Reserve ranges
Bootstrap and Bayesian methods produce distributions of reserve outcomes around the central estimate, showing the range that could emerge.
GRA · Actuarial and Quantitative Risk
Spatial financeWhere capital meets hazard, exposure and transition
Uncertainty quantification and model validation for actuarial and quantitative risk
Uncertainty quantification identifies and propagates uncertainty in data, parameters, model structure and assumptions. On this platform it shows how wide plausible outcomes are for premiums, reserves and capital, and which assumptions matter most.
01Qeyd
Uncertainty quantification and model validation for actuarial and quantitative risk on the record.
Method recordSTEM method
- RêbazPîvandina nediyariyê û rastandina modelê
- ParêzvanZanista pergalan û modelkirin
- Applied inXetera aktuarî û hejmarî
- Dane3 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
02Serlêdan
What uncertainty quantification and model validation does here.
Uncertainty quantification and model validation characterise how uncertainty in inputs, parameters and model structure affects predictions. They establish how much confidence a model’s outputs deserve. In insurance and catastrophe, methods and modelling and capital markets and investment, uncertainty quantification shows where risk estimates are robust and where they are fragile. It guides where better data or models would change decisions. The canonical description is on the STEM Commons.
Parameter and model uncertainty
Parameter uncertainty is separated from the uncertainty of choosing one model over another, and both are carried into capital estimates.
Sensitivity ranking
Inputs are varied to rank which assumptions drive results most, guiding where better data or review would add most value.
03Loss 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) |
|---|---|---|
| 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 |
Rêbaz
- Metirsî: nirxên dawîn ên Gumbel Gumbel (1958), Statistics of Extremes, Columbia University Press
- Nazikiya lognormal (lawazî) FEMA (2003), HAZUS-MH Technical Manual
- Qada îhtîmala derbasbûnê û windahiya salane ya navînî Grossi and Kunreuther (2005), Catastrophe Modeling, Springer
- Bandora bendavê Di Baldassarre and others (2013), Socio-hydrology, HESS 17
- Pankeya nediyarîyê (Monte Carlo ya parametreyan) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- 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
Li seranserê şeş stûnan, bi pêşengiya Nexus Consortium
- Ceribandin Labs
- Hatiye avakirin di Actuarial and Quantitative Risk in Foundry
- Tê ceribandin li Actuarial and Quantitative Risk in Universe
- Hatiye kodkirin di Darayî, sîgorta û aborî di Standards de
- Hatiye weşandin di Actuarial and Quantitative Risk in Reports
- Tê de tê dayîn Fakulteya Darayî û Aborî di Academy de
04Dane
What the method consumes.
Datasets and records used by uncertainty quantification and model validation on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Dane | Rol | Nîşan |
|---|---|---|
| Model inputs and parameters | Input uncertainty | TEXMÎNKIRÎ |
| Alternative models | Structural uncertainty | MODELKIRÎ |
| Historical outcomes | Calibration of ranges | ÇAVDÊRÎKIRÎ |
05Exposure, 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 uncertainty quantification and model validation 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 |
| Windahiya salane ya navînî | 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
06Erêkirin
How results are checked.
Predictive distributions are tested for calibration and sharpness against held-out observations. Verification confirms that code solves the intended equations.
| Kontrol bikin | How it is done |
|---|---|
| Range calibration | Stated ranges are compared with outcomes where possible |
| Dependence of inputs | Correlations between uncertain inputs are documented |
| Clear communication | Uncertainty is presented with each result |
Structural uncertainty and unknown unknowns are difficult to represent. Computational cost limits exploration of high-dimensional models.
08Stewardship
Who maintains the method.
Zanista pergalan û modelkirin stewards uncertainty quantification and model validation for the Consortium and reviews how it is applied across platforms.
09Girêdayî
Ev rûpel li ser Konsorsiyûmê te ber bi kû ve dibe.
10Pirs
Pirs û bersiv.
What are the limits of uncertainty quantification and model validation in this field?
Structural uncertainty and unknown unknowns are difficult to represent. Computational cost limits exploration of high-dimensional models.
How are results checked?
Predictive distributions are tested for calibration and sharpness against held-out observations. Verification confirms that code solves the intended equations.
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.
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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.
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Bring an exposure or capital question to the Alliance.
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Hiqûqî
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