Spatial financeWhere capital meets hazard, exposure and transition
Extreme value theory and tail risk for reinsurance and catastrophe
Extreme value theory describes the tail of loss and hazard distributions: how large rare events may be and how uncertain those estimates become beyond the record. In reinsurance it underpins excess layer pricing,return periodlosses and the tail of catastrophe models.
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 disaster and crisis, extreme value methods set design levels and capital buffers for rare events. Tail dependence analysis reveals when extremes in differentسیستەمەکانtend to occur together. The canonical description is onthe STEM Commons.
Pricing high excess layers
Losses above a threshold are fitted with a generalised Pareto distribution to price upper layers, with the threshold choice and its effect shown.
Hazard return levels
Annual maxima of wind speed, rainfall or surge are fitted to estimate return levels, with confidence intervals reported.
Tail dependence between perils
Joint extremes of perils or regions are examined to judge 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.
04داتا
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 levels are reported
Stationarity
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.
کۆنترۆڵەکان
Typical annual hazard intensity (location)
1.00 intensity index
بڵاوبوونەوەی لێکەوتە توندەکان (پێوەر)
0.80 intensity index
توندییەک کە تێیدا نیوەی بەها لەدەست دەچێت
4.00 intensity index
بڵاوبوونەوەی لاوازی
0.55
بەشی بەها لە شوێنپێی مەترسیدەردا
40%
ستانداردی پاراستن (ماوەی گەڕانەوە)
50 ساڵ
خوێندنەوەکان
تێکڕای زیانی ساڵانە
0.51% of value
زیان لە 1 لە 100 ساڵدا
24.5% of value
زیان لە 1 لە 250 ساڵدا
28.4% of value
توندی لە 1 لە 100 ساڵدا
4.68 intensity index
ژمارە، ڕێگا و سەرچاوەکان
ماوەی گەڕانەوە (ساڵ)
Intensity (intensity index)
زیان (% ی بەها)
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
ڕێگاکان
مەترسیدەری بەهای زۆرداری Gumbel Gumbel (1958)، Statistics of Extremes، Columbia University Press
چەمکی ئەگەری تێپەڕبوون و تێکڕای زیانی ساڵانە Grossi و Kunreuther (2005)، Catastrophe Modeling، Springer
کاریگەری بەربەست Di Baldassarre و ئەوانی دیکە (2013)، Socio-hydrology، HESS 17
پەنجەی نادڵنیایی (مۆنتی کارلۆی پارامیتەر) Spiegelhalter, Pearson و Short (2011)، Visualising uncertainty about the future، Science 333
هەستیاریی یەک-یەک (تۆرناد) Saltelli و ئەوانی دیکە (2008)، Global Sensitivity Analysis: The Primer، Wiley
داتا
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:
بەرینی دروستکراو و نیشتەجێبوون، 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).
Assembled from theNexus Standardsarea profileبیمە و دووبارەبیمە: the shocks it names, theسیستەمەکانand 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.
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 and tail risk for reinsurance and catastrophe 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
تێکڕای زیانی ساڵانە
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
12پرسیارەکان
پرسیار و وەڵام.
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.
Reportsis 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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