Spatial financeWhere capital meets hazard, exposure and transition
Financial and risk engineering for actuarial and quantitative risk
Financial engineering values and structures instruments whose payoffs depend on risk. On this platform it covers valuation of guarantees and options in insurance contracts, asset-liability matching, longevity and catastropherisk transferand economic scenario generation.
Financial and risk engineering designs instruments, models and processes to measure, price and transfer risk. It applies quantitative methods to portfolios, insurance and risk-sharing arrangements. In insurance and catastrophe, methods and modelling and capital markets and investment, financial engineering enables physical and operational risks to be priced and transferred to capital markets. It informs the design of instruments that fund prevention and recovery. The canonical description is onthe STEM Commons.
Valuing embedded guarantees
Options and guarantees in life and savings products are valued with stochastic scenarios that are consistent with market prices.
Economic scenario generators
Interest rate, equity and credit scenarios are generated for capital and valuation, with calibration targets and validation documented.
Risk transfer structures
Longevity swaps and catastrophe bonds are analysed for how they transfer risk, theirbasis riskand counterparty exposure.
03جوگرافیا
Activity and exposure in Japan and Korea.
Night lights, population density and flood water over Japan and Korea, from NASA GIBS. Each layer carries its own date.
نەخشە کارلێککارەکە JavaScript پێویستە؛ لیستی چینەکان و سەرچاوەکان لە خوارەوەن.
Activity and exposure, framed on Japan and Korea.بەرهەمی ماهەوارە و مۆدێل وەک NASA بڵاوی دەکاتەوە، هەر یەکەیان بە بەرواری خۆی. نیشاندەرن، نەک هەڵسەنگاندنی هیچ شوێنێک.
We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified).داتای کراوەی NASA
We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified).داتای کراوەی NASA
ڕووناکیی شەوی VIIRS Black Marble، NASA Goddard Space Flight Center؛ بڵاودەکرێتەوە لەلایەن NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC)؛ وێنەکان لە ڕێگەی NASA GIBS. ڕەنگەکان لە نەخشەڕەنگی بڵاوکراو دووبارە کێشراونەتەوە؛ بەهاکان لە خانە شەشگۆشەییەکاندا تێکڕا کراون کاتێک پیشان دراون (دەستکاریکراو). داتای کراوەی NASA
Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. ڕەنگەکان لە نەخشەڕەنگی بڵاوکراو دووبارە کێشراونەتەوە؛ بەهاکان لە خانە شەشگۆشەییەکاندا تێکڕا کراون کاتێک پیشان دراون (دەستکاریکراو). doi:10.7927/H49C6VHW. CC BY 4.0
بارانی GPM IMERG، NASA Precipitation Processing System؛ لەلایەن NASA Goddard Earth Sciences Data and Information Services Center (GES DISC)ەوە بڵاو کراوەتەوە؛ وێنە لە ڕێگەی NASA GIBS. ڕەنگەکان لە نەخشەی ڕەنگی بڵاوکراوە دووبارە کێشراونەتەوە؛ بەهاکان لە خانە شەشگۆشەییەکاندا (دەگیرێن) کاتێک نیشان دەدرێن (گۆڕدراو). داتای کراوەی NASA
شیکارییەکانی GHRSST Level 4 MUR، NASA Jet Propulsion Laboratory؛ بڵاودەکرێتەوە لەلایەن NASA Physical Oceanography DAAC (PO.DAAC)؛ وێنەکان لە ڕێگەی NASA GIBS. ڕەنگەکان لە نەخشەڕەنگی بڵاوکراو دووبارە کێشراونەتەوە؛ بەهاکان لە خانە شەشگۆشەییەکاندا تێکڕا کراون کاتێک پیشان دراون (دەستکاریکراو). داتای کراوەی NASA
ASTER GDEM بەرهەمی METI و NASA ە (ASTER Global Digital Elevation Model V003، NASA LP DAAC)؛ وێنەکان لە ڕێگەی NASA GIBS. ڕەنگەکان لە نەخشەڕەنگی بڵاوکراو دووبارە کێشراونەتەوە؛ بەهاکان لە خانە شەشگۆشەییەکاندا تێکڕا کراون کاتێک پیشان دراون (دەستکاریکراو). ناوهێنان بەم شێوەیە: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. داتای کراوەی NASA
ڕووداوە ڕاستەوخۆکان: NASA Earth Observatory Natural Event Tracker (EONET)؛ ڕووداوەکان نیشانەدەرن و بەستەری سەرچاوەی ڕەسەنی خۆیان دەهێڵنەوە.
ڕاستەوخۆ: NASA EONET ڕووداوەکان نیشانەدەرن، نەک ئاگادارکردنەوەی دەسەڵات.
ڕاستەوخۆ: U.S. Geological Survey ڕووداوەکان نیشانەدەرن، نەک ئاگادارکردنەوەی دەسەڵات.
ناوەڕۆک: CIESIN/Columbia, NASA SEDAC HBASE. Center for International Earth Science Information Network (CIESIN), Columbia University, and NASA Socioeconomic Data and Applications Center (SEDAC): Global Human Built-up And Settlement Extent (HBASE) 2010; Seto, Guneralp and Hutyra 2012, Global Grid of Probabilities of Urban Expansion to 2030; Global Urban Heat Island (UHI) Data Set, 2013.
NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
چینە ئارەزوومەندانەکان (5 سەرچاوە لە لیستی چینەکان: ڕاستەوخۆ، پێشبینی، چاودێریکراو و ناوەڕۆک) کاتێک دەکرێنەوە ناوی دابینکەرەکەیان لەسەر نەخشە و لێرەدا پیشان دەدەن؛ پێشبینییەکان دەرچووی مۆدێلن و نیشاندەرە وەرگیراوەکان مەزەندەن.
چینەکان و بەروارەکان
ڕووناکی شەو، ڕۆژانە: VIIRS Black Marble (VNP46A2)، NASA LAADS DAAC · 12ی تشرینی یەکەمی 2026
چڕی دانیشتووان، 2020: دانیشتووان: CIESIN، زانکۆی کۆلۆمبیا، Gridded Population of the World v4.11 (2018)، NASA SEDAC، CC BY 4.0 · جێگیر
Datasets and records used by financial and risk engineering on this platform, with the role each plays and the label its values carry. Sources are listed inthe source register.
داتا
ڕۆڵ
ناونیشان
Market prices
Calibration
چاودێریکراو
Contract terms
Payoff structures
DECLARED
Scenario sets
Valuation inputs
مۆدێلکراو
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. The map on this page shows Japan and Korea; the model itself is not tied to a place.
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 Gumbel Gumbel (1958)، Statistics of Extremes، Columbia University Press
چەمکی ئەگەری تێپەڕبوون و تێکڕای زیانی ساڵانە Grossi و Kunreuther (2005)، Catastrophe Modeling، Springer
کاریگەری بەربەست Di 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).
Models are back-tested, benchmarked against alternatives and reviewed by independent validation functions. Stress tests probe behaviour beyond historical data.
بپشکنە
How it is done
Market consistency
Scenarios reproduce calibration instrument prices
Scenario validation
Generators are tested for realistic behaviour
یەکگرتنەوە
Results are checked for stability as scenario counts increase
Models calibrated to normal markets can underestimate tail losses and correlation shifts. Complex instruments may obscure where risk ultimately sits.
07شبیهسازی دووانە و زنجیرەیی
Trace a shock to the balance sheet.
Assembled from theNexus Standardsarea profileبیمە و دووبارەبیمە: the shocks it names, theسیستەمەکانand 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 fullSystem Builder.
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 financial and risk engineering 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
10Stewardship
Who maintains the method.
Actuarial and Quantitative Risk stewards financial and risk engineering for the Consortium and reviews how it is applied across platforms.
What are the limits of financial and risk engineering in this field?
Models calibrated to normal markets can underestimate tail losses and correlation shifts. Complex instruments may obscure where risk ultimately sits.
How are results checked?
Models are back-tested, benchmarked against alternatives and reviewed by independent validation functions. Stress tests probe behaviour beyond historical data.
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.
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