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\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:where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-n1n441m8.nxe img{max-width:100%;height:auto}<\/style><svg class=\"nx-defs\" aria-hidden=\"true\" focusable=\"false\" width=\"0\" height=\"0\"><defs><linearGradient id=\"nx-gi\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-giu\" gradientUnits=\"userSpaceOnUse\" x1=\"0\" y1=\"0\" x2=\"800\" y2=\"0\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-gp\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gp-a\"\/><stop offset=\"1\" class=\"nx-gp-b\"\/><\/linearGradient><\/defs><\/svg><section class=\"nx-scene nx-es-scene\" data-scene=\"text\" data-ground=\"none\" data-es-org=\"gra\" aria-labelledby=\"nx-scene-title\"><div class=\"nx-scene-intro nx-es-intro\"><p class=\"nx-eyebrow\"><abbr title=\"The Global Risks Alliance\">GRA<\/abbr> \u00b7 Insurance Nexus<\/p><span class=\"nx-scene-rule\" aria-hidden=\"true\"><\/span><p class=\"nx-scene-accent nx-es-sig\"><strong>Spatial finance<\/strong><span>Where capital meets hazard, exposure and transition<\/span><\/p><h1 id=\"nx-scene-title\" class=\"nx-scene-title\">Extreme value theory for insurance tails<\/h1><p class=\"nx-scene-lede\">Extreme value theory gives the tail of the loss distribution its shape: how large a rare event loss may be, how losses accumulate in a bad year, and how uncertain those estimates become beyond the record. Capital, reinsurance and catastrophe pricing all rest on it.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/write-to-us\/contact\/\">Make an enquiry <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/stem\/\">Back to STEM foundation of Insurance Nexus<\/a><\/div><\/div><\/section><section id=\"nx-s-1-sec\" class=\"nx-section\" data-hue=\"finance\" data-tone=\"alt\" aria-labelledby=\"nx-s-1\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">01<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Contents<\/span><\/div><h2 id=\"nx-s-1\" class=\"nx-section-title\">Extreme value theory for insurance tails in five parts.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-es-fig\" data-es-layout=\"protocol\"><div class=\"nx-hero-panel\"><div class=\"nx-lv-steps\"><div class=\"nx-register-head\"><span class=\"nx-eyebrow\"><strong>Protocol<\/strong><\/span><span class=\"nx-eyebrow\">5 steps<\/span><\/div><ol><li><a href=\"#nx-sec-application\"><span class=\"nx-lv-steps-n\">01<\/span><span class=\"nx-lv-steps-t\">Application<\/span><span class=\"nx-lv-steps-d\">What extreme value theory and tail risk does here<\/span><\/a><\/li><li><a href=\"#nx-sec-data\"><span class=\"nx-lv-steps-n\">02<\/span><span class=\"nx-lv-steps-t\">Data<\/span><span class=\"nx-lv-steps-d\">What the method consumes<\/span><\/a><\/li><li><a href=\"#nx-sec-checks\"><span class=\"nx-lv-steps-n\">03<\/span><span class=\"nx-lv-steps-t\">Validation<\/span><span class=\"nx-lv-steps-d\">How results are checked<\/span><\/a><\/li><li><a href=\"#nx-sec-connections\"><span class=\"nx-lv-steps-n\">04<\/span><span class=\"nx-lv-steps-t\">Connections<\/span><span class=\"nx-lv-steps-d\">Topics that rely on this method<\/span><\/a><\/li><li><a href=\"#nx-sec-stewardship\"><span class=\"nx-lv-steps-n\">05<\/span><span class=\"nx-lv-steps-t\">Stewardship<\/span><span class=\"nx-lv-steps-d\">Who maintains the method<\/span><\/a><\/li><\/ol><\/div><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-application\" class=\"nx-section\" data-hue=\"climate\" data-tone=\"alt\" data-variant=\"split\" aria-labelledby=\"nx-s-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">02<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Application<\/span><\/div><h2 id=\"nx-s-2\" class=\"nx-section-title\">What extreme value theory and tail risk does here.<\/h2><p class=\"nx-lede\">Extreme value theory and <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 tail-risk\" data-tk=\"Nexus risk vocabulary\" data-td=\"Risk from outcomes at the far end of a distribution, which are rare but carry very large consequences and often dominate total expected harm.\" data-ts=\"NRS-C1\" data-tr=\"draft\">tail risk<\/a> 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, climate and hazards and health and biosecurity, extreme value methods set design levels and capital buffers for rare events. Tail dependence analysis reveals when extremes in different <a class=\"nx-link nx-term\" href=\"https:\/\/docs.therisk.global\/organization\/systems\" data-tc=\"Systems\" data-tk=\"Nexus Standards\" data-td=\"The twenty Nexus systems, NXS-CMD to NXS-LAB\">systems<\/a> tend to occur together. The canonical description is on <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/extreme-value\/\">the STEM Commons<\/a>.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-cols\"><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M22 12h-2.48a2 2 0 0 0-1.93 1.46l-2.35 8.36a.25.25 0 0 1-.48 0L9.24 2.18a.25.25 0 0 0-.48 0l-2.35 8.36A2 2 0 0 1 4.49 12H2\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Large-loss pricing for excess layers<\/h3><p class=\"nx-p nx-mt-1\">Losses above a threshold are fitted with a generalised Pareto distribution to price excess-of-loss reinsurance layers, with the threshold choice and its effect on the layer price shown.<\/p><\/article><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M20 13c0 5-3.5 7.5-7.66 8.95a1 1 0 0 1-.67-.01C7.5 20.5 4 18 4 13V6a1 1 0 0 1 1-1c2 0 4.5-1.2 6.24-2.72a1.17 1.17 0 0 1 1.52 0C14.51 3.81 17 5 19 5a1 1 0 0 1 1 1z\"\/><path d=\"m9 12 2 2 4-4\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Tail measures for solvency capital<\/h3><p class=\"nx-p nx-mt-1\">Loss distributions are extended into the tail to estimate <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 value-at-risk\" data-tk=\"Nexus risk vocabulary\" data-td=\"The loss level that will not be exceeded with a given confidence over a given period.\" data-ts=\"NRS-C1\" data-tr=\"draft\">value at risk<\/a> and tail value at risk at regulatory confidence levels, with <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 parameter-uncertainty\" data-tk=\"Nexus risk vocabulary\" data-td=\"Uncertainty in the values of the inputs or coefficients of a model, given its structure.\" data-ts=\"NRS-C1\" data-tr=\"draft\">parameter uncertainty<\/a> and alternative fits reported.<\/p><\/article><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><circle cx=\"12\" cy=\"4.5\" r=\"2.5\"\/><path d=\"m10.2 6.3-3.9 3.9\"\/><circle cx=\"4.5\" cy=\"12\" r=\"2.5\"\/><path d=\"M7 12h10\"\/><circle cx=\"19.5\" cy=\"12\" r=\"2.5\"\/><path d=\"m13.8 17.7 3.9-3.9\"\/><circle cx=\"12\" cy=\"19.5\" r=\"2.5\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Tail dependence across lines and perils<\/h3><p class=\"nx-p nx-mt-1\">Copulas and multivariate extreme value models examine whether extreme losses in different lines, regions or asset classes tend to occur together, which governs how much diversification credit is defensible.<\/p><\/article><\/div><\/div><\/div><\/section><section id=\"nx-sec-field\" class=\"nx-section\" data-hue=\"health\" aria-labelledby=\"nx-s-3\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">03<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Spatial finance<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">Exposure, hazard and the exceedance curve.<\/h2><p class=\"nx-lede\">Which places drive loss in a severe year, and how far it reaches into reinsurance. Illustrative design values.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><figure class=\"nx-fk nx-sf\" data-fk=\"cat-ep\" data-gl=\"sf:cat-ep\" data-sf=\"cat-ep\" data-sx-credit=\"Natural Earth (public domain) | design event set, not a model output\" data-sx-src=\"Natural Earth ~ public domain ~ https:\/\/www.naturalearthdata.com\/ | Design scenario values by the Global Risks Alliance ~ illustrative, not data\" data-sf-learn-views=\"ep\" aria-label=\"Interactive map: coastal exposure under cyclone basins and earthquake belts, linked to a loss exceedance curve. Choosing a return period on the curve marks the places that lose at it.\" data-nx-zf=\"figure.aa67601c1a0b.txt\" data-nx-zs=\"sha256-qmdgHBoLwbHYOZ49XGwQHMoz\/4JQIZssycOx6q5HvFo=\"><figcaption class=\"nx-fk-cap\"><span>Coastal exposure under cyclone and earthquake zones, beside the loss curve it produces.<\/span><span data-honesty=\"true\">Illustrative design values, not a model output or a price.<\/span><\/figcaption><\/figure><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"map\" data-nxf-kind=\"map\" data-nxf-resource=\"nexus:\/\/gra\/A01\/stem\/extreme-value\/#map\" data-nxf-title=\"Insurance Nexus live map\" data-nxf-height=\"560\" hidden><\/div><\/div><\/div><\/section><section class=\"nx-section\" data-band=\"auto\" data-band-id=\"nx-band-1\" data-hue=\"finance\" aria-label=\"Data, Monitor, Validation\"><div class=\"nx-container\"><div class=\"nx-hm-band nx-band\" data-nx-tabs=\"true\" data-active=\"p1\"><div class=\"nx-tablist nx-hm-tabs\" role=\"tablist\" aria-label=\"Data, Monitor, Validation\"><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-1-tab-1\" aria-controls=\"nx-sec-data\" aria-selected=\"true\" data-key=\"p1\">Data<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-1-tab-2\" aria-controls=\"nx-s-5-sec\" aria-selected=\"false\" data-key=\"p2\">Monitor<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-1-tab-3\" aria-controls=\"nx-sec-checks\" aria-selected=\"false\" data-key=\"p3\">Validation<\/button><\/div><section id=\"nx-sec-data\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" aria-labelledby=\"nx-s-4\" role=\"tabpanel\" data-band-tab=\"nx-band-1-tab-1\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">04<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Data<\/span><\/div><h2 id=\"nx-s-4\" class=\"nx-section-title\">What the method consumes.<\/h2><p class=\"nx-lede\">Datasets and records used by extreme value theory and <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 tail-risk\" data-tk=\"Nexus risk vocabulary\" data-td=\"Risk from outcomes at the far end of a distribution, which are rare but carry very large consequences and often dominate total expected harm.\" data-ts=\"NRS-C1\" data-tr=\"draft\">tail risk<\/a> on this platform, with the role each plays and the label its values carry. Sources are listed in <a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/research\/sources\/\">the source register<\/a>.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"register\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Data<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Label<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Data\">Large-loss histories<\/td><td data-label=\"Role\">Tail fitting<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Industry catastrophe loss records<\/td><td data-label=\"Role\">Event severity<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Data\"><a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 catastrophe-model\" data-tk=\"Nexus risk vocabulary\" data-td=\"A model combining a stochastic hazard event set, exposure and vulnerability functions to estimate the distribution of losses from rare, severe events.\" data-ts=\"NRS-C1\" data-tr=\"draft\">Catastrophe model<\/a> event sets<\/td><td data-label=\"Role\">Synthetic tail extension<\/td><td data-label=\"Label\"><span class=\"nx-chip\">MODELLED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Hazard maxima such as wind speed and rainfall<\/td><td data-label=\"Role\">Hazard tail behaviour<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"data\" data-nxf-kind=\"data\" data-nxf-resource=\"nexus:\/\/gra\/A01\/stem\/extreme-value\/#data\" data-nxf-title=\"Method record\" data-nxf-height=\"520\" hidden><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"evidence\" data-nxf-kind=\"evidence\" data-nxf-resource=\"nexus:\/\/gra\/A01\/stem\/extreme-value\/#evidence\" data-nxf-title=\"Evidence\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-s-5-sec\" class=\"nx-section nx-band-panel\" data-hue=\"climate\" aria-labelledby=\"nx-s-5\" role=\"tabpanel\" data-band-tab=\"nx-band-1-tab-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">05<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Monitor<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">Tail fit.<\/h2><p class=\"nx-lede\">The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.<\/p><\/div><div class=\"nx-section-body\"><figure class=\"nx-fk nx-cx\" data-fk=\"cx-insurance-method-tail\" data-cx=\"insurance.method-tail\" data-variant=\"section\" aria-label=\"Tail fit: illustrative console with example names and values\"><div class=\"nx-fk-head\"><div class=\"nx-fk-heading\"><h3 class=\"nx-fk-title\">Tail fit<\/h3><p class=\"nx-fk-sub\">Insurance \u00b7 Extreme value theory for insurance \u00b7 Run R-7661<\/p><\/div><div class=\"nx-fk-actions\"><button type=\"button\" class=\"nx-fk-btn\" data-fk-act=\"menu\" aria-haspopup=\"true\" aria-expanded=\"false\" hidden><svg viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M12 4v11\"\/><path d=\"m7 10 5 5 5-5\"\/><path d=\"M5 20h14\"\/><\/svg>Export<\/button><ul class=\"nx-fk-menu\" data-fk-menu=\"true\" hidden><li><button type=\"button\" data-fk-act=\"export-png\">Image<small>PNG of the map or chart<\/small><\/button><\/li><li><button type=\"button\" data-fk-act=\"export-csv\">Data<small>CSV of the figure&#8217;s values<\/small><\/button><\/li><\/ul><\/div><\/div><div class=\"nx-cx-status\"><span class=\"nx-fk-pill\" data-state=\"near\"><i aria-hidden=\"true\"><\/i>Shape parameter uncertain<\/span><span class=\"nx-cx-prod\">GRA \u00b7 Insurance Nexus<\/span><\/div><div class=\"nx-cx-grid\"><div class=\"nx-fk-panel nx-cx-p\" data-w=\"4\" data-t=\"chart\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Return level curve<\/h4><\/div><\/div><svg class=\"nx-fk-svg\" viewBox=\"0 0 460 210\" width=\"460\" height=\"210\" role=\"img\" aria-label=\"Return level curve\" focusable=\"false\" tabindex=\"0\" preserveAspectRatio=\"xMidYMid meet\" data-fk-chart=\"cx-insurance-method-tail-c0\" data-fk-xs=\"log,2,500,36,446\" data-fk-ys=\"lin,0,100,180,20\" data-fk-xf=\"rp\" data-fk-series=\"OEP|ink|l||2:3;5:9;10:16.4;20:25.2;50:35.3;100:46.7;200:59.5;500:73.6~AEP|acc|l||2:3.2;5:9.6;10:17.6;20:27.4;50:38.8;100:51.8;200:66.8;500:83.5\" data-nx-z=\"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\"><\/svg><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"2\" data-t=\"kv\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Method card<\/h4><\/div><\/div><dl class=\"nx-fk-dl\"><div><dt>Method<\/dt><dd>Extreme value theory and tail risk<\/dd><\/div><div><dt>Approach<\/dt><dd>peaks over threshold<\/dd><\/div><div><dt>Threshold<\/dt><dd>90th percentile<\/dd><\/div><div><dt>Label<\/dt><dd><span class=\"nx-fk-pill\" data-state=\"info\"><i aria-hidden=\"true\"><\/i>MODELLED<\/span><\/dd><\/div><\/dl><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"6\" data-t=\"table\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Parameters<\/h4><p class=\"nx-fk-panel-sub\">generalised Pareto fit<\/p><\/div><\/div><div class=\"nx-fk-tablewrap\" data-fk-view=\"table:cx-insurance-method-tail-t2\"><table class=\"nx-fk-table\" data-fk-table=\"cx-insurance-method-tail-t2\" aria-label=\"Parameters\"><thead><tr><th scope=\"col\" data-col=\"c0\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c0\">Parameter<\/button><\/th><th scope=\"col\" data-col=\"c1\" data-num=\"true\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c1\">Estimate<\/button><\/th><th scope=\"col\" data-col=\"c2\" data-num=\"true\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c2\">Lower<\/button><\/th><th scope=\"col\" data-col=\"c3\" data-num=\"true\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c3\">Upper<\/button><\/th><th scope=\"col\" data-col=\"c4\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c4\">Note<\/button><\/th><\/tr><\/thead><tbody><tr data-fk-key=\"cx-insurance-method-tail:r2-0\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Shape \u03be<\/td><td data-num=\"true\" data-sv=\"0.22\">0.22<\/td><td data-num=\"true\" data-sv=\"-0.03\">\u22120.03<\/td><td data-num=\"true\" data-sv=\"0.45\">0.45<\/td><td>heavy tail if above zero<\/td><\/tr><tr data-fk-key=\"cx-insurance-method-tail:r2-1\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Scale \u03c3<\/td><td data-num=\"true\" data-sv=\"2.7\">2.7<\/td><td data-num=\"true\" data-sv=\"1\">1.0<\/td><td data-num=\"true\" data-sv=\"6.9\">6.9<\/td><td>units of the variable<\/td><\/tr><tr data-fk-key=\"cx-insurance-method-tail:r2-2\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Exceedances<\/td><td data-num=\"true\" data-sv=\"88\">88<\/td><td data-num=\"true\"><\/td><td data-num=\"true\"><\/td><td>declustered<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><div class=\"nx-fk-tip\" aria-hidden=\"true\" hidden><\/div><figcaption class=\"nx-fk-cap\"><span>Tail fit: the screen&#8217;s structure, terms and states for Insurance.<\/span><span data-honesty=\"true\">Example names and illustrative values; a design, not a record or a live system.<\/span><\/figcaption><\/figure><\/div><\/div><\/section><section id=\"nx-sec-checks\" class=\"nx-section nx-band-panel\" data-hue=\"health\" data-variant=\"stack\" aria-labelledby=\"nx-s-6\" role=\"tabpanel\" data-band-tab=\"nx-band-1-tab-3\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">06<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Validation<\/span><\/div><h2 id=\"nx-s-6\" class=\"nx-section-title\">How results are checked.<\/h2><p class=\"nx-lede\">Fits are checked with diagnostic plots, goodness-of-fit tests and out-of-sample comparison. Sensitivity to threshold and record length is reported.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Check<\/th><th scope=\"col\">How it is done<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Check\">Threshold stability<\/td><td data-label=\"How it is done\">Tail parameters are estimated across a range of thresholds and the choice is justified by stability plots<\/td><\/tr><tr><td data-label=\"Check\">Goodness of fit in the tail<\/td><td data-label=\"How it is done\">Fits are judged on tail behaviour, not overall fit, using quantile plots and out-of-sample large losses<\/td><\/tr><tr><td data-label=\"Check\"><a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 parameter-uncertainty\" data-tk=\"Nexus risk vocabulary\" data-td=\"Uncertainty in the values of the inputs or coefficients of a model, given its structure.\" data-ts=\"NRS-C1\" data-tr=\"draft\">Parameter uncertainty<\/a><\/td><td data-label=\"How it is done\">Confidence intervals for return-period losses are reported, by bootstrap or profile likelihood<\/td><\/tr><tr><td data-label=\"Check\">Indexation and stationarity<\/td><td data-label=\"How it is done\">Losses are brought to a common basis for inflation and exposure, and trends are tested before fitting<\/td><\/tr><\/tbody><\/table><\/div><p class=\"nx-note nx-mt-3\">Extrapolation far beyond the data is highly uncertain. Changing climates and markets violate stationarity assumptions.<\/p><\/div><\/div><\/section><\/div><\/div><\/section><section class=\"nx-section\" data-band=\"auto\" data-band-id=\"nx-band-2\" data-tone=\"alt\" data-hue=\"finance\" aria-label=\"Loss model, Connections\"><div class=\"nx-container\"><div class=\"nx-hm-band nx-band\" data-nx-tabs=\"true\" data-active=\"p1\"><div class=\"nx-tablist nx-hm-tabs\" role=\"tablist\" aria-label=\"Loss model, Connections\"><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-2-tab-1\" aria-controls=\"nx-sec-instrument\" aria-selected=\"true\" data-key=\"p1\">Loss model<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-2-tab-2\" aria-controls=\"nx-sec-connections\" aria-selected=\"false\" data-key=\"p2\">Connections<\/button><\/div><section id=\"nx-sec-instrument\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" data-ns-layout=\"es-wide\" aria-labelledby=\"nx-s-7\" role=\"tabpanel\" data-band-tab=\"nx-band-2-tab-1\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">07<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Loss model<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">Loss exceedance, average annual loss and probable maximum loss.<\/h2><p class=\"nx-lede\">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 <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 catastrophe-model\" data-tk=\"Nexus risk vocabulary\" data-td=\"A model combining a stochastic hazard event set, exposure and vulnerability functions to estimate the distribution of losses from rare, severe events.\" data-ts=\"NRS-C1\" data-tr=\"draft\">catastrophe model<\/a> 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.<\/p><\/div><div class=\"nx-section-body\"><figure class=\"nx-in nx-fk\" id=\"nx-in-insurance-nexus-stem-extreme-value-0\" data-nxi=\"hevl\" data-nxi-id=\"hevl\" data-nxi-z=\"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\" aria-label=\"Loss exceedance, average annual loss and probable maximum loss\"><div class=\"nx-in-body\"><div class=\"nx-in-main\"><div class=\"nx-in-tabs\" data-nxi-tabs=\"true\" aria-label=\"Views of the model\" hidden><button type=\"button\" class=\"nx-in-tab\" data-nxi-view=\"result\">Result<\/button><button type=\"button\" class=\"nx-in-tab\" data-nxi-view=\"fan\">Uncertainty<\/button><button type=\"button\" class=\"nx-in-tab\" data-nxi-view=\"tornado\">Sensitivity<\/button><button type=\"button\" class=\"nx-in-tab\" data-nxi-view=\"loops\">Loops<\/button><\/div><div class=\"nx-in-stage\" id=\"nx-in-insurance-nexus-stem-extreme-value-0-stage\" data-nxi-stage=\"true\" data-view=\"result\"><svg class=\"nx-in-svg\" viewBox=\"0 0 720 340\" role=\"img\" aria-label=\"Loss exceedance curve\" data-nx-z=\"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\"><\/svg><\/div><p class=\"nx-in-tip\" data-nxi-tip=\"true\" aria-live=\"polite\"><\/p><div class=\"nx-in-bar\" data-nxi-bar=\"true\" role=\"group\" aria-label=\"Play over time\" hidden><button type=\"button\" class=\"nx-in-btn\" data-nxi-prev=\"true\" aria-label=\"Step back\">&#8249;<\/button><button type=\"button\" class=\"nx-in-btn\" data-nxi-play=\"true\" aria-pressed=\"false\">Play<\/button><button type=\"button\" class=\"nx-in-btn\" data-nxi-next=\"true\" aria-label=\"Step forward\">&#8250;<\/button><span class=\"nx-in-step\" data-nxi-step=\"true\" aria-live=\"polite\"><\/span><\/div><\/div><div class=\"nx-in-side\"><p class=\"nx-eyebrow\">Controls<\/p><div class=\"nx-in-ctl\" data-nxi-ctl=\"true\"><dl class=\"nx-in-pv\"><div><dt>Typical annual hazard intensity (location)<\/dt><dd>1.00 intensity index<\/dd><\/div><div><dt>Spread of extremes (scale)<\/dt><dd>0.80 intensity index<\/dd><\/div><div><dt>Intensity at which half the value is lost<\/dt><dd>4.00 intensity index<\/dd><\/div><div><dt>Dispersion of vulnerability<\/dt><dd>0.55<\/dd><\/div><div><dt>Share of value in the hazard footprint<\/dt><dd>40%<\/dd><\/div><div><dt>Protection standard (return period)<\/dt><dd>50 years<\/dd><\/div><\/dl><\/div><p class=\"nx-eyebrow\">Readouts<\/p><dl class=\"nx-in-out\" data-nxi-out=\"true\" aria-live=\"polite\"><div><dt>Average annual loss<\/dt><dd>0.51% of value<\/dd><\/div><div><dt>Loss at 1 in 100 years<\/dt><dd>24.5% of value<\/dd><\/div><div><dt>Loss at 1 in 250 years<\/dt><dd>28.4% of value<\/dd><\/div><div><dt>Intensity at 1 in 100 years<\/dt><dd>4.68 intensity index<\/dd><\/div><\/dl><\/div><\/div><details class=\"nx-in-more\"><summary>Numbers, methods and sources<\/summary><div class=\"nx-in-more-in\"><div class=\"nx-in-tblw\" data-nxi-table=\"true\"><table class=\"nx-in-tbl\"><thead><tr><th scope=\"col\">Return period (years)<\/th><th scope=\"col\">Intensity (intensity index)<\/th><th scope=\"col\">Loss (% of value)<\/th><\/tr><\/thead><tbody><tr><th scope=\"row\">1<\/th><td>0.09<\/td><td>0.00<\/td><\/tr><tr><th scope=\"row\">2<\/th><td>1.27<\/td><td>0.00<\/td><\/tr><tr><th scope=\"row\">4<\/th><td>1.91<\/td><td>0.00<\/td><\/tr><tr><th scope=\"row\">7<\/th><td>2.47<\/td><td>0.00<\/td><\/tr><tr><th scope=\"row\">13<\/th><td>3.00<\/td><td>0.00<\/td><\/tr><tr><th scope=\"row\">24<\/th><td>3.51<\/td><td>0.00<\/td><\/tr><tr><th scope=\"row\">44<\/th><td>4.02<\/td><td>0.00<\/td><\/tr><tr><th scope=\"row\">82<\/th><td>4.53<\/td><td>23.55<\/td><\/tr><tr><th scope=\"row\">154<\/th><td>5.03<\/td><td>26.44<\/td><\/tr><tr><th scope=\"row\">287<\/th><td>5.53<\/td><td>28.87<\/td><\/tr><tr><th scope=\"row\">536<\/th><td>6.03<\/td><td>30.88<\/td><\/tr><tr><th scope=\"row\">1,000<\/th><td>6.53<\/td><td>32.53<\/td><\/tr><\/tbody><\/table><\/div><button type=\"button\" class=\"nx-in-btn\" data-nxi-csv=\"true\" hidden>Download the numbers (CSV)<\/button><h4 class=\"nx-h4\">Methods<\/h4><ol class=\"nx-in-meth\"><li>Gumbel extreme-value hazard <span class=\"nx-in-cite\">Gumbel (1958), Statistics of Extremes, Columbia University Press<\/span><\/li><li>Lognormal fragility (vulnerability) <span class=\"nx-in-cite\">FEMA (2003), HAZUS-MH Technical Manual<\/span><\/li><li>Exceedance-probability curve and average annual loss <span class=\"nx-in-cite\">Grossi and Kunreuther (2005), Catastrophe Modeling, Springer<\/span><\/li><li>Levee effect <span class=\"nx-in-cite\">Di Baldassarre and others (2013), Socio-hydrology, HESS 17<\/span><\/li><li>Uncertainty fan (parameter Monte Carlo) <span class=\"nx-in-cite\">Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333<\/span><\/li><li>One-at-a-time sensitivity (tornado) <span class=\"nx-in-cite\">Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley<\/span><\/li><\/ol><h4 class=\"nx-h4\">Data<\/h4><p class=\"nx-p\">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&#8217;s map can draw:<\/p><ul class=\"nx-in-src\"><li>Rivers <span class=\"nx-in-cite\">Natural Earth<\/span><\/li><li>Urban extents <span class=\"nx-in-cite\">Natural Earth<\/span><\/li><li>Cities <span class=\"nx-in-cite\">Natural Earth<\/span><\/li><li>Earthquakes M4.5+, past week (USGS) <span class=\"nx-in-cite\">U.S. Geological Survey \u00b7 register usgs.earthquake.summary-feeds<\/span><\/li><li>Rain rate, last half hour (IMERG) <span class=\"nx-in-cite\">NASA GPM IMERG Early \u00b7 register nasa.gibs.context-live-layers<\/span><\/li><li>River discharge forecast, 30 days with range (GloFAS via Open-Meteo) <span class=\"nx-in-cite\">Open-Meteo.com (CC BY 4.0), Copernicus GloFAS \u00b7 register open-meteo.public-pages<\/span><\/li><li>Built-up and settlement extent, 2010 (HBASE, Landsat) <span class=\"nx-in-cite\">CIESIN\/Columbia, NASA SEDAC HBASE \u00b7 register sedac.context-urban-layers<\/span><\/li><\/ul><\/div><\/details><figcaption class=\"nx-caption\">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).<\/figcaption><div class=\"nx-in-pls\"><p class=\"nx-eyebrow\">Across the six pillars, led by Nexus Consortium<\/p><ul class=\"nx-in-pl\" aria-label=\"Where this instrument connects across the pillars\"><li data-pillar=\"labs\" data-hue=\"social\"><span class=\"nx-pl-dot\" aria-hidden=\"true\"><\/span><span class=\"nx-in-plv\">Experiments<\/span> <a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-labs\/\">Labs<\/a><\/li><li data-pillar=\"foundry\" data-hue=\"infrastructure\"><span class=\"nx-pl-dot\" aria-hidden=\"true\"><\/span><span class=\"nx-in-plv\">Built in<\/span> <a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/foundry\/\">Insurance Nexus in Foundry<\/a><\/li><li data-pillar=\"universe\" data-hue=\"water\"><span class=\"nx-pl-dot\" aria-hidden=\"true\"><\/span><span class=\"nx-in-plv\">Tested at<\/span> <a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/universe\/\">Insurance Nexus in Universe<\/a><\/li><li data-pillar=\"standards\" data-hue=\"biodiversity\"><span class=\"nx-pl-dot\" aria-hidden=\"true\"><\/span><span class=\"nx-in-plv\">Codified in<\/span> <a class=\"nx-link\" href=\"https:\/\/nexustandards.com\/finance\/\">Finance, Insurance and the Economy in Standards<\/a><\/li><li data-pillar=\"reports\" data-hue=\"energy\"><span class=\"nx-pl-dot\" aria-hidden=\"true\"><\/span><span class=\"nx-in-plv\">Published in<\/span> <a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/reports\/\">Insurance Nexus in Reports<\/a><\/li><li data-pillar=\"academy\" data-hue=\"risk\"><span class=\"nx-pl-dot\" aria-hidden=\"true\"><\/span><span class=\"nx-in-plv\">Taught in<\/span> <a class=\"nx-link\" href=\"https:\/\/risksacademy.com\/finance-economics\/\">Faculty of Finance and Economics in Academy<\/a><\/li><\/ul><\/div><span class=\"nx-in-keep\" hidden><i data-c=\"c1\"><\/i><i data-c=\"c2\"><\/i><i data-c=\"c3\"><\/i><i data-c=\"c4\"><\/i><i data-c=\"c5\"><\/i><i data-c=\"c6\"><\/i><i data-c=\"l0\"><\/i><i data-c=\"l1\"><\/i><i data-c=\"l2\"><\/i><i data-c=\"l3\"><\/i><i data-c=\"l5\"><\/i><i data-c=\"h3\"><\/i><i data-c=\"neg\"><\/i><i data-c=\"on0\"><\/i><i data-c=\"on1\"><\/i><i data-l=\"0\"><\/i><i data-l=\"1\"><\/i><i data-l=\"2\"><\/i><i data-l=\"3\"><\/i><i data-l=\"4\"><\/i><i data-l=\"5\"><\/i><i data-on=\"0\"><\/i><i data-on=\"1\"><\/i><i data-pol=\"p\"><\/i><i data-pol=\"n\"><\/i><i data-k=\"R\"><\/i><i data-k=\"B\"><\/i><i data-k=\"dash\"><\/i><i data-dash=\"true\"><\/i><i data-view=\"result\"><\/i><i data-nxi-live=\"on\"><\/i><i data-nxi-live=\"off\"><\/i><i class=\"nx-in-svg nx-in-grid nx-in-ax nx-in-tk nx-in-at nx-in-ln nx-in-band nx-in-lbl nx-in-lk nx-in-ref nx-in-reft nx-in-mk nx-in-x nx-in-rect nx-in-val nx-in-rowl nx-in-cl nx-in-arrow nx-in-pol nx-in-cln nx-in-loop nx-in-edge nx-in-node nx-in-nl nx-in-reach nx-in-trib nx-in-agent nx-in-flow nx-in-legend nx-in-sw nx-in-note nx-in-loops nx-in-3d nx-in-cv nx-in-field nx-in-fl nx-in-fv nx-in-range nx-in-btn nx-in-err nx-in-tbl nx-in-cite\"><\/i><\/span><\/figure><\/div><\/div><\/section><section id=\"nx-sec-connections\" class=\"nx-section nx-band-panel\" data-hue=\"climate\" data-variant=\"split\" aria-labelledby=\"nx-s-8\" role=\"tabpanel\" data-band-tab=\"nx-band-2-tab-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">08<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connections<\/span><\/div><h2 id=\"nx-s-8\" class=\"nx-section-title\">Topics that rely on this method.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-list\" data-nav=\"true\"><li><a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/topics\/insurance-market-structure\/\"><span>Insurance market structure<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/topics\/underwriting-and-pricing-risk\/\"><span>Underwriting and pricing risk<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/topics\/property-and-casualty\/\"><span>Property and casualty<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/topics\/health-and-life-insurance\/\"><span>Health and life insurance<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/topics\/insurance-protection-gaps\/\"><span>Insurance protection gaps<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/topics\/climate-and-nature-risk\/\"><span>Climate and nature risk<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/topics\/takaful-risk\/\"><span>Takaful risk<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><\/ul><\/div><\/div><\/section><\/div><\/div><\/section><section id=\"nx-sec-twin\" class=\"nx-section\" data-hue=\"health\" data-span=\"wide\" data-ns-layout=\"twin\" aria-labelledby=\"nx-s-9\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">09<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Twin and cascade simulator<\/span><\/div><h2 id=\"nx-s-9\" class=\"nx-section-title\">Trace a shock in extreme value theory for insurance tails to the balance sheet.<\/h2><p class=\"nx-lede\">Assembled from the <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/\" data-tc=\"Nexus Standards\" data-tk=\"Nexus Standards\" data-td=\"The foundation standards\">Nexus Standards<\/a> area profile <a class=\"nx-link\" href=\"https:\/\/nexustandards.com\/finance\/insurance-and-reinsurance\/\">Insurance and reinsurance<\/a>: the shocks it names, the <a class=\"nx-link nx-term\" href=\"https:\/\/docs.therisk.global\/organization\/systems\" data-tc=\"Systems\" data-tk=\"Nexus Standards\" data-td=\"The twenty Nexus systems, NXS-CMD to NXS-LAB\">systems<\/a> 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 <a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/system-builder\/\">System Builder<\/a>.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-ns-twin\" data-ns-data=\"nx-twin-es-stem-extreme-value-A01\" data-ns-credits=\"Nexus Standards registry and area profiles; Nexus Hazard Standard NHS-1 1.1.0\"><div class=\"nx-ns-twin-static\"><div><p class=\"nx-eyebrow\">Hazards<\/p><ul class=\"nx-list\"><li><a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/hazards\/hydrometeorological-hazard-standards\/\" data-tc=\"NHS-1 atmo.cyclone.tropical-cyclone\" data-tk=\"Atmosphere and weather \u00b7 Cyclonic storm systems\" data-td=\"A warm-core rotating storm over tropical oceans whose winds, rain and surge threaten coasts and inland river basins.\" data-ts=\"NRS-H2\" data-tr=\"draft\">Tropical cyclone<\/a><\/li><li><a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/hazards\/geophysical-hazard-standards\/\" data-tc=\"NHS-1 geo.seismic.earthquake\" data-tk=\"Solid Earth \u00b7 Fault shaking and rupture\" data-td=\"Sudden slip on a fault releasing seismic waves that shake the ground and damage structures and lifelines.\" data-ts=\"NRS-H17\" data-tr=\"draft\">Earthquake<\/a><\/li><li><a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-h9\" data-tc=\"NHS-1 water.flood.ice-jam-flood\" data-tk=\"Water systems \u00b7 Freshwater inundation\" data-td=\"River water backing up behind an ice blockage and flooding adjacent land, sometimes with ice and debris.\" data-ts=\"NRS-H9\" data-tr=\"draft\">Ice-jam flood<\/a><\/li><li><a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/hazards\/wildfire-and-vegetation-fire-standards\/\" data-tc=\"NHS-1 eco.wildfire.wildfire\" data-tk=\"Land and ecosystems \u00b7 Vegetation and peat fire\" data-td=\"An uncontrolled fire in forest, scrub or grass that burns structures, crops and habitat and fills the air with smoke.\" data-ts=\"NRS-H24\" data-tr=\"draft\">Wildfire<\/a><\/li><li><a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-h11\" data-tc=\"NHS-1 water.coastal.storm-surge\" data-tk=\"Water systems \u00b7 Coastal and sea-level hazards\" data-td=\"Sea level pushed above the tide by storm wind and low pressure, flooding the coast.\" data-ts=\"NRS-H11\" data-tr=\"draft\">Storm surge<\/a> (set off downstream)<\/li><li><a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-h4\" data-tc=\"NHS-1 atmo.precipitation.heavy-rain\" data-tk=\"Atmosphere and weather \u00b7 Precipitation extremes\" data-td=\"Rain falling fast or long enough to overwhelm drainage, soils and streams and to trigger floods and slope failures.\" data-ts=\"NRS-H4\" data-tr=\"draft\">Intense rainfall<\/a> (set off downstream)<\/li><li>River flood (set off downstream)<\/li><li>Flash flood (set off downstream)<\/li><li>Dangerous surf (set off downstream)<\/li><li>Widespread power outage (set off downstream)<\/li><li>Mass displacement (set off downstream)<\/li><li>Coastal flood (set off downstream)<\/li><li>Aftershock sequence (set off downstream)<\/li><li>Fault surface rupture (set off downstream)<\/li><li>Soil liquefaction (set off downstream)<\/li><li>Slope slide (set off downstream)<\/li><li>Rockfall (set off downstream)<\/li><li>Tsunami (set off downstream)<\/li><\/ul><\/div><div><p class=\"nx-eyebrow\">Systems and couplings<\/p><ul class=\"nx-list\"><li><a class=\"nx-link\" href=\"https:\/\/nexustandards.com\/finance\/insurance-and-reinsurance\/\">Insurance and reinsurance<\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/nexustandards.com\/finance\/banking\/\">Banking<\/a> <span class=\"nx-ns-sub\">Coupling<\/span><\/li><li><a class=\"nx-link\" href=\"https:\/\/nexustandards.com\/finance\/development-finance-institutions\/\">Development finance institutions<\/a> <span class=\"nx-ns-sub\">Coupling<\/span><\/li><li><a class=\"nx-link\" href=\"https:\/\/nexustandards.com\/humanitarian\/humanitarian-sector-standards\/\">Humanitarian and civil society<\/a> <span class=\"nx-ns-sub\">Coupling<\/span><\/li><li><a class=\"nx-link\" href=\"https:\/\/nexustandards.com\/water-food\/agriculture-sector-standards\/\">Agriculture and land use<\/a> <span class=\"nx-ns-sub\">Coupling<\/span><\/li><\/ul><\/div><div><p class=\"nx-eyebrow\">Controls<\/p><ul class=\"nx-list\"><li><code>NS-0<\/code> Constitution and principles<\/li><li><code>NS-3<\/code> Risk record UNIs (grammar, legacy crosswalk, allocation authority)<\/li><li><code>NS-9<\/code> Contextual UNIs (deployments, instances, programme instances)<\/li><li><code>NS-10<\/code> Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)<\/li><li><code>NS-11<\/code> Verification and monitoring (evidence, attestations, zero-trust conformance observation)<\/li><li><code>NS-14<\/code> Conformance and crosswalk to external standards<\/li><li><code>NRS-F3<\/code> Insurance, catastrophe risk and protection gaps<\/li><li><code>NRS-H113<\/code> Hazard family: Financial-system fragility (NHS-1 soc.financial-system)<\/li><li><code>NRS-U17<\/code> <a class=\"nx-link\" href=\"https:\/\/docs.therisk.global\/organization\/programme\/universe\">Universe<\/a> platform profile: Insurance Nexus<\/li><li><code>NRS-H71<\/code> Hazard family: Deep-time and cosmic catastrophes (NHS-1 planet.deep-time)<\/li><li><code>NRS-H65<\/code> Hazard family: Planetary boundary transgressions (NHS-1 planet.boundary)<\/li><li><code>NRS-F8<\/code> Parametric and index-based risk transfer<\/li><\/ul><\/div><div><p class=\"nx-eyebrow\">Observation<\/p><ul class=\"nx-list\"><li>OECD Data Explorer (incl. Creditor Reporting System, insurance and pension statistics) (listed for reference)<\/li><li>Insurance statistics (listed for reference)<\/li><li>USGS earthquake GeoJSON summary feeds (listed for reference)<\/li><li>Direct economic loss attributed to disasters in relation to global gross domestic product (GDP)<\/li><li>Direct economic loss attributed to disasters in relation to global gross domestic product<\/li><li>(a) Number of commercial bank branches per 100,000 adults and (b) number of automated teller machines (ATMs) per 100,000 adults<\/li><\/ul><\/div><div><p class=\"nx-eyebrow\">Example geography<\/p><ul class=\"nx-list\"><li>Western Europe: 152 hexagonal cells<\/li><li>Rain rate, last half hour (IMERG), NASA GIBS<\/li><li>Night lights tonight, outages (VIIRS Day\/Night Band), NASA GIBS<\/li><li>Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS<\/li><li>Carbon monoxide, smoke transport (AIRS), NASA GIBS<\/li><\/ul><\/div><\/div><\/div><\/div><\/div><\/section><section class=\"nx-section\" data-band=\"auto\" data-band-id=\"nx-band-3\" data-hue=\"finance\" aria-label=\"Stewardship, Exposure, loss and capital, Connected, Questions\"><div class=\"nx-container\"><div class=\"nx-hm-band nx-band\" data-nx-tabs=\"true\" data-active=\"p1\"><div class=\"nx-tablist nx-hm-tabs\" role=\"tablist\" aria-label=\"Stewardship, Exposure, loss and capital, Connected, Questions\"><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-3-tab-1\" aria-controls=\"nx-sec-stewardship\" aria-selected=\"true\" data-key=\"p1\">Stewardship<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-3-tab-2\" aria-controls=\"nx-sec-audience\" aria-selected=\"false\" data-key=\"p2\">Exposure, loss and capital<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-3-tab-3\" aria-controls=\"nx-s-12-sec\" aria-selected=\"false\" data-key=\"p3\">Connected<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-3-tab-4\" aria-controls=\"nx-s-13-sec\" aria-selected=\"false\" data-key=\"p4\">Questions<\/button><\/div><section id=\"nx-sec-stewardship\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" aria-labelledby=\"nx-s-10\" role=\"tabpanel\" data-band-tab=\"nx-band-3-tab-1\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">10<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Stewardship<\/span><\/div><h2 id=\"nx-s-10\" class=\"nx-section-title\">Who maintains the method.<\/h2><p class=\"nx-lede\"><a class=\"nx-link\" href=\"https:\/\/globalriskalliance.com\/reinsurance-catastrophe\/\">Reinsurance and Catastrophe<\/a> stewards extreme value theory and <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 tail-risk\" data-tk=\"Nexus risk vocabulary\" data-td=\"Risk from outcomes at the far end of a distribution, which are rare but carry very large consequences and often dominate total expected harm.\" data-ts=\"NRS-C1\" data-tr=\"draft\">tail risk<\/a> for the Consortium and reviews how it is applied across platforms.<\/p><\/div><div class=\"nx-section-body\"><p class=\"nx-p\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/extreme-value\/\">Canonical page: Extreme value theory and tail risk<\/a><\/p><\/div><\/div><\/section><section id=\"nx-sec-audience\" class=\"nx-section nx-band-panel\" data-hue=\"climate\" aria-labelledby=\"nx-s-11\" role=\"tabpanel\" data-band-tab=\"nx-band-3-tab-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">11<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Exposure, loss and capital<\/span><\/div><h2 id=\"nx-s-11\" class=\"nx-section-title\">What extreme value theory for insurance tails means for exposure, loss and capital.<\/h2><p class=\"nx-lede\">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.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-au\" data-au-voice=\"finance\" data-au-n=\"1\"><figure class=\"nx-au-c\" data-au-fig=\"exposure-loss\"><h3 class=\"nx-h4\">Exposure and loss at return periods<\/h3><p class=\"nx-p nx-au-what\">How losses from extreme value theory for insurance tails grow with severity, gross and net of reinsurance, against capital.<\/p><div class=\"nx-au-cols\"><div class=\"nx-au-fig\"><svg class=\"nx-au-svg\" viewBox=\"0 0 560 214\" role=\"img\" aria-label=\"Exceedance probability curve, gross and net loss by return period\" focusable=\"false\"><line class=\"nx-au-ax\" x1=\"50\" y1=\"186\" x2=\"540\" y2=\"186\"\/><line class=\"nx-au-ax\" x1=\"50\" y1=\"16\" x2=\"50\" y2=\"186\"\/><path class=\"nx-au-ln\" d=\"M50 156.3l163.3-40.4 163.4-27.6 163.3-46.8\"\/><path class=\"nx-au-ac\" d=\"M50 164.8l163.3-23.4 163.4-12.8 163.3-25.5\"\/><text class=\"nx-au-t\" x=\"50\" y=\"202\" text-anchor=\"middle\">1 in 10<\/text><circle class=\"nx-au-nd nx-au-nd-ac\" cx=\"50.0\" cy=\"164.8\" r=\"3.5\"\/><text class=\"nx-au-t\" x=\"213.33\" y=\"202\" text-anchor=\"middle\">1 in 50<\/text><circle class=\"nx-au-nd nx-au-nd-ac\" cx=\"213.3\" cy=\"141.4\" r=\"3.5\"\/><text class=\"nx-au-t\" x=\"376.67\" y=\"202\" text-anchor=\"middle\">1 in 100<\/text><circle class=\"nx-au-nd nx-au-nd-ac\" cx=\"376.7\" cy=\"128.6\" r=\"3.5\"\/><text class=\"nx-au-t\" x=\"540\" y=\"202\" text-anchor=\"middle\">1 in 250<\/text><circle class=\"nx-au-nd nx-au-nd-ac\" cx=\"540.0\" cy=\"103.1\" r=\"3.5\"\/><text class=\"nx-au-t\" x=\"534\" y=\"33.5\" text-anchor=\"end\">gross<\/text><text class=\"nx-au-t\" x=\"526\" y=\"129.13\" text-anchor=\"end\">net of reinsurance<\/text><text class=\"nx-au-t\" x=\"44\" y=\"20\" text-anchor=\"end\">80<\/text><text class=\"nx-au-t\" x=\"44\" y=\"190\" text-anchor=\"end\">0<\/text><\/svg><\/div><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Return period<\/th><th scope=\"col\">Annual probability<\/th><th scope=\"col\">Gross loss<\/th><th scope=\"col\">Net loss<\/th><th scope=\"col\">Net, capital = 100<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Return period\">1 in 10<\/td><td data-label=\"Annual probability\">0.10<\/td><td data-label=\"Gross loss\">14<\/td><td data-label=\"Net loss\">10<\/td><td data-label=\"Net, capital = 100\"><span class=\"nx-au-num\">10<\/span><\/td><\/tr><tr><td data-label=\"Return period\">1 in 50<\/td><td data-label=\"Annual probability\">0.02<\/td><td data-label=\"Gross loss\">33<\/td><td data-label=\"Net loss\">21<\/td><td data-label=\"Net, capital = 100\"><span class=\"nx-au-num\">21<\/span><\/td><\/tr><tr><td data-label=\"Return period\">1 in 100<\/td><td data-label=\"Annual probability\">0.01<\/td><td data-label=\"Gross loss\">46<\/td><td data-label=\"Net loss\">27<\/td><td data-label=\"Net, capital = 100\"><span class=\"nx-au-num\">27<\/span><\/td><\/tr><tr><td data-label=\"Return period\">1 in 250<\/td><td data-label=\"Annual probability\">0.004<\/td><td data-label=\"Gross loss\">68<\/td><td data-label=\"Net loss\">39<\/td><td data-label=\"Net, capital = 100\"><span class=\"nx-au-num\">39<\/span><\/td><\/tr><tr><td data-label=\"Return period\">Average annual loss<\/td><td data-label=\"Annual probability\"><\/td><td data-label=\"Gross loss\">3.1<\/td><td data-label=\"Net loss\">2.2<\/td><td data-label=\"Net, capital = 100\">2.2<\/td><\/tr><\/tbody><\/table><\/div><\/div><figcaption class=\"nx-caption\"><span>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.<\/span><\/figcaption><\/figure><\/div><div class=\"nx-chips nx-au-fw\" aria-label=\"Frameworks cited\"><span class=\"nx-chip\">NGFS climate scenarios \u00b7 Network for Greening the Financial System<\/span><span class=\"nx-chip\">IFRS S2 Climate-related Disclosures \u00b7 ISSB<\/span><span class=\"nx-chip\">TCFD recommendations \u00b7 Financial Stability Board TCFD<\/span><span class=\"nx-chip\">Principles for the effective management of climate-related financial risks \u00b7 Basel Committee on Banking Supervision<\/span><\/div><\/div><\/div><\/section><section id=\"nx-s-12-sec\" class=\"nx-section nx-band-panel\" data-hue=\"health\" aria-labelledby=\"nx-s-12\" role=\"tabpanel\" data-band-tab=\"nx-band-3-tab-3\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">12<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connected<\/span><\/div><h2 id=\"nx-s-12\" class=\"nx-section-title\">Where this page leads across the Consortium.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-dest\"><li><a class=\"nx-dest-card\" href=\"https:\/\/globalriskalliance.com\/reinsurance-catastrophe\/stem\/extreme-value\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Steward platform<\/span><span class=\"nx-dest-host\">GRA<\/span><\/span><span class=\"nx-dest-title\">Extreme value theory and tail risk for reinsurance and catastrophe<\/span><span class=\"nx-dest-why\">Reinsurance and Catastrophe stewards this method.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><\/ul><\/div><\/div><\/section><section id=\"nx-s-13-sec\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" aria-labelledby=\"nx-s-13\" role=\"tabpanel\" data-band-tab=\"nx-band-3-tab-4\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">13<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Questions<\/span><\/div><h2 id=\"nx-s-13\" class=\"nx-section-title\">Questions and answers.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-faq\"><details><summary>What are the limits of extreme value theory and tail risk in this field?<\/summary><div class=\"nx-answer\">Extrapolation far beyond the data is highly uncertain. Changing climates and markets violate stationarity assumptions.<\/div><\/details><details><summary>How are results checked?<\/summary><div class=\"nx-answer\">Fits are checked with diagnostic plots, goodness-of-fit tests and out-of-sample comparison. Sensitivity to threshold and record length is reported.<\/div><\/details><details><summary>Are the values on this page real?<\/summary><div class=\"nx-answer\">The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method&#8217;s use, not a run; no result is reported here.<\/div><\/details><details><summary>Where else is this method applied?<\/summary><div class=\"nx-answer\">The <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/extreme-value\/\">canonical method page<\/a> lists every platform that applies it.<\/div><\/details><\/div><\/div><\/div><\/section><\/div><\/div><\/section><section id=\"nx-sec-nr-gateway\" class=\"nx-section\" data-hue=\"climate\" data-tone=\"alt\" aria-labelledby=\"nx-nr-gateway-h\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">Reports<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Contribute<\/span><\/div><h2 id=\"nx-nr-gateway-h\" class=\"nx-section-title\">Contribute evidence from this page.<\/h2><p class=\"nx-lede\"><a class=\"nx-link\" href=\"https:\/\/docs.therisk.global\/organization\/programme\/reports\">Reports<\/a> is the Consortium&#8217;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.<\/p><div class=\"nx-chips\"><span class=\"nx-chip\">Global Risks Alliance<\/span><span class=\"nx-chip\">Insurance Nexus<\/span><span class=\"nx-chip\">Finance and Insurance Readiness<\/span><span class=\"nx-chip\">Global Sector Risk and Resilience<\/span><\/div><\/div><div class=\"nx-section-body\"><ul class=\"nx-dest\"><li><a class=\"nx-dest-card\" href=\"https:\/\/globalriskalliance.com\/insurance-nexus\/write-to-us\/contact\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Email<\/span><span class=\"nx-dest-host\">Gateway GW-GRA<\/span><\/span><span class=\"nx-dest-title\">Send it from this page<\/span><span class=\"nx-dest-why\">Evidence, a correction, a case, a method, a review, a translation or profile input. It arrives tagged with this page, so the editor who owns it receives it.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><li><a class=\"nx-dest-card\" href=\"https:\/\/globalriskalliance.com\/nexus-reports\/open-science\/#nx-sec-zenodo\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Zenodo<\/span><span class=\"nx-dest-host\">Open deposit<\/span><\/span><span class=\"nx-dest-title\">Deposit it and submit to the community<\/span><span class=\"nx-dest-why\">Publish your own record on Zenodo with your ORCID iD, then submit it to the Reports community there. Curators review it; the DOI stays yours.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><li><a class=\"nx-dest-card\" href=\"https:\/\/globalriskalliance.com\/nexus-reports\/contribute\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Reports<\/span><span class=\"nx-dest-host\">Global Risks Alliance<\/span><\/span><span class=\"nx-dest-title\">See every way to contribute<\/span><span class=\"nx-dest-why\">The contribution types, how each is reviewed, what credit you receive, the licence and what is never accepted.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><\/ul><div class=\"nx-live-slot\" data-nxf-slot=\"contribute\" data-nxf-kind=\"workflow\" data-nxf-resource=\"nexus:\/\/gra\/insurance-nexus\/stem\/extreme-value#contribute\" data-nxf-title=\"Contribute evidence\" data-nxf-height=\"480\" hidden><\/div><p class=\"nx-note nx-mt-3\">Never send personal data, sensitive infrastructure detail or anything you have no right to share. A contribution is reviewed before use; sending it confers no authorship or editorial rights, and sponsorship never buys a finding.<\/p><\/div><\/div><\/section><section class=\"nx-section nx-deeper-band\" aria-labelledby=\"nx-deeper-kalliance-com-insurance-nexus-stem-extreme-value-h\"><div class=\"nx-container\"><nav class=\"nx-deeper\" aria-labelledby=\"nx-deeper-kalliance-com-insurance-nexus-stem-extreme-value-h\" data-nx-deeper=\"3\" data-nx-resources=\"1 1 1\"><h2 class=\"nx-deeper-h\" id=\"nx-deeper-kalliance-com-insurance-nexus-stem-extreme-value-h\">Go deeper<\/h2><ul><li><a href=\"https:\/\/docs.therisk.global\/organization\/model\/scenarios\/urban-flood\" data-ext><span class=\"nx-deeper-k\">Documentation:<\/span> Urban flood<span class=\"nx-deeper-x\" aria-hidden=\"true\">\u2197<\/span><span class=\"nx-deeper-sr\"> (opens another site)<\/span><\/a><\/li><li><a href=\"https:\/\/risksacademy.com\/finance-economics\/insurance-reinsurance\/\" data-ext><span class=\"nx-deeper-k\">Academy:<\/span> Insurance, reinsurance and protection gaps<span class=\"nx-deeper-x\" aria-hidden=\"true\">\u2197<\/span><span class=\"nx-deeper-sr\"> (opens another site)<\/span><\/a><\/li><li><a href=\"https:\/\/nexustandards.com\/finance\/financial-and-economic-risk-standards\/\" data-ext><span class=\"nx-deeper-k\">Standards:<\/span> Financial and economic risk standards<span class=\"nx-deeper-x\" aria-hidden=\"true\">\u2197<\/span><span class=\"nx-deeper-sr\"> (opens another site)<\/span><\/a><\/li><\/ul><\/nav><\/div><\/section><section class=\"nx-closing\" aria-labelledby=\"nx-close-14\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Insurance Nexus<\/span><h2 id=\"nx-close-14\" class=\"nx-section-title\">Bring an exposure or capital question to the Alliance.<\/h2><p class=\"nx-lede\">Information and enquiries are open today. 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