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.
What extreme value theory and tail risk does here.
Extreme value theory andtail riskstudy 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 differentpergalantend to occur together. The canonical description is onthe 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 estimatevalue at riskand tail value at risk at regulatory confidence levels, withparameter uncertaintyand 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 andtail riskon this platform, with the role each plays and the label its values carry. Sources are listed inthe source register.
Confidence intervals for return-period losses are reported, by bootstrap or profile likelihood
Indexation and stationarity
Losses 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 everycatastrophe modeluses. 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)
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
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:
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
Trace a shock in extreme value theory for insurance tails to the balance sheet.
Assembled from theNexus Standardsarea profileSîgorta û ji nû ve sîgorta: the shocks it names, thepergalanand 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 fullSystem Builder.
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.
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
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
12Girêdayî
Ev rûpel li ser Konsorsiyûmê te ber bi kû ve dibe.
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.
rêzeya Pergalên Stratejîkis 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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