تێکڕای زیانی ساڵانە
0.51% of value
زیان لە 1 لە 100 ساڵدا
24.5% of value
زیان لە 1 لە 250 ساڵدا
28.4% of value
توندی لە 1 لە 100 ساڵدا
4.68 intensity index
ژمارە، ڕێگا و سەرچاوەکان
ماوەی گەڕانەوە (ساڵ)Intensity (intensity index)زیان (% ی بەها)
10.090.00
21.270.00
41.910.00
72.470.00
133.000.00
243.510.00
444.020.00
824.5323.55
1545.0326.44
2875.5328.87
5366.0330.88
1,0006.5332.53

ڕێگاکان

  1. مەترسیدەری بەهای زۆرداری Gumbel Gumbel (1958)، Statistics of Extremes، Columbia University Press
  2. ناسکیی لۆگنۆرماڵ (لاوازی) FEMA (2003)، HAZUS-MH Technical Manual
  3. چەمکی ئەگەری تێپەڕبوون و تێکڕای زیانی ساڵانە Grossi و Kunreuther (2005)، Catastrophe Modeling، Springer
  4. کاریگەری بەربەست Di Baldassarre و ئەوانی دیکە (2013)، Socio-hydrology، HESS 17
  5. پەنجەی نادڵنیایی (مۆنتی کارلۆی پارامیتەر) Spiegelhalter, Pearson و Short (2011)، Visualising uncertainty about the future، Science 333
  6. هەستیاریی یەک-یەک (تۆرناد) Saltelli و ئەوانی دیکە (2008)، Global Sensitivity Analysis: The Primer، Wiley

داتا

The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page’s map can draw:

  • ڕووبارەکان Natural Earth
  • بەرینی شارەکان Natural Earth
  • شارەکان Natural Earth
  • بوومەلەرزەکانی M4.5+، هەفتەی ڕابردوو (USGS) U.S. Geological Survey · register usgs.earthquake.summary-feeds
  • ڕێژەی باران، نیو کاتژمێری ڕابردوو (IMERG) NASA GPM IMERG Early · register nasa.gibs.context-live-layers
  • پێشبینی ڕۆیشتنی ڕووبار، 30 ڕۆژ لەگەڵ مەودا (GloFAS لە ڕێگەی Open-Meteo) Open-Meteo.com (CC BY 4.0), Copernicus GloFAS · register open-meteo.public-pages
  • بەرینی دروستکراو و نیشتەجێبوون، 2010 (HBASE، Landsat) CIESIN/Columbia, NASA SEDAC HBASE · register sedac.context-urban-layers
Loss exceedance, average annual loss and probable maximum loss. Illustrative teaching model: it runs on the values set by the controls, not on measured data. Methods: Gumbel extreme-value hazard (Gumbel (1958), Statistics of Extremes, Columbia University Press); Lognormal fragility (vulnerability) (FEMA (2003), HAZUS-MH Technical Manual); Exceedance-probability curve and average annual loss (Grossi and Kunreuther (2005), Catastrophe Modeling, Springer).

لە ناو شەش کۆڵەکەکەدا، بە سەرپەرشتیی Nexus Consortium

06پشتڕاستکردنەوە

How results are checked.

سیستەمەکان are tested through benchmarks, load testing and failover exercises. Numerical reproducibility is checked across hardware and software configurations.

بپشکنەHow it is done
یەکگرتنەوەSimulation results are stable as iterations increase
ReproducibilityA reviewer can rerun results from recorded inputs
Independent testingCode is tested against independent calculations

Concentration in few providers or regions creates shared points of failure. Energy, water and supply chain constraints limit capacity growth.

07Exposure, loss and capital

What it means for exposure, loss and capital.

The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.

Exposure and loss at return periods

How losses from high-performance, cloud and edge computing for actuarial and quantitative risk grow with severity, gross and net of reinsurance, against capital.

1 in 101 in 501 in 1001 in 250grossnet of reinsurance800
Return periodAnnual probabilityGross lossNet lossNet, capital = 100
1 in 100.10141010
1 in 500.02332121
1 in 1000.01462727
1 in 2500.004683939
تێکڕای زیانی ساڵانە3.12.22.2
Illustrative loss index for a example portfolio (capital = 100): the exceedance-probability form insurers and supervisors read. Not data on any institution, not a price or a rating.
NGFS climate scenarios · Network for Greening the Financial SystemIFRS S2 Climate-related Disclosures · ISSBTCFD recommendations · Financial Stability Board TCFDPrinciples for the effective management of climate-related financial risks · Basel Committee on Banking Supervision
08Connections

Topics that rely on this method.

09Stewardship

Who maintains the method.

داتا و کۆمپیوتەر stewards high-performance, cloud and edge computing for the Consortium and reviews how it is applied across platforms.

Canonical page: High-performance, cloud and edge computing

10پەیوەست

ئەو شوێنانەی ئەم لاپەڕەیە لە ناو کۆنسۆرتیۆم بۆیان دەبات.

11پرسیارەکان

پرسیار و وەڵام.

What are the limits of high-performance, cloud and edge computing in this field?
Concentration in few providers or regions creates shared points of failure. Energy, water and supply chain constraints limit capacity growth.
How are results checked?
Systems are tested through benchmarks, load testing and failover exercises. Numerical reproducibility is checked across hardware and software configurations.
Are the values on this page real?
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.
Where else is this method applied?
ئەو canonical method page lists every platform that applies it.
Reportsبەشداربە

لەم لاپەڕەیەوە بەڵگە پێشکەش بکە.

Reports 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.

Global Risks Allianceمەترسی ئەکچوەری و بڕیڕێگا و تەکنیکیئامادەیی دارایی و بیمە

هەرگیز داتای کەسی، وردەکاریی هەستیاری ژێرخان یان هیچ شتێک مەنێرە کە مافی بڵاوکردنەوەت نییە. بەشدارییەک پێش بەکارهێنان پێداچوونەوەی بۆ دەکرێت؛ ناردنی ماف نابەخشێت بە نووسەرایەتی یان بەڕێوەبردنی ڕاگەیاندن، و پاڵپشتی دارایی هەرگیز دۆزینەوەیەک ناکڕێت.

مەترسی ئەکچوەری و بڕی

Bring an exposure or capital question to the Alliance.

زانیاری و پرسیار ئەمڕۆ کراوەن. پرسیارێک ژمارەی ئاماژەی پێدەدرێت و هەنگاوی دواتر دیاری دەکات؛ داواکاری، پارەدان، تەرخانکردنی کۆمپیوتەر و تۆمارکردن تەنیا کاتێک دەکرێنەوە کە تیم، مەرج و توانای پێداچوونەوەیان هەبێت.

Propose a readiness pilot بینینی هەموو ڕێگاکانی پەیوەندی
یاسایی

نەخشە، داتا و وێنەکانی ئەم ماڵپەڕە بۆ ناوەڕۆک و زانیاریی گشتی پیشان دراون. ئامۆژگاری، پێشبینی یان ئاگادارکردنەوە نین؛ سنوورەکانی زانیارییەکە لە ڕەتکردنەوەی بەرپرسیارێتی و ئاگاداریی داتادا دەستنیشان کراون.