Monte Carlo simulation
Large simulations estimate aggregate losses and capital, with convergence tested and random seeds recorded so results can be reproduced.
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.
01Register
High-performance, cloud and edge computing for actuarial and quantitative risk on the record.
Method recordSTEM method
- MethodHigh-performance, cloud and edge computing
- StewardData and Compute
- Applied inActuarial and Quantitative Risk
- Data3 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
02Application
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.
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.
03Monitor
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 reporting99of 100Normal
- Median latency343ms
- Records failing checks2.8per 1,000Watch
- Backlog15min
Gaps on 5 channelsGRA · Actuarial and Quantitative Risk
Latency, p95
Events
- 06:25WarnChannel C-17 stale
- 07:37InfoSchema v2 accepted
- 08:19ErrorChecksum mismatch, batch quarantined
- 08:31DoneReplay complete
04Data
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.
| Data | Role | Label |
|---|---|---|
| Model code repositories | Implementation | DECLARED |
| Simulation outputs | Results | MODELLED |
| Spreadsheet inventories | End-user computing | DECLARED |
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.
Controls
- Typical annual hazard intensity (location)
- 1.00 intensity index
- Spread of extremes (scale)
- 0.80 intensity index
- Intensity at which half the value is lost
- 4.00 intensity index
- Dispersion of vulnerability
- 0.55
- Share of value in the hazard footprint
- 40%
- Protection standard (return period)
- 50 years
Readouts
- Average annual loss
- 0.51% of value
- Loss at 1 in 100 years
- 24.5% of value
- Loss at 1 in 250 years
- 28.4% of value
- Intensity at 1 in 100 years
- 4.68 intensity index
Numbers, methods and sources
| Return period (years) | Intensity (intensity index) | Loss (% of value) |
|---|---|---|
| 1 | 0.09 | 0.00 |
| 2 | 1.27 | 0.00 |
| 4 | 1.91 | 0.00 |
| 7 | 2.47 | 0.00 |
| 13 | 3.00 | 0.00 |
| 24 | 3.51 | 0.00 |
| 44 | 4.02 | 0.00 |
| 82 | 4.53 | 23.55 |
| 154 | 5.03 | 26.44 |
| 287 | 5.53 | 28.87 |
| 536 | 6.03 | 30.88 |
| 1,000 | 6.53 | 32.53 |
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
- Levee effect Di Baldassarre and others (2013), Socio-hydrology, HESS 17
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
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:
- Rivers Natural Earth
- Urban extents Natural Earth
- Cities Natural Earth
- Earthquakes M4.5+, past week (USGS) U.S. Geological Survey · register usgs.earthquake.summary-feeds
- Rain rate, last half hour (IMERG) NASA GPM IMERG Early · register nasa.gibs.context-live-layers
- River discharge forecast, 30 days with range (GloFAS via Open-Meteo) Open-Meteo.com (CC BY 4.0), Copernicus GloFAS · register open-meteo.public-pages
- Built-up and settlement extent, 2010 (HBASE, Landsat) CIESIN/Columbia, NASA SEDAC HBASE · register sedac.context-urban-layers
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Actuarial and Quantitative Risk in Foundry
- Tested at Actuarial and Quantitative Risk in Universe
- Codified in Finance, Insurance and the Economy in Standards
- Published in Actuarial and Quantitative Risk in Reports
- Taught in Faculty of Finance and Economics in Academy
06Validation
How results are checked.
Systems are tested through benchmarks, load testing and failover exercises. Numerical reproducibility is checked across hardware and software configurations.
| Check | How it is done |
|---|---|
| Convergence | Simulation results are stable as iterations increase |
| Reproducibility | A reviewer can rerun results from recorded inputs |
| Independent testing | Code 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.
| Return period | Annual probability | Gross loss | Net loss | Net, capital = 100 |
|---|---|---|---|---|
| 1 in 10 | 0.10 | 14 | 10 | 10 |
| 1 in 50 | 0.02 | 33 | 21 | 21 |
| 1 in 100 | 0.01 | 46 | 27 | 27 |
| 1 in 250 | 0.004 | 68 | 39 | 39 |
| Average annual loss | 3.1 | 2.2 | 2.2 |
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.
Data and Compute stewards high-performance, cloud and edge computing for the Consortium and reviews how it is applied across platforms.
10Connected
Where this page leads across the Consortium.
11Questions
Questions and answers.
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?
The canonical method page lists every platform that applies it.
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