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GRA · Reinsurance and Catastrophe

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.

01Qeyd

Actuarial mathematics for reinsurance and catastrophe on the record.

Method recordSTEM method
  1. RêbazActuarial mathematics
  2. Applied inSîgortaya duyemîn û katastrof
  3. Canonical pageSTEM Commons
02Serlêdan

What actuarial mathematics does here.

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 on the 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 to tail 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.
04Dane

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 in the source register.

DaneRolNîşan
Event loss tablesLayer lossesMODELKIRÎ
Ceded loss historyExperience ratingREPORTED
Contract termsFinancial structureDECLARED
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

Reinsurance and catastrophe · Actuarial mathematics · Run R-2774

Shape parameter uncertainGRA · Reinsurance and Catastrophe

Return level curve

Method card

Rêbaz
Actuarial mathematics
Nêzîkatî
peaks over threshold
Threshold
94th percentile
Nîşan
MODELKIRÎ

Parameters

generalised Pareto fit

Shape ξ0.19−0.010.59heavy tail if above zero
Scale σ1.60.85.2units of the variable
Exceedances49declustered
Tail fit: the screen’s structure, terms and states for Reinsurance and catastrophe.Nav û nirxên nimûneyî; sêwiran e, ne qeyd an pergaleke zindî ye.
06Erêkirin

How results are checked.

Assumptions are tested through experience studies and actual-versus-expected analysis. Independent actuarial review checks methods and results.

Kontrol bikinHow it is done
Actual versus expectedLayer losses are compared with expectations over time
Sensitivity to model choicePrices are recomputed under alternative models
Nirxandina serbixwePricing 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 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 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.

Xetera aktuarî û hejmarî stewards actuarial mathematics for the Consortium and reviews how it is applied across platforms.

11Exposure, 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 actuarial mathematics for reinsurance and catastrophe 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 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.
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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Agahî û pirs îro vekirî ne. Pirsek referansekê distîne û gava piştî wê dide nasîn; serlêdan, drav, veqetandina hesabkirinê û tomarkirin tenê dema ku tîm, mercên wê û kapasîteya vekolînê hebin vedibin.