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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.

01Qeyd

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

Method recordSTEM method
  1. RêbazKomputerkirina bi performansa bilind, ewr û kenar
  2. Applied inXetera aktuarî û hejmarî
  3. Canonical pageSTEM Commons
02Serlêdan

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.

03Çavdêrî bike

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 100Normal
  • Median latency
    343ms
  • Records failing checks
    2.8per 1,000Çavdêrî
  • Backlog
    15hûr
Gaps on 5 channelsGRA · Actuarial and Quantitative Risk

Latency, p95

Bûyer

  1. 06:25Hişyar bikeChannel C-17 stale
  2. 07:37AgahîSchema v2 accepted
  3. 08:19ErrorChecksum mismatch, batch quarantined
  4. 08:31QediyaReplay complete
Compute workload: the screen’s structure, terms and states for Actuarial and quantitative risk.Nav û nirxên nimûneyî; sêwiran e, ne qeyd an pergaleke zindî ye.
04Dane

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.

DaneRolNîşan
Model code repositoriesBicihanînDECLARED
Simulation outputsEncamMODELKIRÎ
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.

Kontrol

Typical annual hazard intensity (location)
1.00 intensity index
Belavbûna nirxên tund (pîvan)
0.80 intensity index
Tundiya ku nîvê nirxê tê de winda dibe
4.00 intensity index
Belavbûna lawazî
0.55
Para nirxê di nav şopa metirsî de
40%
Standarda parastinê (heyama vegerê)
50 sal

Xwendin

Windahiya salane ya navînî
0.51% of value
Windahî di 1 ji 100 salan de
24.5% of value
Windahî di 1 ji 250 salan de
28.4% of value
Tundî di 1 ji 100 salan de
4.68 intensity index
Jimar, rêbaz û çavkanî
Heyama vegerê (sal)Intensity (intensity index)Windahî (% ya nirxê)
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

Rêbaz

  1. Metirsî: nirxên dawîn ên Gumbel Gumbel (1958), Statistics of Extremes, Columbia University Press
  2. Nazikiya lognormal (lawazî) FEMA (2003), HAZUS-MH Technical Manual
  3. Qada îhtîmala derbasbûnê û windahiya salane ya navînî Grossi and Kunreuther (2005), Catastrophe Modeling, Springer
  4. Bandora bendavê Di Baldassarre and others (2013), Socio-hydrology, HESS 17
  5. Pankeya nediyarîyê (Monte Carlo ya parametreyan) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
  6. Hesasiyeta yek-yek (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley

Dane

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:

  • Çem Natural Earth
  • Dirêjahiyên bajarî Natural Earth
  • Bajar Natural Earth
  • Erdhejên M4.5+, hefteya borî (USGS) U.S. Geological Survey · register usgs.earthquake.summary-feeds
  • Rêjeya baranê, nîv saeta dawîn (IMERG) NASA GPM IMERG Early · register nasa.gibs.context-live-layers
  • Pêşbînî: herikîna çemê, 30 roj bi navber (GloFAS bi riya Open-Meteo) Open-Meteo.com (CC BY 4.0), Copernicus GloFAS · register open-meteo.public-pages
  • Berfirehiya avakirî û niştecihbûnê, 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).

Li seranserê şeş stûnan, bi pêşengiya Nexus Consortium

06Erêkirin

How results are checked.

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

Kontrol bikinHow it is done
HevgirtinSimulation 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
Windahiya salane ya navînî3.12.22.2
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
09Stewardship

Who maintains the method.

Dane û hesabkirin stewards high-performance, cloud and edge computing for the Consortium and reviews how it is applied across platforms.

10Girêdayî

Ev rûpel li ser Konsorsiyûmê te ber bi kû ve dibe.

11Pirs

Pirs û bersiv.

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?
Çavdêrxaneya canonical method page lists every platform that applies it.
rêzeya Pergalên StratejîkTevkarî bike

Ji vê rûpelê delîl bidin.

rêzeya Pergalên Stratejîk 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 AllianceXetera aktuarî û hejmarîRêbaz û teknîkîAmadebûna fînans û sîgortayê
Xetera aktuarî û hejmarî

Bring an exposure or capital question to the Alliance.

Agahî û pirs îro vekirî ne. Pirsek referansekê distîne û gava piştî wê dide nasîn; serlêdan, drav, veqetandina hesabkirinê û tomarkirin tenê dema ku tîm, mercên wê û kapasîteya vekolînê hebin vedibin.