GRA · Insurance Nexus

Spatial financeWhere capital meets hazard, exposure and transition

Extreme value theory for insurance tails

Extreme value theory gives the tail of the loss distribution its shape: how large a rare event loss may be, how losses accumulate in a bad year, and how uncertain those estimates become beyond the record. Capital, reinsurance and catastrophe pricing all rest on it.

02Serlêdan

What extreme value theory and tail risk does here.

Extreme value theory and tail risk study the behaviour of rare, high-impact events. They provide methods to estimate the magnitude and frequency of extremes beyond the range of observed data. In insurance and catastrophe, climate and hazards and health and biosecurity, extreme value methods set design levels and capital buffers for rare events. Tail dependence analysis reveals when extremes in different pergalan tend to occur together. The canonical description is on the STEM Commons.

Large-loss pricing for excess layers

Losses above a threshold are fitted with a generalised Pareto distribution to price excess-of-loss reinsurance layers, with the threshold choice and its effect on the layer price shown.

Tail measures for solvency capital

Loss distributions are extended into the tail to estimate value at risk and tail value at risk at regulatory confidence levels, with parameter uncertainty and alternative fits reported.

Tail dependence across lines and perils

Copulas and multivariate extreme value models examine whether extreme losses in different lines, regions or asset classes tend to occur together, which governs how much diversification credit is defensible.

03Spatial finance

Exposure, hazard and the exceedance curve.

Which places drive loss in a severe year, and how far it reaches into reinsurance. Illustrative design values.

Coastal exposure under cyclone and earthquake zones, beside the loss curve it produces.Illustrative design values, not a model output or a price.
04Dane

What the method consumes.

Datasets and records used by extreme value theory and tail risk on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.

DaneRolNîşan
Large-loss historiesTail fittingÇAVDÊRÎKIRÎ
Industry catastrophe loss recordsEvent severityTEXMÎNKIRÎ
Modela karesatê event setsSynthetic tail extensionMODELKIRÎ
Hazard maxima such as wind speed and rainfallHazard tail behaviourÇAVDÊRÎKIRÎ
05Çavdêrî bike

Tail fit.

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

Tail fit

Insurance · Extreme value theory for insurance · Run R-7661

Shape parameter uncertainGRA · Insurance Nexus

Return level curve

Method card

Rêbaz
Teoriya nirxên tund û xetera dûvê
Nêzîkatî
peaks over threshold
Threshold
90th percentile
Nîşan
MODELKIRÎ

Parameters

generalised Pareto fit

Shape ξ0.22−0.030.45heavy tail if above zero
Scale σ2.71.06.9units of the variable
Exceedances88declustered
Tail fit: the screen’s structure, terms and states for Insurance.Nav û nirxên nimûneyî; sêwiran e, ne qeyd an pergaleke zindî ye.
06Erêkirin

How results are checked.

Fits are checked with diagnostic plots, goodness-of-fit tests and out-of-sample comparison. Sensitivity to threshold and record length is reported.

Kontrol bikinHow it is done
Threshold stabilityTail parameters are estimated across a range of thresholds and the choice is justified by stability plots
Goodness of fit in the tailFits are judged on tail behaviour, not overall fit, using quantile plots and out-of-sample large losses
Parameter uncertaintyConfidence intervals for return-period losses are reported, by bootstrap or profile likelihood
Indexation and stationarityLosses are brought to a common basis for inflation and exposure, and trends are tested before fitting

Extrapolation far beyond the data is highly uncertain. Changing climates and markets violate stationarity assumptions.

07Loss 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. The map on this page shows The Gulf of Mexico coast; the model itself is not tied to a place.

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

09Simulatorê cêwî û kaskade

Trace a shock in extreme value theory for insurance tails to the balance sheet.

Assembled from the Nexus Standards area profile Sîgorta û ji nû ve sîgorta: the shocks it names, the pergalan and sectors that carry the loss, the controls that dampen it and the data that observe it. Pick an initiating shock and step through the cascade to see which exposures it reaches and where losses could concentrate. The Map view places the run on an example geography with open data. Every weight is a stated assumption. An exercise, not a forecast, a valuation, a rating or investment advice. To model a place of your own choosing, open the full System Builder.

Metirsî

  • Tropical cyclone
  • Erdhej
  • Ice-jam flood
  • Agirê daristanan
  • Storm surge (set off downstream)
  • Intense rainfall (set off downstream)
  • River flood (set off downstream)
  • Flash flood (set off downstream)
  • Dangerous surf (set off downstream)
  • Qutbûna berfireh a elektrîkê (li aliyê jêr çê dibe)
  • Koçberiya girseyî (li aliyê jêr çê dibe)
  • Coastal flood (set off downstream)
  • Aftershock sequence (set off downstream)
  • Fault surface rupture (set off downstream)
  • Soil liquefaction (set off downstream)
  • Slope slide (set off downstream)
  • Rockfall (set off downstream)
  • Tsunami (set off downstream)

Kontrol

  • NS-0 Destûr û prensîb
  • NS-3 UNIyên tomara xetereyê (rêzimanî, nexşeya veguhastina ji pergala kevin, desthilata veqetandinê)
  • NS-9 UNI yên çarçoveyî (belavkirin, mînak, mînakên bernameyê)
  • NS-10 Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)
  • NS-11 Piştrastkirin û çavdêrî (delîl, erêkirin, çavdêriya lihevhatina zero-trust)
  • NS-14 Lihevhatin û nexşeya li standardên derveyî
  • NRS-F3 Sîgorta, xetera felaketan û valahiyên parastinê
  • NRS-H113 Malbata metirsiyê: Nazikiya pergala darayî (NHS-1 soc.financial-system)
  • NRS-U17 Universe platform profile: Insurance Nexus
  • NRS-H71 Malbata metirsiyê: Felaketên kozmîk û yên dema kûr (NHS-1 planet.deep-time)
  • NRS-H65 Malbata metirsiyê: Binpêkirinên sînorên gerstêrkê (NHS-1 planet.boundary)
  • NRS-F8 Veguhastina xeterê ya parametrîk û li ser bingeha îndeksê

Çavdêrî

  • OECD Data Explorer (incl. Creditor Reporting System, insurance and pension statistics) (listed for reference)
  • Insurance statistics (listed for reference)
  • USGS earthquake GeoJSON summary feeds (listed for reference)
  • Direct economic loss attributed to disasters in relation to global gross domestic product (GDP)
  • Direct economic loss attributed to disasters in relation to global gross domestic product
  • (a) Number of commercial bank branches per 100,000 adults and (b) number of automated teller machines (ATMs) per 100,000 adults

Erdnîgariya mînakê

  • Western Europe: 152 hexagonal cells
  • Rêjeya barînê, nîv saeta dawî (IMERG), NASA GIBS
  • Ronahiyên şevê îşev, qutbûn (VIIRS Day/Night Band), NASA GIBS
  • Ava rûyê erdê di nav ewr re, 30 m (radara OPERA DSWx-S1), NASA GIBS
  • Carbon monoxide, smoke transport (AIRS), NASA GIBS
10Stewardship

Who maintains the method.

Sîgortaya duyemîn û katastrof stewards extreme value theory and tail risk for the Consortium and reviews how it is applied across platforms.

11Exposure, loss and capital

What extreme value theory for insurance tails 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 extreme value theory for insurance tails 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
13Pirs

Pirs û bersiv.

What are the limits of extreme value theory and tail risk in this field?
Extrapolation far beyond the data is highly uncertain. Changing climates and markets violate stationarity assumptions.
How are results checked?
Fits are checked with diagnostic plots, goodness-of-fit tests and out-of-sample comparison. Sensitivity to threshold and record length is reported.
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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