Layer pricing from model output
Event loss tables are passed through layer terms, including reinstatements, to estimate expected loss and volatility per layer.
GRA · Reinsurance and Catastrophe
Spatial financeWhere capital meets hazard, exposure and transition
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 on the STEM Commons.
Event loss tables are passed through layer terms, including reinstatements, to estimate expected loss and volatility per layer.
Historical losses and modelled losses are blended in proportion to the credibility of the experience for each layer.
Capital is allocated to treaties by their contribution to tail risk, informing the cost of capital loading.
Which places drive loss in a severe year, and how far it reaches into reinsurance. Illustrative design values.
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.
| داتا | ڕۆڵ | ناونیشان |
|---|---|---|
| Event loss tables | Layer losses | مۆدێلکراو |
| Ceded loss history | Experience rating | REPORTED |
| Contract terms | Financial structure | DECLARED |
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Reinsurance and catastrophe · Actuarial mathematics · Run R-2774
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 |
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.
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.
کۆنترۆڵەکان
خوێندنەوەکان
| 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 |
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:
لە ناو شەش کۆڵەکەکەدا، بە سەرپەرشتیی Nexus Consortium
Assembled from the Nexus Standards area profile بیمە و دووبارەبیمە: the shocks it names, the سیستەمەکان 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.
مەترسیدەرەکان
سیستەم و بەستنەوەکان
کۆنترۆڵەکان
NS-0 دەستوور و بنەماکانNS-3 UNIەکانی تۆماری مەترسی (ڕێزمان، خشتەی گواستنەوەی کۆن، دەسەڵاتی تەرخانکردن)NS-9 UNIی ناوەڕۆکی (دامەزراندنەکان، نموونەکان، نموونەکانی پڕۆگرام)NS-10 Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11 سەلماندن و چاودێری (بەڵگە، گواهییەکان، چاودێریی گونجان لەگەڵ zero-trust)NS-14 پابەندبوون و هاوتاکردن لەگەڵ ستانداردی دەرەکیNRS-F3 بیمە، مەترسیی کارەسات و کەلێنەکانی پاراستنNRS-H113 خێزانی مەترسیدەر: ناسکیی سیستەمی دارایی (NHS-1 soc.financial-system)NRS-U17 Universe platform profile: Insurance NexusNRS-H71 خێزانی مەترسیدەر: کارەساتە کەونی و کاتی قووڵەکان (NHS-1 planet.deep-time)NRS-H65 خێزانی مەترسیدەر: پێشێلکردنی سنوورەکانی گۆی زەوی (NHS-1 planet.boundary)NRS-F8 گواستنەوەی مەترسیی پارامێتری و لەسەر بنەمای نیشاندەرچاودێری
جوگرافیای نموونە
مەترسی ئەکچوەری و بڕی stewards actuarial mathematics for the Consortium and reviews how it is applied across platforms.
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.
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 |
Reports 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.
هەرگیز داتای کەسی، وردەکاریی هەستیاری ژێرخان یان هیچ شتێک مەنێرە کە مافی بڵاوکردنەوەت نییە. بەشدارییەک پێش بەکارهێنان پێداچوونەوەی بۆ دەکرێت؛ ناردنی ماف نابەخشێت بە نووسەرایەتی یان بەڕێوەبردنی ڕاگەیاندن، و پاڵپشتی دارایی هەرگیز دۆزینەوەیەک ناکڕێت.
زانیاری و پرسیار ئەمڕۆ کراوەن. پرسیارێک ژمارەی ئاماژەی پێدەدرێت و هەنگاوی دواتر دیاری دەکات؛ داواکاری، پارەدان، تەرخانکردنی کۆمپیوتەر و تۆمارکردن تەنیا کاتێک دەکرێنەوە کە تیم، مەرج و توانای پێداچوونەوەیان هەبێت.
نەخشە، داتا و وێنەکانی ئەم ماڵپەڕە بۆ ناوەڕۆک و زانیاریی گشتی پیشان دراون. ئامۆژگاری، پێشبینی یان ئاگادارکردنەوە نین؛ سنوورەکانی زانیارییەکە لە ڕەتکردنەوەی بەرپرسیارێتی و ئاگاداریی داتادا دەستنیشان کراون.
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