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\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<\/script><script 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:where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-nl30p11e.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-hero\" data-layout=\"dag\" aria-labelledby=\"nx-title-1\"><div class=\"nx-container\"><div class=\"nx-theme-toggle\" role=\"group\" aria-label=\"Appearance of this page\" hidden><button type=\"button\" data-mode=\"light\" aria-pressed=\"false\">Light<\/button><button type=\"button\" data-mode=\"dark\" aria-pressed=\"false\">Dark<\/button><button type=\"button\" data-mode=\"auto\" aria-pressed=\"true\">Auto<\/button><\/div><div class=\"nx-folio\" aria-label=\"Document reference\"><strong>A-P11 \u00b7 TOPIC \u00b7 extreme-value-methods<\/strong>GRA dossier \u00b7 service level R0<\/div><div class=\"nx-hero-grid\"><div class=\"nx-hero-text\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">GRA<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">Actuarial and Quantitative Risk<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">In development<\/span><\/div><div class=\"nx-accent-row\"><span class=\"nx-accent-rule\" aria-hidden=\"true\"><\/span><span>Topic dossier \u00b7 Actuarial and Quantitative Risk<\/span><\/div><h1 id=\"nx-title-1\" class=\"nx-hero-title\">Extreme value methods<\/h1><p class=\"nx-tagline\">Extreme value theory gives a statistical basis for estimating the size and frequency of losses and hazards beyond what the record shows. It is used for large-loss pricing, catastrophe hazard, operational risk and capital at high confidence levels. Results depend on thresholds, sample size and whether conditions are stable. This page sets out how extreme value methods work, where they fail, the data they need and who relies on them.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/contact\/\">Make an enquiry <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/\">Back to Topics<\/a><\/div><\/div><div class=\"nx-hero-canvas\"><div class=\"nx-plate\" data-wide=\"true\"><div class=\"nx-plate-label\"><span class=\"nx-eyebrow\"><strong>Systems<\/strong> \u00b7 around this subject<\/span><span class=\"nx-eyebrow\">Design, not a record<\/span><\/div><div class=\"nx-figure-frame\" data-scene=\"true\"><figure class=\"nx-fk nx-cx\" data-fk=\"cx-actuarial-reserving\" data-cx=\"actuarial.reserving\" data-variant=\"section\" aria-label=\"Reserving workbench: illustrative console with placeholder names and values\" data-nx-z=\"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\"><figcaption class=\"nx-fk-cap\"><span>Reserving workbench: the screen's structure, terms and states for Extreme value methods.<\/span><span data-honesty=\"true\">Placeholder names and illustrative values; a design, not a record or a live system.<\/span><\/figcaption><\/figure><\/div><\/div><p class=\"nx-caption\"><strong>Fig.<\/strong><span>Reserving workbench. Design wireframe with placeholder names and illustrative values, not a record or a live system.<\/span><\/p><\/div><\/div><\/div><\/section> <nav class=\"nx-index-strip\" data-nx-spy=\"true\" aria-label=\"On this page\"><div class=\"nx-container\"><a href=\"#nx-sec-brief-answer\"><span>01<\/span>The short answer<\/a><a href=\"#nx-sec-field\"><span>02<\/span>Exposure, hazard and the exceedance\u2026<\/a><a href=\"#nx-sec-question\"><span>03<\/span>Extreme value methods: how the risk\u2026<\/a><a href=\"#nx-sec-terms\"><span>04<\/span>Terms used on this page<\/a><a href=\"#nx-sec-indicators\"><span>05<\/span>What is measured, and how well<\/a><a href=\"#nx-sec-pipeline\"><span>06<\/span>From sources to decisions<\/a><a href=\"#nx-sec-sources\"><span>07<\/span>Where the evidence comes from<\/a><a href=\"#nx-sec-decisions\"><span>08<\/span>Who decides, and what the evidence\u2026<\/a><\/div><\/nav> <section id=\"nx-sec-brief-answer\" class=\"nx-section\" data-hue=\"finance\" data-variant=\"inset\" aria-labelledby=\"nx-s-2\"><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\">In brief<\/span><\/div><h2 id=\"nx-s-2\" class=\"nx-section-title\">The short answer.<\/h2><p class=\"nx-lede\">Extreme value methods are statistical techniques for estimating the size and frequency of the largest losses and hazards, beyond the observed record. They rest on limiting distributions, threshold choices and stable conditions. Actuaries, capital model owners, validators and supervisors decide how they are used.<\/p><\/div><\/div><\/section><section id=\"nx-sec-field\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-3\"><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\">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.\" 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\"><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\/A-P11\/topics\/extreme-value-methods\/#map\" data-nxf-title=\"Actuarial and Quantitative Risk live map\" data-nxf-height=\"560\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-question\" class=\"nx-section\" data-hue=\"finance\" data-variant=\"stack\" aria-labelledby=\"nx-s-4\"><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\">The question<\/span><\/div><h2 id=\"nx-s-4\" class=\"nx-section-title\">Extreme value methods: how the risk develops.<\/h2><p class=\"nx-lede\">Tail estimation for hazards, markets and operational losses. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-cells\" data-cols=\"2\"><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><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><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Tails follow limiting laws<\/h3><p class=\"nx-p nx-mt-1\">Under broad conditions, block maxima follow a generalised extreme value distribution and exceedances over a high threshold follow a generalised Pareto distribution.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><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><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Thresholds trade bias for variance<\/h3><p class=\"nx-p nx-mt-1\">A high threshold respects the theory but leaves few points; a low threshold adds data but bias. Results shift with the choice.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><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=\"M5 22h14\"\/><path d=\"M5 2h14\"\/><path d=\"M17 22v-4.172a2 2 0 0 0-.586-1.414L12 12l-4.414 4.414A2 2 0 0 0 7 17.828V22\"\/><path d=\"M7 2v4.172a2 2 0 0 0 .586 1.414L12 12l4.414-4.414A2 2 0 0 0 17 6.172V2\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Short records widen intervals<\/h3><p class=\"nx-p nx-mt-1\">Estimating a rare return period from a short record gives wide confidence intervals that are often left unreported.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><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 2v2\"\/><path d=\"m4.93 4.93 1.41 1.41\"\/><path d=\"M20 12h2\"\/><path d=\"m19.07 4.93-1.41 1.41\"\/><path d=\"M15.947 12.65a4 4 0 0 0-5.925-4.128\"\/><path d=\"M3 20a5 5 0 1 1 8.9-4H13a3 3 0 0 1 2 5.24\"\/><path d=\"M11 20v2\"\/><path d=\"M7 19v2\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Non-stationarity breaks assumptions<\/h3><p class=\"nx-p nx-mt-1\">If climate, exposure or claims practice change, past extremes may not represent future ones, and stationary fits mislead.<\/p><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-terms\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"split\" aria-labelledby=\"nx-s-5\"><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\">Key terms<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">Terms used on this page.<\/h2><p class=\"nx-lede\">Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Term<\/th><th scope=\"col\">Definition<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Term\">Block maxima<\/td><td data-label=\"Definition\">The largest value in each fixed period, such as a year, used to fit a generalised extreme value distribution.<\/td><\/tr><tr><td data-label=\"Term\">Generalised Pareto distribution<\/td><td data-label=\"Definition\">The distribution used to model exceedances over a high threshold.<\/td><\/tr><tr><td data-label=\"Term\">Shape parameter<\/td><td data-label=\"Definition\">The parameter that determines whether a distribution's tail is heavy, light or bounded.<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/section><section id=\"nx-sec-indicators\" class=\"nx-section\" data-hue=\"finance\" data-variant=\"inset\" aria-labelledby=\"nx-s-6\"><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\">Indicators<\/span><\/div><h2 id=\"nx-s-6\" class=\"nx-section-title\">What is measured, and how well.<\/h2><p class=\"nx-lede\">Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Indicator<\/th><th scope=\"col\">Unit<\/th><th scope=\"col\">Cadence<\/th><th scope=\"col\">Source type<\/th><th scope=\"col\">Label<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Indicator\">Shape parameter of the tail<\/td><td data-label=\"Unit\">dimensionless<\/td><td data-label=\"Cadence\">per analysis<\/td><td data-label=\"Source type\">Fitted extreme value models<\/td><td data-label=\"Label\"><span class=\"nx-chip\">MODELLED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Return level at stated return period<\/td><td data-label=\"Unit\">currency or hazard unit<\/td><td data-label=\"Cadence\">per analysis<\/td><td data-label=\"Source type\">Fitted models<\/td><td data-label=\"Label\"><span class=\"nx-chip\">MODELLED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Confidence interval width for return level<\/td><td data-label=\"Unit\">currency or hazard unit<\/td><td data-label=\"Cadence\">per analysis<\/td><td data-label=\"Source type\">Bootstrap or profile likelihood<\/td><td data-label=\"Label\"><span class=\"nx-chip\">MODELLED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Exceedances above threshold<\/td><td data-label=\"Unit\">count<\/td><td data-label=\"Cadence\">per analysis<\/td><td data-label=\"Source type\">Data<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Record length<\/td><td data-label=\"Unit\">years<\/td><td data-label=\"Cadence\">per dataset<\/td><td data-label=\"Source type\">Data<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Trend in annual maxima<\/td><td data-label=\"Unit\">change per decade<\/td><td data-label=\"Cadence\">per analysis<\/td><td data-label=\"Source type\">Non-stationary fits<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"indicators\" data-nxf-kind=\"indicator\" data-nxf-resource=\"nexus:\/\/gra\/A-P11\/topics\/extreme-value-methods\/#indicators\" data-nxf-title=\"Indicators\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-pipeline\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-7\"><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\">Canvas<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">From sources to decisions.<\/h2><p class=\"nx-lede\">The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-plate\" data-wide=\"true\"><div class=\"nx-plate-label\"><span class=\"nx-eyebrow\"><strong>Pipeline canvas<\/strong> \u00b7 Extreme value methods as a run graph<\/span><span class=\"nx-eyebrow\">Design, not a record<\/span><\/div><div class=\"nx-figure-frame\" data-scene=\"true\"><svg class=\"nx-figure nx-ui nx-dag\" viewBox=\"0 0 1200 564\" data-nx-dag=\"true\" data-w=\"1200\" data-h=\"564\" role=\"img\" aria-labelledby=\"nx-dag-a-p11-topics-extreme-value-methods-catmod-t nx-dag-a-p11-topics-extreme-value-methods-catmod-d\" 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\"><title id=\"nx-dag-a-p11-topics-extreme-value-methods-catmod-t\">Extreme value methods: catastrophe model pipeline<\/title><desc id=\"nx-dag-a-p11-topics-extreme-value-methods-catmod-d\">A stochastic event catalogue drives the hazard module; exposure and vulnerability turn intensity into damage; the financial module applies policy terms; event and year loss tables give exceedance curves, PML and TVaR. Design of the process, not a record of a run.<\/desc><\/svg><\/div><\/div><p class=\"nx-caption\"><strong>Fig. 02<\/strong><span>Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.<\/span><\/p><\/div><\/div><\/section><section id=\"nx-sec-sources\" class=\"nx-section\" data-hue=\"finance\" data-variant=\"split\" aria-labelledby=\"nx-s-8\"><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\">Sources<\/span><\/div><h2 id=\"nx-s-8\" class=\"nx-section-title\">Where the evidence comes from.<\/h2><p class=\"nx-lede\">Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Source<\/th><th scope=\"col\">What it gives<\/th><th scope=\"col\">Licence class<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Source\">Coles, An Introduction to Statistical Modeling of Extreme Values<\/td><td data-label=\"What it gives\">Standard reference text on extreme value methods<\/td><td data-label=\"Licence class\">Proprietary, published book<\/td><\/tr><tr><td data-label=\"Source\">R extRemes and evd packages<\/td><td data-label=\"What it gives\">Open-source extreme value tools<\/td><td data-label=\"Licence class\">Open source, GPL<\/td><\/tr><tr><td data-label=\"Source\">NOAA and national meteorological hazard records<\/td><td data-label=\"What it gives\">Long records of hazard extremes<\/td><td data-label=\"Licence class\">Public domain or terms per service<\/td><\/tr><tr><td data-label=\"Source\">Operational risk loss data consortia publications<\/td><td data-label=\"What it gives\">Large operational loss records<\/td><td data-label=\"Licence class\">Members only; summaries free to read<\/td><\/tr><tr><td data-label=\"Source\">Large-loss databases of insurers and reinsurers<\/td><td data-label=\"What it gives\">Individual large claims<\/td><td data-label=\"Licence class\">Proprietary, internal<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"evidence\" data-nxf-kind=\"evidence\" data-nxf-resource=\"nexus:\/\/gra\/A-P11\/topics\/extreme-value-methods\/#evidence\" data-nxf-title=\"Sources\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-decisions\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"inset\" aria-labelledby=\"nx-s-9\"><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\">Decisions<\/span><\/div><h2 id=\"nx-s-9\" class=\"nx-section-title\">Who decides, and what the evidence serves.<\/h2><p class=\"nx-lede\">Actuarial and Quantitative Risk prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.<\/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\">Who decides<\/th><th scope=\"col\">The question<\/th><th scope=\"col\">Evidence this page serves<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Who decides\">Actuaries and risk modellers<\/td><td data-label=\"The question\">Which extreme value method and threshold to use<\/td><td data-label=\"Evidence this page serves\">Stability diagnostics and data review<\/td><\/tr><tr><td data-label=\"Who decides\">Capital model owners<\/td><td data-label=\"The question\">How tail estimates enter capital<\/td><td data-label=\"Evidence this page serves\">Uncertainty and sensitivity evidence<\/td><\/tr><tr><td data-label=\"Who decides\">Validators<\/td><td data-label=\"The question\">Whether tail fits are adequate<\/td><td data-label=\"Evidence this page serves\">Back-testing and alternative fits<\/td><\/tr><tr><td data-label=\"Who decides\">Supervisors<\/td><td data-label=\"The question\">Whether tail calibration supports regulatory capital<\/td><td data-label=\"Evidence this page serves\">Model documentation and validation<\/td><\/tr><\/tbody><\/table><\/div><h3 class=\"nx-h4 nx-mt-4\">Outputs this topic produces<\/h3><ul class=\"nx-list\"><li>Guide to block maxima and peaks-over-threshold methods<\/li><li>Method note on threshold selection and stability plots<\/li><li>Brief on reporting uncertainty for return levels<\/li><li>Questions for reviewers on non-stationarity<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-core-questions\" class=\"nx-section\" data-hue=\"finance\" data-variant=\"stack\" aria-labelledby=\"nx-s-10\"><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\">Core questions<\/span><\/div><h2 id=\"nx-s-10\" class=\"nx-section-title\">The questions experts ask.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-list\"><li>How large is the loss that is exceeded, on average, once in a given number of years?<\/li><li>Which threshold or block size gives a reliable estimate of the tail?<\/li><li>Is the tail heavy, light or bounded, and how confident can we be?<\/li><li>Is the extreme behaviour changing over time?<\/li><li>How do extremes in different variables or places occur together?<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-mechanisms\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"split\" aria-labelledby=\"nx-s-11\"><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\">Mechanisms<\/span><\/div><h2 id=\"nx-s-11\" class=\"nx-section-title\">The theory of extremes.<\/h2><\/div><div class=\"nx-section-body\"><p class=\"nx-p\">Two classical results underpin the field. The distribution of block maxima, such as the largest annual flood, tends towards the generalized extreme value distribution under broad conditions. The distribution of exceedances over a high threshold tends towards the generalized Pareto distribution. A single shape parameter determines whether the tail is heavy, light or bounded, and it is the most important and most uncertain quantity in any extreme value analysis.<\/p><p class=\"nx-p\">Practical choices drive results: the block size or threshold, how to handle clusters of extremes that occur together, such as aftershocks or volatile trading days, and whether to include covariates for trends or seasonality. Non-stationary extreme value models let parameters change with time or with climate indices, which is essential when the underlying process is changing.<\/p><p class=\"nx-p\">Multivariate and spatial extremes address joint behaviour, such as storm surge with rainfall, or losses across regions. Dependence in the extremes can differ from dependence in the bulk of the data, which has direct consequences for diversification.<\/p><\/div><\/div><\/section><section id=\"nx-sec-methods-and-data\" class=\"nx-section\" data-hue=\"finance\" data-variant=\"inset\" aria-labelledby=\"nx-s-12\"><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\">Methods and data<\/span><\/div><h2 id=\"nx-s-12\" class=\"nx-section-title\">Methods and their limits.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-cells\" data-cols=\"3\"><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><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=\"m19 9-5 5-4-4-3 3\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Peaks<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Peaks over threshold<\/h3><p class=\"nx-p nx-mt-1\">Fitting the generalized Pareto distribution to exceedances; results depend on threshold choice.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><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><span class=\"nx-eyebrow\">Maxima<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Block maxima<\/h3><p class=\"nx-p nx-mt-1\">Fitting the generalized extreme value distribution to block maxima; uses data less efficiently.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><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=\"M5 22h14\"\/><path d=\"M5 2h14\"\/><path d=\"M17 22v-4.172a2 2 0 0 0-.586-1.414L12 12l-4.414 4.414A2 2 0 0 0 7 17.828V22\"\/><path d=\"M7 2v4.172a2 2 0 0 0 .586 1.414L12 12l4.414-4.414A2 2 0 0 0 17 6.172V2\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Non-stationary<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Covariate models<\/h3><p class=\"nx-p nx-mt-1\">Letting parameters vary with time or climate; risks over-fitting short records.<\/p><\/div><\/div><p class=\"nx-note nx-mt-3\">Limits: estimates far beyond the data rest on the assumption that the limiting distribution applies, and confidence intervals for long return periods are wide. Reporting return levels without intervals overstates certainty.<\/p><\/div><\/div><\/section><section id=\"nx-sec-methods\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-13\"><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\">Methods<\/span><\/div><h2 id=\"nx-s-13\" class=\"nx-section-title\">How it is studied, and what the methods cannot say.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-methods\" data-limits=\"true\"><ol class=\"nx-mledger\" aria-label=\"Methods\"><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/stem\/extreme-value\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">01<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Extreme value theory and tail risk<\/span><span class=\"nx-mledger-role\">Excess layer pricing \u00b7 Tail capital measures \u00b7 Non-stationary hazard extremes<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/extreme-value\/\" aria-label=\"Extreme value theory and tail risk: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/stem\/probability-statistics\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">02<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Probability and statistics<\/span><span class=\"nx-mledger-role\">Generalised linear models in pricing \u00b7 Bayesian credibility \u00b7 Goodness-of-fit testing<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/probability-statistics\/\" aria-label=\"Probability and statistics: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/stem\/uncertainty-quantification\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">03<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Uncertainty quantification and model validation<\/span><span class=\"nx-mledger-role\">Reserve ranges \u00b7 Parameter and model uncertainty \u00b7 Sensitivity ranking<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/uncertainty-quantification\/\" aria-label=\"Uncertainty quantification and model validation: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/stem\/computing\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">04<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">High-performance, cloud and edge computing<\/span><span class=\"nx-mledger-role\">Monte Carlo simulation \u00b7 Version control and reproducibility \u00b7 End-user computing controls<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/technology\/computing\/\" aria-label=\"High-performance, cloud and edge computing: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><\/ol><aside class=\"nx-limits\" aria-label=\"What the methods cannot say\"><span class=\"nx-eyebrow\">What the methods cannot say<\/span><p class=\"nx-limits-text\">Extreme value estimates are uncertain by nature, and that uncertainty grows quickly beyond the record. Theory assumes conditions that may not hold. Large-loss data are often proprietary and few. Actuarial and Quantitative Risk explains methods; it does not estimate any return level or capital requirement.<\/p><\/aside><\/div><\/div><\/div><\/section><section id=\"nx-s-14-sec\" class=\"nx-section\" data-hue=\"finance\" aria-labelledby=\"nx-s-14\"><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-14\" class=\"nx-section-title\">Questions and answers.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-faq\"><details><summary>What is the peaks-over-threshold method?<\/summary><div class=\"nx-answer\">An extreme value approach that models losses above a high threshold with a generalised Pareto distribution, using more data than annual maxima.<\/div><\/details><details><summary>What does the shape parameter tell us?<\/summary><div class=\"nx-answer\">Whether the tail is heavy, light or bounded; a positive shape implies a heavy tail where very large losses are relatively more likely.<\/div><\/details><details><summary>Can extreme value theory predict the next catastrophe?<\/summary><div class=\"nx-answer\">No. It estimates frequencies and sizes of extremes under assumptions, with wide uncertainty, not when an event will happen.<\/div><\/details><details><summary>What is a return period?<\/summary><div class=\"nx-answer\">The average interval between exceedances of a given level under stationary conditions. It is not a schedule: a rare event can happen in consecutive years.<\/div><\/details><details><summary>Can extreme value theory predict a crash?<\/summary><div class=\"nx-answer\">No. It estimates the probability and size of extremes, not their timing.<\/div><\/details><details><summary>What if the climate is changing?<\/summary><div class=\"nx-answer\">Non-stationary models allow parameters to change, but they need care to avoid over-fitting short records.<\/div><\/details><\/div><\/div><\/div><\/section><section class=\"nx-section\" data-tone=\"alt\" aria-labelledby=\"nx-rel-15\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Related<\/span><h2 id=\"nx-rel-15\" class=\"nx-h3 nx-mt-2\">Around Actuarial and Quantitative Risk.<\/h2><nav class=\"nx-related nx-mt-3\" aria-label=\"Related\"><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/loss-distributions\/\">Loss distributions <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/climate-adjusted-pricing\/\">Climate-adjusted pricing questions <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/credibility-and-sparse-data\/\">Credibility and sparse data <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/dependence-and-correlation\/\">Dependence and correlation <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/capital-and-solvency-models\/\">Capital and solvency models <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/model-validation-quant\/\">Quantitative model validation <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/\">Actuarial and Quantitative Risk home <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/counterparts\/\">Counterparts across the Consortium <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/\">The Global Risks Alliance home <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/globalriskalliance.com\/trust\/\">GRA governance and trust <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/nav><\/div><\/section><section class=\"nx-closing\" aria-labelledby=\"nx-close-16\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Actuarial and Quantitative Risk<\/span><h2 id=\"nx-close-16\" class=\"nx-section-title\">Bring your expertise to this field.<\/h2><p class=\"nx-lede\">The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"mailto:contact@globalriskalliance.com?subject=Actuarial%20and%20Quantitative%20Risk%3A%20Extreme%20value%20methods\">Write to the team <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/contact\/\">See all contact routes<\/a><\/div><\/div><\/section><\/div><\/div><script class=\"_iub_cs_skip\">window.__nxMount ? window.__nxMount(document.currentScript) : null;<\/script>\n\n\n<script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"FAQPage\",\"@id\":\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/extreme-value-methods\/#faq\",\"isPartOf\":{\"@id\":\"https:\/\/globalriskalliance.com\/actuarial-quantitative-risk\/topics\/extreme-value-methods\/#webpage\"},\"inLanguage\":\"en\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"What is the peaks-over-threshold method?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"An extreme value approach that models losses above a high threshold with a generalised Pareto distribution, using more data than annual maxima.\"}},{\"@type\":\"Question\",\"name\":\"What does the shape parameter tell us?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Whether the tail is heavy, light or bounded; a positive shape implies a heavy tail where very large losses are relatively more likely.\"}},{\"@type\":\"Question\",\"name\":\"Can extreme value theory predict the next catastrophe?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"No. It estimates frequencies and sizes of extremes under assumptions, with wide uncertainty, not when an event will happen.\"}},{\"@type\":\"Question\",\"name\":\"What is a return period?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"The average interval between exceedances of a given level under stationary conditions. 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