Excess layer pricing
Losses above a threshold are fitted with a generalised Pareto distribution to price excess-of-loss layers, showing how threshold choice moves the price.
GRA · Actuarial and Quantitative Risk
Spatial financeWhere capital meets hazard, exposure and transition
Extreme value theory models the tails of loss and hazard distributions. On this platform it supports large-loss pricing, catastrophe hazard, operational risk and capital at high confidence levels, with threshold choice and uncertainty made explicit.
Extreme value theory and tail risk study the behaviour of rare, high-impact events. They provide methods to estimate the magnitude and frequency of extremes beyond the range of observed data. In insurance and catastrophe, methods and modelling and capital markets and investment, extreme value methods set design levels and capital buffers for rare events. Tail dependence analysis reveals when extremes in different سیستەمەکان tend to occur together. The canonical description is on the STEM Commons.
Losses above a threshold are fitted with a generalised Pareto distribution to price excess-of-loss layers, showing how threshold choice moves the price.
Value at risk and tail value at risk at regulatory confidence levels are estimated from fitted tails, with parameter uncertainty reported.
Covariates such as time or climate indices are added to extreme value models to test whether tails are changing over time.
Which places drive loss in a severe year, and how far it reaches into reinsurance. Illustrative design values.
Datasets and records used by extreme value theory and tail risk on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| داتا | ڕۆڵ | ناونیشان |
|---|---|---|
| Large-loss histories | Tail fitting | چاودێریکراو |
| Hazard maxima | Hazard tails | چاودێریکراو |
| Simulated event sets | Tail extension | مۆدێلکراو |
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Actuarial and quantitative risk · Extreme value theory and tail risk · Run R-3811
generalised Pareto fit
| Shape ξ | 0.18 | −0.02 | 0.45 | heavy tail if above zero |
| Scale σ | 2.1 | 0.9 | 6.0 | units of the variable |
| Exceedances | 123 | declustered |
Fits are checked with diagnostic plots, goodness-of-fit tests and out-of-sample comparison. Sensitivity to threshold and record length is reported.
| بپشکنە | How it is done |
|---|---|
| Threshold stability | Parameters are checked across thresholds |
| Interval reporting | Confidence intervals accompany return levels |
| Stationarity tests | Trends are tested before stationary fits are used |
Extrapolation far beyond the data is highly uncertain. Changing climates and markets violate stationarity assumptions.
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 Japan and Korea; 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
بیمەی ئەتکایی و کارەسات stewards extreme value theory and tail risk 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 extreme value theory and tail risk 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 |
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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