Spatial financeWhere capital meets hazard, exposure and transition
Actuarial mathematics at Actuarial and Quantitative Risk
Actuarial mathematics turns experience, exposure and assumptions into premiums, reserves and capital. On this platform it is the core discipline, applied under professional standards and with the judgement and documentation those standards require.
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, methods and modelling and capital markets and investment, 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.
Reserving with development triangles
Chain ladder, Bornhuetter-Ferguson and stochastic methods project ultimate losses from development triangles, with the spread between methods reported as uncertainty.
Premium build-up
Expected losses, expenses, reinsurance costs and the cost of capital are combined into a technical premium, with each component documented.
Annuity valuation
Mortality tables and improvement assumptions are combined with discount rates to value annuities, with sensitivity to each assumption shown.
03Loss 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.
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
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.
Dane
Rol
Nîşan
Loss development triangles
Reserving
Statîstîkên weşandî
Exposure and policy data
Pricing
REPORTED
Mortality tables
Life valuation
TEXMÎNKIRÎ
05Exposure, 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 at Actuarial and Quantitative Risk 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
06Erêkirin
How results are checked.
Assumptions are tested through experience studies and actual-versus-expected analysis. Independent actuarial review checks methods and results.
Kontrol bikin
How it is done
Actual versus expected
Assumptions are compared with emerging experience
Peer review
Work is reviewed under professional standards
Hesasî
Key assumptions are varied and effects reported
Historical experience may not reflect emerging or correlated risks. Long-tail liabilities are sensitive to assumptions about future trends.
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.
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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