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	<title>Definition:Historical event set - Revision history</title>
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		<title>PlumBot: Bot: Creating new article from JSON</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;Historical event set&amp;#039;&amp;#039;&amp;#039; is a collection of past loss-causing events — such as hurricanes, earthquakes, floods, or industrial catastrophes — used within [[Definition:Catastrophe modeling | catastrophe models]] and [[Definition:Actuarial science | actuarial analyses]] to calibrate risk estimates, validate model outputs, and stress-test insurance portfolios against real-world experience. In the insurance and [[Definition:Reinsurance | reinsurance]] industry, historical event sets serve as a critical empirical anchor: while modern [[Definition:Catastrophe model | catastrophe models]] generate thousands of [[Definition:Stochastic event set | stochastic]] (simulated) scenarios to estimate potential losses, the historical event set provides the observable record against which those simulations are benchmarked. Model vendors such as [[Definition:Risk Management Solutions (RMS) | RMS]], [[Definition:AIR Worldwide | AIR Worldwide]], and [[Definition:CoreLogic | CoreLogic]] maintain curated historical event databases that reconstruct the physical characteristics of past events — wind fields, ground motion, flood depths — and apply them to current exposure portfolios.&lt;br /&gt;
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⚙️ Using a historical event set, an [[Definition:Underwriter | underwriter]] or [[Definition:Risk manager | risk manager]] can answer a deceptively simple but strategically vital question: &amp;quot;What would Hurricane Andrew, the Tōhoku earthquake, or Windstorm Lothar cost us if it happened today, given our current book of business?&amp;quot; The historical event is reconstructed with its original physical parameters and then run through the model&amp;#039;s vulnerability and financial modules against the insurer&amp;#039;s present-day [[Definition:Exposure | exposure]] data. This produces a &amp;quot;what-if&amp;quot; loss estimate that accounts for changes in insured values, urbanization patterns, building codes, and [[Definition:Policy terms and conditions | policy terms]] since the original event. Historical event sets are also essential for model validation: if a catastrophe model&amp;#039;s stochastic output does not produce loss distributions that are broadly consistent with what the historical record shows, confidence in the model diminishes. Regulators in some jurisdictions — including [[Definition:Lloyd&amp;#039;s of London | Lloyd&amp;#039;s]], which requires syndicates to report [[Definition:Realistic disaster scenario (RDS) | realistic disaster scenario]] losses — explicitly incorporate historical events into their supervisory frameworks.&lt;br /&gt;
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🧭 The limitations of historical event sets are as important to understand as their utility. The observable record of catastrophic events is geologically and meteorologically short — perhaps a few centuries of useful data for windstorms in the North Atlantic, even less for earthquake events in many regions — meaning that the historical set almost certainly underrepresents the full range of plausible outcomes. This &amp;quot;sample size problem&amp;quot; is precisely why the industry supplements historical data with stochastic simulation, but it also means that over-reliance on historical experience can create blind spots. Events like the 2011 Thailand floods or the 2011 Tōhoku earthquake exceeded many modelers&amp;#039; historical calibration ranges and produced insured losses that surprised the market. For [[Definition:Insurtech | insurtech]] firms and advanced analytics teams, enriching historical event sets with paleoclimate data, improved geospatial resolution, and machine-learning-enhanced reconstructions represents a growing area of innovation — one that can materially improve the accuracy of [[Definition:Probable maximum loss (PML) | probable maximum loss]] estimates and the resilience of [[Definition:Reinsurance program | reinsurance programs]] built upon them.&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:Catastrophe model]]&lt;br /&gt;
* [[Definition:Stochastic event set]]&lt;br /&gt;
* [[Definition:Realistic disaster scenario (RDS)]]&lt;br /&gt;
* [[Definition:Probable maximum loss (PML)]]&lt;br /&gt;
* [[Definition:Exposure management]]&lt;br /&gt;
* [[Definition:Loss exceedance curve]]&lt;br /&gt;
{{Div col end}}&lt;/div&gt;</summary>
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