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	<title>Definition:Exceedance probability curve - Revision history</title>
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		<summary type="html">&lt;p&gt;Bot: Creating new article from JSON&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;📈 &amp;#039;&amp;#039;&amp;#039;Exceedance probability curve&amp;#039;&amp;#039;&amp;#039; is a graphical representation that maps every potential [[Definition:Loss | loss]] level in an insurance portfolio to the probability of that level being exceeded in a given time frame. Produced as a core output of [[Definition:Catastrophe model | catastrophe models]], the curve provides [[Definition:Insurance carrier | insurers]], [[Definition:Reinsurance | reinsurers]], and capital providers with a continuous view of risk — from frequent, low-severity events on the left side to rare, catastrophic scenarios on the right. It is the primary analytical tool used to translate raw model simulations into actionable metrics for [[Definition:Underwriting | underwriting]], capital allocation, and [[Definition:Reinsurance | reinsurance]] purchasing decisions.&lt;br /&gt;
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🔧 Constructing the curve involves running a stochastic simulation of thousands of possible event scenarios against a portfolio&amp;#039;s [[Definition:Exposure | exposure]] data. Each simulated year produces a set of losses, which the model ranks and assigns cumulative probabilities. The x-axis of the resulting curve shows loss amounts; the y-axis shows the [[Definition:Exceedance probability | exceedance probability]]. Two standard versions exist: the occurrence exceedance probability (OEP) curve, which reflects the probability of the largest single event loss exceeding a threshold, and the aggregate exceedance probability (AEP) curve, which accounts for the total of all event losses in a year. Analysts read specific points on the curve to extract key metrics — for example, the loss at the 1-in-100-year return period, which corresponds to a 1% exceedance probability.&lt;br /&gt;
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🎯 Decision-makers across the insurance value chain rely on exceedance probability curves to make precise, quantifiable choices. A [[Definition:Chief risk officer (CRO) | chief risk officer]] uses the curve to determine how much [[Definition:Catastrophe excess-of-loss reinsurance | catastrophe reinsurance]] to buy and where to set [[Definition:Retention | retention]] levels. [[Definition:Rating agency | Rating agencies]] examine the shape of the curve — particularly the tail — to assess capital adequacy. [[Definition:Insurance-linked securities (ILS) | Insurance-linked securities]] investors study the curve to price [[Definition:Catastrophe bond | catastrophe bonds]]. Because the curve synthesizes an enormous amount of modeled data into a single visual, it enables comparison across portfolios, geographies, and perils, making it indispensable for strategic planning in any organization with significant catastrophe exposure.&lt;br /&gt;
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&amp;#039;&amp;#039;&amp;#039;Related concepts:&amp;#039;&amp;#039;&amp;#039;&lt;br /&gt;
{{Div col|colwidth=20em}}&lt;br /&gt;
* [[Definition:Exceedance probability]]&lt;br /&gt;
* [[Definition:Catastrophe model]]&lt;br /&gt;
* [[Definition:Probable maximum loss (PML)]]&lt;br /&gt;
* [[Definition:Occurrence exceedance probability (OEP)]]&lt;br /&gt;
* [[Definition:Aggregate exceedance probability (AEP)]]&lt;br /&gt;
* [[Definition:Return period]]&lt;br /&gt;
{{Div col end}}&lt;/div&gt;</summary>
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