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F5\/s9XCNR1uN\/AxTKs3WvWkdlU7cCiV5Zv5L+e+Iz8kNRstXuIBMKEoNJuW9iBEaiBD7GUrJUA8K54DZlvngEyrwjEkdxY4yR02YTXK5RtRVWEND6RpPmTI+ZAi50O\/cgcSQq4Bw6BQKsFvYE+WbGhpDJOInEljC5g7DH0mjmkzwx5b0QnTmsAs0b9QQYMnwLRFrUoOPbPsfIPzwCkoA6uJ7T8lig+dB0LIiZjRQ3YWOEmJn8WbklDLKd42uTG6\/2PkqThEnuDVQjspPSXyIyCIZ6WHPcxpkjF0PaaaB2cpqGcvpG0b3Gv212tvY2leAhYhsyjMeAgniT1C0rUaJms1Klur4UvWaoRXY8GoRwJNRgXuxaxB4k23GhVQO\/2mo2Z64Omq550LDa\/ghTvUDo\/HqZqTm3mYEQyHMWUfuLY43brM4MYVY+L6QrFEkDAdzsg477r3V6bf\/otKLrzB+gkMQEXuPULGOfR2h9oYUHkXn8YrgJTrT7OdwqmE2hinE2MgElIEgK\/XmxOSKk1P9GSWYi44bewjaZof1cY+isY+po3Neh+Fg+k5FQ1GcBTWtG3V23r9W7OJfzZ+40t4ctwEXUYRimJMrq+d6OnwzmlxfLiOwLjM+72Vp\/tnz6CKsOsOtdvOXjfZ6F31YHuht9408zqVz7JKuvq3QkN5oXEL0bPG+qcXGOtnmWAJqHGyR8gsAfqJRIdZ72TbXP8EopEd+L5QOnzO9GVFA33rMLXQZz9BP\/eUmuEZTpV0clYR\/T6lMTvx3QmnKBeRNcmUgKVftc9S\/fAZdFOZjy0\/06ePyaEj2nE+Gnvq0cgiTdM6ihKoWACVqGf1K07+Yw+p3VMtwitfS4\/2BduSEAYjw6aex0VvU8iDzIiCeZyOYUGa5uIoTMawkCsT9hZ9XjXueX28yj2Wcdox6PY916d8iLmjnyUq5g6A8l1v\/GxjUWUBaAGVSzph4AJKBTMaKdtEIi+AqsCyoi2WxRyFUYGCSrOwEDO2e+Myuev\/f9CTnJdHLTim+EJZ\/FhpPXfcGcLmY4VujblhJhoVC3H7IFhrAg\/KQQFJHzmhmOYs348KaWF8UzzmD9WofBC2FTnQCY0mUp3lk14PibEJv169VFnGbIT7elqSQTGlQ+WTU9WCg3A\/4\/GUNE2aHbhp4mbi92i\/1+orXKmgdCt6P4aWDzZHxfGWN9HsmzlFG5ENm485QEZiRa3n2rdUx1450yGSW\/6Zunl9h1YOnUd51K1XfI+EEOVE1pChDqp9XZz+u8af9Jrr1+gipBGrgUstnaM2kqsbVo5tgLAZ5gZW3m8M3ipVX+qo9nV62Prt36iN3tPFNK51meU7VuTEyQfTysHbw4UXIS6siKVgvmDLgmhOK5XkgvCxnd+HXP4YssaC\/4GdP6w\/SpHgL65hXwhNe+NXvBqS5CFXpPGrNO6Npcq9sQANfP1XaC55l7YqBI16683xtiCDV9AIWAJ5C418ncavm0cp1sBgMynrV30o5BYz+c6CkIyEKJOxKW4t1YXrGFjQ\/84Y+mMFp+OEASCfw4iylYieWQmL+I2Ij7veeiU3CG1EHFCtN5zkFgAfZYDPKhcg33pVCRKrBCReASR\/a0AhFXqafbqiOR+UMlSXWirhOytgUL5LPTqjnnSyxUYcei7T0BolgNky0IlK3ONKrXLwNa4fZEIL2D69IyshlV5yGAqJxWUD0U1DOoL1mqA4lHanBXk\/nQxopIVAU9OdssunJDn4gzsJlb4kcgd1xip6GCxyJxpu0+RcO6yjdg3Vtai36K\/XiAOjhuoLnBx2kbIiNznzTGYOLeg\/mBv29VovBVS5SbTGXR5AX1t21OHsNI13o17b12Ea7dLV4IU30lvDr9L99pTlls42ihMSH81Wd1fC\/Ua0\/ZNUkFWLHLBL\/6kJGVTu0ISEoa1CTThw+HuE\/IRXPxHfZFTzE0vLRo5LTB\/WKHXdlVNUaAmlGuUIoUPfQAY+p7++YBFM8YxApRyoZM9\/a1U1tzWWwuWKpdq72LZC0OwpTVW0tS1A\/i\/hm0LVVLR09CUtZ0iP8u5tCtgTx7YqIsyXpQqzdUmaJc09ONQjybfwLlAFU4b0vxWh06e7bS0AIY+OqO\/UjmsgB8kd09drFZ9YQmhouQlp6FLPkVstERvX62zXSbjkvdgsLaGDLyPsY0IrT5KS\/OS1VEfjEh1V5isAhsCp5wG718bbiqqd9D\/VsC\/qqIHqEch\/De7uArosaKxEwHfcSeZnyU\/QvGQ504fCyuER9FfdTCTvpjawbqkGyQOiyj+WmKfCiMYxWKcSEHSkKwmvUAYFLt\/KQhHGqTVrkw1QMEI\/xwj97eOxPEkNpoxBEIrJi6TmsKZZUiU\/Zl6gjJEgvGVr42WfV94u8CrUFgVTyaay+jatW\/C1WGSWCkYjtF6nL2oIrxbbJoqSTlWTaZi2Wz5dgdGwK8q+0yiyXL8RDIdiX45xpbtIrmziWVIWD6CUy\/yFczvOz+04jqDDfuX4\/e2Nkl\/scXFJE2lFyvqLCmw1owxbCSGjDvdoleLWCIxD5ZqSxFJpiSLpUtzlKKoiZ5mJLMK9tLWD3len3\/vq6Eb\/54OC5JHwBjj9CpFTjifKIkZ0ES4iaqYOpRbOteERLUoESwPhXqsv6JOEvJA+o8wbREdgxpFW5gXxpU2mqVNRJhE9kw\/ydT3RxCUiZp56j7QLAZFbcmGw4MJdUH6CBDEXr27JbTarr6dah2hfndUvG1zXOntfHYxhonrmRHKbwboLDcMRkCT0eUgupCo4VR+3dO2iMcQHoHLmw\/tMWg3+NQzmWqtxq\/8COiP1wLSojzTr58+4PoYx3h61AKhl6s2u3AP56j6gXkVxHrxa7usm2FMYBTaNY9cfSR77GLqx+MUiy6biJ+ei4ueEssi1RWUbgnX76XvwFxJO\/Z0dmmnVwS2iL1BQs0xBXeaGx0+rPX9bP51TBQlHPSk5maVExM1iDWAWpcSLzxgIxB14U28iqoSsUe6OzMYRjceB57SNw9dKFNoSHN2W7MhNR7\/kHqERTXXgLust2VYw2glUPFErnojoKtY7Yf1nJqKooU+fkb3yxj9JPp7xdRHU4iX2Vu4Ss9vPFCeKVWFwW4GE04Z6ukSNtrWpcpUR1Cc9hITE7zX7UtH+PnCoalJ+EuOekSe1ADfoC9dioV+YyTMJfwJDvvh4ndMjNHX1ff2p0n2bKydmuz8fvlLXawwDdYfaEyEkdVXbeOSJb7EnvArVETVTdjcjTXOWGaxnqbF+Bshiy2CGJ7zibwDIZYxOcv9sR6j1q8K8i3UWWtpHwr3TPrcYfq7bQn\/VIfJJeTUfjXSw\/zOjG0+kaT5lMHlKYfKkwuRPvBprfypWLAHOdLEhRENv4vrhK\/1EJONJ3e8UJV09751\/TF4WMXC409tCmES5w0UCkx2BAtz16ojM8OokVyc1sV13Ph5tj\/xn401CnAv1NmPtRHq8y2OKwhQFCR7BaYXbHHZa6JSje9oUnN03mxET+767w2FXQAzp3WLYx\/NG2C5+UXjzRTVD6qrEvdfUW32FvN+SpnmbLdotLNpFStlp7zZbuJJTat7nfL1WywuSJwd+x2eTH\/JnPuT8F4Di54yxlPTJz69MoYB405VRDRYtFRiYNfBoqtCaWCxyF+rJ9hwY1Tk7slIonHNGdU81i\/bOgbPx8+IucaTEL9WDZj121E35n8ekb2qX9TwmvFPlTxGZ9pLxg2NanDC6CyGHQBQI30FX3VsppeNUJm01FYcoGNMDOwqyXcqSbXrGSEB7D6xfNoRICk5cc6POMoYWY3Z0ls4yTlt0KTljvZiVnoBFU0JkcWnHpUcXacCMrKUqsKXzIa0Mf9gRoZeM9RHG+siOvHT2j3ysp1TzaO8xWYYpI8gH845BJwPqgPfvhKKMctxmsRjlAczJmGhjHIDfs+6r8ogwBbKcKVCa9Xu0r2R8Wa\/hRWp9L90KQvTwpeQBvgvtSPcXlIsc2yHwMZelcpH4W8S95JTKAaCzxGHUB79oPhUwveofYEbw+OkHI3TtwAuiRmxD8+2aY0WPONEDcicfRkRAVAb6KR9\/4lgGigBeoPOp8ymJ6u0gaAi1kcedL9O\/SiDbd+45fQ0WHy6UMSxalBbiqYyurAKRWRVMIuHk48oSE2FyFnI6zx4ymDrOcoGSEVAl2EYIDxoP4eRz6oi5iFba\/J34jXP1LMfJVcrKtdUG5GGhZGsO5ECFY3\/pXJIiMFk15BtigYSWIh0Azn0sjFPOv8HHKOCHTc7tq2pJqJRVVF1n+BJzdJerCrt2pzkeLOawqXbuAGkP42Vht1UQI16+wYLRyKO1VDO3wy2He+XsVCxCgZK1EgsFrvlbih2O5vm0G8+5lQPVa8vJrhzKLNdri5RRbLNJHca9ZP\/\/IU4u0MUfRHGk9LosiKwRR64rRifaNKUdDK9ELRAO\/oAdIPZnh\/8WGMt\/ilBONY7yj+ycdFkepa2CXXLIq1zyp3v+qTsO5r7wVkx113tXDK+KruAyaFg4g++i9ZKSzV0gQZFeM2xv0Nh6KVFWvOErp9eM0JVIDM\/yG8I5S03i9AdOlVEAng9L8L2JQ88C52Ofp0lA7iQMImbxQ4VgZA6tzLDBV5qLbTRK2OU4TdWRQuWy46RM+TLnoHP5o2MyL9Nl37vs7F2xzp5D83GyPK1WRQD+mEZB7ZhrbKTjMWhtIF1CmVhqU5\/u\/tpwwV+2skxkTXiXoJOoKEOLg+HRywyvblhZriY1LZO+f+S4sxonasK\/RkXcGrdPo1EUTENU42TAswaQnuQkDKkVWb5Na8FQBA2F1oii4yNBY2qAtSQxBdRSApFssppKU4gkFMfX8O3o64Go9nxbfJOWN\/XWih5\/oCW+x8tb6vL0NkpbXw8cd3a8D3h0w57f\/Pzwcdm5YhXomy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:where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-n1utmwsd.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 Actuarial and Quantitative Risk<\/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\">Uncertainty quantification and model validation for actuarial and quantitative risk<\/h1><p class=\"nx-scene-lede\">Uncertainty quantification identifies and propagates uncertainty in data, parameters, model structure and assumptions. On this platform it shows how wide plausible outcomes are for premiums, reserves and capital, and which assumptions matter most.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/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\/actuarial-quantitative-risk\/stem\/\">Back to STEM foundation of Actuarial and Quantitative Risk<\/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\">Register<\/span><\/div><h2 id=\"nx-s-1\" class=\"nx-section-title\">Uncertainty quantification and model validation for actuarial and quantitative risk on the record.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-es-fig\" data-es-layout=\"console\"><div class=\"nx-hero-panel\"><div class=\"nx-register\"><div class=\"nx-register-head\"><span class=\"nx-eyebrow\"><strong>Method record<\/strong><\/span><span class=\"nx-eyebrow\">STEM method<\/span><\/div><ol class=\"nx-register-rows\"><li><div><span class=\"nx-register-k\">Method<\/span><span class=\"nx-register-v\">Uncertainty quantification and model validation<\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Steward<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/systems-modelling\/\">Systems Science and Modelling<\/a><\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Applied in<\/span><span class=\"nx-register-v\">Actuarial and Quantitative Risk<\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Data<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"#nx-sec-data\">3 inputs<\/a><\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Checks<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"#nx-sec-checks\">3 validation steps<\/a><\/span><\/div><\/li><li><div><span class=\"nx-register-k\">Canonical page<\/span><span class=\"nx-register-v\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/uncertainty-quantification\/\">STEM Commons<\/a><\/span><\/div><\/li><\/ol><\/div><\/div><\/div><\/div><\/div><\/section><section class=\"nx-section\" data-band=\"auto\" data-band-id=\"nx-band-1\" data-hue=\"project\" aria-label=\"Application, Loss model, Data\"><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=\"Application, Loss model, Data\"><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-1-tab-1\" aria-controls=\"nx-sec-application\" aria-selected=\"true\" data-key=\"p1\">Application<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-1-tab-2\" aria-controls=\"nx-sec-instrument\" aria-selected=\"false\" data-key=\"p2\">Loss model<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-1-tab-3\" aria-controls=\"nx-sec-data\" aria-selected=\"false\" data-key=\"p3\">Data<\/button><\/div><section id=\"nx-sec-application\" class=\"nx-section nx-band-panel\" data-hue=\"project\" data-variant=\"split\" aria-labelledby=\"nx-s-2\" 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\">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 uncertainty quantification and model validation does here.<\/h2><p class=\"nx-lede\">Uncertainty quantification and model validation characterise how uncertainty in inputs, parameters and model structure affects predictions. They establish how much confidence a model&#8217;s outputs deserve. In insurance and catastrophe, methods and modelling and capital markets and investment, uncertainty quantification shows where risk estimates are robust and where they are fragile. It guides where better data or models would change decisions. The canonical description is on <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/uncertainty-quantification\/\">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=\"M3 3v18h18\"\/><path d=\"M18 17V9\"\/><path d=\"M13 17V5\"\/><path d=\"M8 17v-3\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Reserve ranges<\/h3><p class=\"nx-p nx-mt-1\">Bootstrap and Bayesian methods produce distributions of reserve outcomes around the central estimate, showing the range that could emerge.<\/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=\"m12.83 2.18a2 2 0 0 0-1.66 0L2.6 6.08a1 1 0 0 0 0 1.83l8.58 3.91a2 2 0 0 0 1.66 0l8.58-3.9a1 1 0 0 0 0-1.83Z\"\/><path d=\"m22 17.65-9.17 4.16a2 2 0 0 1-1.66 0L2 17.65\"\/><path d=\"m22 12.65-9.17 4.16a2 2 0 0 1-1.66 0L2 12.65\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Parameter and model uncertainty<\/h3><p class=\"nx-p nx-mt-1\"><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> is separated from the uncertainty of choosing one model over another, and both are carried into capital estimates.<\/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=\"12\" r=\"10\"\/><circle cx=\"12\" cy=\"12\" r=\"6\"\/><circle cx=\"12\" cy=\"12\" r=\"2\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Sensitivity ranking<\/h3><p class=\"nx-p nx-mt-1\">Inputs are varied to rank which assumptions drive results most, guiding where better data or review would add most value.<\/p><\/article><\/div><\/div><\/div><\/section><section id=\"nx-sec-instrument\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" data-ns-layout=\"es-wide\" aria-labelledby=\"nx-s-3\" 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\">03<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Loss model<\/span><\/div><h2 id=\"nx-s-3\" 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.<\/p><\/div><div class=\"nx-section-body\"><figure class=\"nx-in nx-fk\" id=\"nx-in-ive-risk-stem-uncertainty-quantification-0\" data-nxi=\"hevl\" data-nxi-id=\"hevl\" data-nxi-z=\"lZVNb+M2EIb\/ymCABbwAa4iynWx1a9ECLdBDsV30EvhAi+OIKEUK5MixN\/B\/L0h9RE422PYki5RnHr4vZ+YZ2bAlrPAPHyPQuSbSytUkQJ0oqEcC5VyvLNi0r5yGLviDOliCVp1N27d5BwU23lHkC1b4hVTdGPcIrddk4clwA5EVkwZjbR85KDYngpOyPUUBzjMo6AJpU7Pxbo0COxVUG7F6eEajscK2R4FWHcimBJfO1MpOaI36qoIG45hcNHyBlfW1SpE+osDeGcYKX3aN03RGga1xWBXrUmCrzlhtBEamLi0VpcAMh5UUaH1auxfYGKzkenMVI9OBWC2o\/uoCKQ3+CHTmQC1FWMVaWfpPFNsRo1xvFyDybiYp1p8mlruRZSbh5hbl9zmNYnhqTN1Ao+wRuBlVBxOTb\/xdMjnrI8sF16cZaztAlYkuQe3WdzPW11uqX0zsKETjXRLp1FtHQR2MNXx54XiRRO6mzOtlarlQZLebJNn8OKQv1velQI1VOVPERgVa+pTeM8GghMuyjJfo6D13wbiFMh9mLSaeoph4XmC2xYCyKUYdioxRzBhd8Ew1L0D+HFaSHpGV0yn\/KhD3wUFHwXi9uDkXUiG+Jtl9i2Q3kpQjScbNKHuBR3MmjdUzmv6bpkdK2\/dbuSmvAn3PXc+LMlTKLg7w09sWsZBtlhhfGdK1VhbFIk7uPYpBJjNkUcB02HdCyWWocvduqHL3P0Kdb5luSug9sLf65YPuBdaNCpx0PmOFn5eewioHEWD9I8z94TKxrz4MHaTzkfRAmvbfadNQ9+FEeE2O+y6mhM5rSo7hb6\/aIor8b0r8L1cvvVCoyZxIQ1RHyh\/+mvL3uWz+vqnTvUBr3D8pwUMhpJB78bAdn7vxKUWZn6WQ4ofhxyYvbMRWyP1+hp1u1c\/ylYMU05hpO4ZuBhXT9Jlqpu45N7H09SqPGku6ysXcva2sj2mqTNpIUe5n4z\/fZKcTEdDxSDVXyzjGnQxThENvrE7D7UCNcRoMC4h+MKwPlCETgqMzD0PzMfgnWGmKdTAH0sO8q33b9Uy3WEmnpNB1fxXoiL+Mpg9OijmJuG2eOWcuvuu\/\" 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-ive-risk-stem-uncertainty-quantification-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\/actuarial-quantitative-risk\/foundry\/\">Actuarial and Quantitative Risk 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\/actuarial-quantitative-risk\/universe\/\">Actuarial and Quantitative Risk 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\/actuarial-quantitative-risk\/reports\/\">Actuarial and Quantitative Risk 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-data\" class=\"nx-section nx-band-panel\" data-hue=\"project\" data-variant=\"split\" aria-labelledby=\"nx-s-4\" 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\">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 uncertainty quantification and model validation 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\/actuarial-quantitative-risk\/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\">Model inputs and parameters<\/td><td data-label=\"Role\">Input uncertainty<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Alternative models<\/td><td data-label=\"Role\">Structural uncertainty<\/td><td data-label=\"Label\"><span class=\"nx-chip\">MODELLED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Historical outcomes<\/td><td data-label=\"Role\">Calibration of ranges<\/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\/A-P11\/stem\/uncertainty-quantification\/#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\/A-P11\/stem\/uncertainty-quantification\/#evidence\" data-nxf-title=\"Evidence\" data-nxf-height=\"520\" hidden><\/div><\/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=\"Exposure, loss and capital, Validation, 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=\"Exposure, loss and capital, Validation, Connections\"><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-2-tab-1\" aria-controls=\"nx-sec-audience\" aria-selected=\"true\" data-key=\"p1\">Exposure, loss and capital<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-2-tab-2\" aria-controls=\"nx-sec-checks\" aria-selected=\"false\" data-key=\"p2\">Validation<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-2-tab-3\" aria-controls=\"nx-sec-connections\" aria-selected=\"false\" data-key=\"p3\">Connections<\/button><\/div><section id=\"nx-sec-audience\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" aria-labelledby=\"nx-s-5\" 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\">05<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Exposure, loss and capital<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">What it 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 uncertainty quantification and model validation for actuarial and quantitative risk 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-sec-checks\" class=\"nx-section nx-band-panel\" data-hue=\"project\" data-variant=\"inset\" aria-labelledby=\"nx-s-6\" 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\">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\">Predictive distributions are tested for calibration and sharpness against held-out observations. Verification confirms that code solves the intended equations.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"ledger\"><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\">Range calibration<\/td><td data-label=\"How it is done\">Stated ranges are compared with outcomes where possible<\/td><\/tr><tr><td data-label=\"Check\">Dependence of inputs<\/td><td data-label=\"How it is done\">Correlations between uncertain inputs are documented<\/td><\/tr><tr><td data-label=\"Check\">Clear communication<\/td><td data-label=\"How it is done\">Uncertainty is presented with each result<\/td><\/tr><\/tbody><\/table><\/div><p class=\"nx-note nx-mt-3\">Structural uncertainty and unknown unknowns are difficult to represent. Computational cost limits exploration of high-dimensional models.<\/p><\/div><\/div><\/section><section id=\"nx-sec-connections\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" data-variant=\"split\" aria-labelledby=\"nx-s-7\" role=\"tabpanel\" data-band-tab=\"nx-band-2-tab-3\"><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\">Connections<\/span><\/div><h2 id=\"nx-s-7\" 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\/actuarial-quantitative-risk\/topics\/extreme-value-methods\/\"><span>Extreme value methods<\/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\/actuarial-quantitative-risk\/topics\/climate-adjusted-pricing\/\"><span>Climate-adjusted pricing questions<\/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\/actuarial-quantitative-risk\/topics\/credibility-and-sparse-data\/\"><span>Credibility and sparse data<\/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\/actuarial-quantitative-risk\/topics\/dependence-and-correlation\/\"><span>Dependence and correlation<\/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\/actuarial-quantitative-risk\/topics\/capital-and-solvency-models\/\"><span>Capital and solvency models<\/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\/actuarial-quantitative-risk\/topics\/model-validation-quant\/\"><span>Quantitative model validation<\/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\/actuarial-quantitative-risk\/topics\/mortality-and-longevity\/\"><span>Mortality and longevity<\/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 class=\"nx-section\" data-band=\"auto\" data-band-id=\"nx-band-3\" data-hue=\"project\" aria-label=\"Stewardship, 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, 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-s-9-sec\" aria-selected=\"false\" data-key=\"p2\">Connected<\/button><button class=\"nx-tab\" type=\"button\" role=\"tab\" id=\"nx-band-3-tab-3\" aria-controls=\"nx-s-10-sec\" aria-selected=\"false\" data-key=\"p3\">Questions<\/button><\/div><section id=\"nx-sec-stewardship\" class=\"nx-section nx-band-panel\" data-hue=\"project\" data-variant=\"split\" aria-labelledby=\"nx-s-8\" 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\">08<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Stewardship<\/span><\/div><h2 id=\"nx-s-8\" class=\"nx-section-title\">Who maintains the method.<\/h2><p class=\"nx-lede\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/systems-modelling\/\">Systems Science and Modelling<\/a> stewards uncertainty quantification and model validation 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\/uncertainty-quantification\/\">Canonical page: Uncertainty quantification and model validation<\/a><\/p><\/div><\/div><\/section><section id=\"nx-s-9-sec\" class=\"nx-section nx-band-panel\" data-hue=\"finance\" aria-labelledby=\"nx-s-9\" 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\">09<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connected<\/span><\/div><h2 id=\"nx-s-9\" 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:\/\/therisk.global\/systems-modelling\/stem\/uncertainty-quantification\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Steward platform<\/span><span class=\"nx-dest-host\"><abbr title=\"The Global Centre for Risk and Innovation\">GCRI<\/abbr><\/span><\/span><span class=\"nx-dest-title\">Uncertainty quantification<\/span><span class=\"nx-dest-why\">Systems Science and Modelling 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-10-sec\" class=\"nx-section nx-band-panel\" data-hue=\"project\" aria-labelledby=\"nx-s-10\" 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\">10<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Questions<\/span><\/div><h2 id=\"nx-s-10\" 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 uncertainty quantification and model validation in this field?<\/summary><div class=\"nx-answer\">Structural uncertainty and unknown unknowns are difficult to represent. Computational cost limits exploration of high-dimensional models.<\/div><\/details><details><summary>How are results checked?<\/summary><div class=\"nx-answer\">Predictive distributions are tested for calibration and sharpness against held-out observations. Verification confirms that code solves the intended equations.<\/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\/uncertainty-quantification\/\">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=\"finance\" 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\">Actuarial and Quantitative Risk<\/span><span class=\"nx-chip\">Methods and Technical<\/span><span class=\"nx-chip\">Finance and Insurance Readiness<\/span><\/div><\/div><div class=\"nx-section-body\"><ul class=\"nx-dest\"><li><a class=\"nx-dest-card\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/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. 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