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	<title>Definition:Economic scenario generator - Revision history</title>
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	<updated>2026-05-02T12:40:59Z</updated>
	<subtitle>Revision history for this page on the wiki</subtitle>
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		<id>https://www.insurerbrain.com/w/index.php?title=Definition:Economic_scenario_generator&amp;diff=12952&amp;oldid=prev</id>
		<title>PlumBot: Bot: Creating new article from JSON</title>
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		<updated>2026-03-13T12:22:06Z</updated>

		<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;Economic scenario generator&amp;#039;&amp;#039;&amp;#039; is a stochastic modeling tool used by insurers and reinsurers to project a wide range of plausible future economic conditions — including interest rates, equity returns, inflation, credit spreads, and foreign exchange movements — for the purpose of valuing [[Definition:Insurance liability | insurance liabilities]], assessing [[Definition:Solvency | solvency]], and informing [[Definition:Asset-liability management (ALM) | asset-liability management]] decisions. Unlike deterministic forecasts that rely on a single &amp;quot;best estimate&amp;quot; path, an economic scenario generator (ESG) produces thousands or even millions of simulated pathways, capturing the uncertainty and tail risks that are central to sound [[Definition:Risk management | risk management]] in insurance. ESGs are foundational components of [[Definition:Internal model | internal models]] used under regulatory regimes such as [[Definition:Solvency II | Solvency II]] in Europe and play a critical role in [[Definition:Stochastic reserving | stochastic reserving]] and [[Definition:Capital modeling | capital modeling]] globally.&lt;br /&gt;
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⚙️ An ESG operates by calibrating mathematical models — often drawing on techniques from financial economics such as the Black-Scholes framework, the Cox-Ingersoll-Ross model for interest rates, or more complex multi-factor approaches — to current market data and historical patterns. Insurers then run Monte Carlo simulations to generate probability-weighted distributions of future economic states over horizons that may stretch decades into the future, reflecting the long-tail nature of [[Definition:Life insurance | life insurance]] and [[Definition:Annuity | annuity]] obligations. In practice, ESGs feed directly into [[Definition:Actuarial valuation | actuarial valuation]] platforms: life insurers use them to project the interaction between policyholder guarantees and investment returns, while [[Definition:Property and casualty insurance | property and casualty]] companies may use them to stress-test [[Definition:Investment portfolio | investment portfolios]] under adverse macroeconomic scenarios. Under [[Definition:IFRS 17 | IFRS 17]], which requires [[Definition:Market-consistent valuation | market-consistent valuation]] of insurance contracts, ESGs have become even more central, as they provide the risk-neutral scenarios needed to discount future [[Definition:Cash flow | cash flows]] and calculate the [[Definition:Risk adjustment | risk adjustment]].&lt;br /&gt;
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💡 The quality and calibration of an economic scenario generator can materially affect an insurer&amp;#039;s reported financial position, regulatory capital requirements, and strategic investment decisions. A poorly calibrated ESG may understate tail risks — leading to inadequate [[Definition:Reserve | reserves]] or overly aggressive [[Definition:Investment strategy | investment strategies]] — while an overly conservative one can tie up capital unnecessarily. Regulators in jurisdictions from the [[Definition:Prudential Regulation Authority (PRA) | PRA]] in the United Kingdom to the [[Definition:China Banking and Insurance Regulatory Commission (CBIRC) | CBIRC]] in China scrutinize the assumptions embedded in ESGs when approving internal models or reviewing [[Definition:Own Risk and Solvency Assessment (ORSA) | ORSA]] submissions. For insurtech firms and vendors, building and licensing ESG platforms has become a significant line of business, with providers competing on computational speed, transparency of methodology, and the ability to incorporate emerging risks such as climate transition scenarios into their projections.&lt;br /&gt;
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&amp;#039;&amp;#039;&amp;#039;Related concepts:&amp;#039;&amp;#039;&amp;#039;&lt;br /&gt;
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* [[Definition:Stochastic modeling]]&lt;br /&gt;
* [[Definition:Asset-liability management (ALM)]]&lt;br /&gt;
* [[Definition:Capital modeling]]&lt;br /&gt;
* [[Definition:Monte Carlo simulation]]&lt;br /&gt;
* [[Definition:Market-consistent valuation]]&lt;br /&gt;
* [[Definition:Internal model]]&lt;br /&gt;
{{Div col end}}&lt;/div&gt;</summary>
		<author><name>PlumBot</name></author>
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