Spatial financeWhere capital meets hazard, exposure and transition
Probability and statistics for actuarial and quantitative risk
Probability and statistics provide the language of quantitative risk: distributions, estimation, dependence, testing and uncertainty. On this platform they underpin loss modelling, credibility, mortality and validation, and show how much confidence each estimate deserves.
Probability and statistics provide the mathematical framework for describing uncertainty and learning from data. They underpin estimation, testing and prediction in every risk discipline. In insurance and catastrophe, methods and modelling and capital markets and investment, statistical methods quantify the likelihood and uncertainty of losses across interconnectedسیستەمەکان. They make the evidence behind risk decisions explicit and reproducible. The canonical description is onthe STEM Commons.
Generalised linear models in pricing
Claim frequency and severity are related to risk factors with exposure offsets, so each factor’s effect and uncertainty can be reviewed.
Bayesian credibility
Prior assumptions from industry data are updated with own experience, giving posterior estimates that show how far the data moved the prior.
Goodness-of-fit testing
Fitted distributions are tested with formal statistics and quantile plots, with attention to fit in the tail where it matters most.
03چاودێری
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
Actuarial and quantitative risk · Probability and statistics · Run R-1415
Shape parameter uncertainGRA · Actuarial and Quantitative Risk
Return level curve
Method card
ڕێگا
ئەگەر و ئامار
ڕێباز
peaks over threshold
Threshold
93th percentile
ناونیشان
مۆدێلکراو
Parameters
generalised Pareto fit
Shape ξ
0.10
0.01
0.43
heavy tail if above zero
Scale σ
1.6
0.9
7.1
units of the variable
Exceedances
132
declustered
Tail fit: the screen’s structure, terms and states for Actuarial and quantitative risk.ناو و بەهای نموونەیی؛ دیزاینێکە، نەک تۆمار یان سیستەمی ڕاستەوخۆ.
04داتا
What the method consumes.
Datasets and records used by probability and statistics on this platform, with the role each plays and the label its values carry. Sources are listed inthe source register.
داتا
ڕۆڵ
ناونیشان
Claims and exposure records
Model fitting
REPORTED
Industry benchmarks
Priors
مەزەندەکراو
Mortality data
Life models
ئامارە بڵاوکراوەکان
05Loss 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.
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 GumbelGumbel (1958)، Statistics of Extremes، Columbia University Press
چەمکی ئەگەری تێپەڕبوون و تێکڕای زیانی ساڵانەGrossi و Kunreuther (2005)، Catastrophe Modeling، Springer
کاریگەری بەربەستDi 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).
Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.
بپشکنە
How it is done
Out-of-sample validation
Models are tested on data not used for fitting
Residual diagnostics
Residuals are inspected for structure
Assumption sensitivity
Distributional assumptions are varied and effects reported
Inference depends on assumptions about independence, distribution and sampling that may not hold. Small or biased samples limit reliability.
07Exposure, 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 probability and statistics for 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
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 probability and statistics in this field?
Inference depends on assumptions about independence, distribution and sampling that may not hold. Small or biased samples limit reliability.
How are results checked?
Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.
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.
Global Risks Allianceمەترسی ئەکچوەری و بڕیڕێگا و تەکنیکیئامادەیی دارایی و بیمە
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