Spatial financeWhere capital meets hazard, exposure and transition
Actuarial mathematics for reinsurance and catastrophe
Actuarial mathematics turns exposure, loss experience and assumptions into reinsurance prices, reserves and capital. In catastrophe reinsurance it combines model output with experience, applies contract terms and allocates capital across layers.
Actuarial mathematics applies probability, statistics and financial theory to measure and price contingent risks, particularly in insurance and pensions. It determines reserves and premiums for uncertain future obligations. In insurance and catastrophe, climate and hazards and disaster and crisis, actuarial methods determine whether risks can be insured and at what cost. They help assess accumulation of correlated losses across portfolios and regions. The canonical description is onthe STEM Commons.
Layer pricing from model output
Event loss tables are passed through layer terms, including reinstatements, to estimate expected loss and volatility per layer.
Blending experience and exposure rating
Historical losses and modelled losses are blended in proportion to the credibility of the experience for each layer.
Capital allocation
Capital is allocated to treaties by their contribution totail risk, informing the cost of capital loading.
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 actuarial mathematics on this platform, with the role each plays and the label its values carry. Sources are listed inthe source register.
داتا
ڕۆڵ
ناونیشان
Event loss tables
Layer losses
مۆدێلکراو
Ceded loss history
Experience rating
REPORTED
Contract terms
Financial structure
DECLARED
05چاودێری
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
Reinsurance and catastrophe · Actuarial mathematics · Run R-2774
Shape parameter uncertainGRA · Reinsurance and Catastrophe
Return level curve
Method card
ڕێگا
Actuarial mathematics
ڕێباز
peaks over threshold
Threshold
94th percentile
ناونیشان
مۆدێلکراو
Parameters
generalised Pareto fit
Shape ξ
0.19
−0.01
0.59
heavy tail if above zero
Scale σ
1.6
0.8
5.2
units of the variable
Exceedances
49
declustered
Tail fit: the screen’s structure, terms and states for Reinsurance and catastrophe.ناو و بەهای نموونەیی؛ دیزاینێکە، نەک تۆمار یان سیستەمی ڕاستەوخۆ.
06پشتڕاستکردنەوە
How results are checked.
Assumptions are tested through experience studies and actual-versus-expected analysis. Independent actuarial review checks methods and results.
بپشکنە
How it is done
Actual versus expected
Layer losses are compared with expectations over time
Sensitivity to model choice
Prices are recomputed under alternative models
پێداچوونەوەی سەربەخۆ
Pricing is reviewed against actuarial standards
Historical experience may not reflect emerging or correlated risks. Long-tail liabilities are sensitive to assumptions about future trends.
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 actuarial mathematics 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
What are the limits of actuarial mathematics in this field?
Historical experience may not reflect emerging or correlated risks. Long-tail liabilities are sensitive to assumptions about future trends.
How are results checked?
Assumptions are tested through experience studies and actual-versus-expected analysis. Independent actuarial review checks methods and results.
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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