Insurance & Actuarial8 min read

Economic Scenario Generators: Role in Insurance Risk

By Jonas Adam Mohamed Osman, known as Yonas Osman

CEO of Quantica Risk

60 Rue François Ier, 75008 Paris, France

Published 15 October 2026

How insurers use economic scenario generators for capital modelling, ALM and ORSA, and the key validation challenges actuaries must address.

Executive answer

Economic scenario generators (ESGs) produce the thousands of stochastic simulations of interest rates, equity returns, credit spreads, inflation and other economic variables that underpin insurers' internal capital models, asset-liability management, and market-consistent valuation of options and guarantees embedded in life insurance products. The quality of an ESG's calibration directly determines the reliability of the capital figures and hedging decisions built on top of it, making ESG governance a core actuarial responsibility rather than a purely technical modelling exercise.

Supervisors reviewing internal models under Solvency II routinely focus heavily on ESG validation, because a poorly calibrated or badly understood ESG can silently distort capital requirements across an entire balance sheet without any obvious red flag in the headline output. Firms using third-party vendor ESGs are not exempt from this scrutiny; they remain responsible for understanding and validating the calibration, not simply trusting vendor documentation.

Real-world versus risk-neutral calibration

ESGs are typically run in two modes: risk-neutral calibration, used for market-consistent valuation of options and guarantees where all assets are assumed to earn the risk-free rate, and real-world calibration, used for ORSA capital projections and strategic asset allocation where expected returns reflect actual risk premia. Confusing these two modes, or applying risk-neutral outputs to a real-world question such as long-term funding adequacy, is a common and serious modelling error that produces systematically misleading results.

Real-world calibration requires explicit judgement about equity risk premia, term premia and other risk compensation parameters that are inherently more subjective than risk-neutral calibration, which is anchored to observable market prices of traded options and bonds. Actuaries should document the rationale for real-world parameter choices clearly, since these assumptions materially affect long-term projections used in strategic decisions like new business pricing and capital planning.

Negative and low interest rate regime challenges

The shift from the prolonged low and negative interest rate environment of the 2010s to the higher rate regime since 2022 has tested many ESGs' ability to produce plausible scenario distributions across very different starting conditions, particularly for models calibrated primarily on the low-rate historical period. Models that embedded assumptions appropriate to near-zero rates, such as certain volatility term structures or mean-reversion speeds, needed material recalibration to remain credible once rates normalised, and firms that delayed this recalibration risked materially understating capital requirements during the transition.

Interest rate model choice, whether a one-factor or multi-factor term structure model, Libor Market Model, or other approach, affects the realism of yield curve shapes generated across simulations, with some simpler models prone to generating implausible curve inversions or negative rates even in regimes where these are economically unlikely. Validation should explicitly check the plausibility of generated yield curve shapes against historical and current market observations, not just summary statistics like mean and standard deviation of simulated rates.

Validation practices and governance

Robust ESG validation includes martingale tests to confirm risk-neutral scenarios correctly price a set of benchmark instruments, comparison of implied volatility surfaces generated by the model against observed market volatility, and out-of-sample backtesting of real-world scenarios against subsequent realised economic outcomes where data permits. Validation should be performed by a function independent of the team responsible for calibration, consistent with the broader model governance expectations supervisors apply to internal models generally.

Governance frameworks should specify clear triggers for ESG recalibration, such as material market regime shifts or validation test failures, rather than relying solely on an annual recalibration cycle that may miss the need for interim updates during fast-moving market conditions. Documentation of calibration choices, validation results and any expert judgement overrides should be maintained to a standard that would satisfy both internal audit and external supervisory review without requiring reconstruction after the fact.

Conclusion

Economic scenario generators are foundational infrastructure for insurance capital and valuation, and their calibration quality deserves scrutiny proportionate to how much downstream decision-making depends on their output. The shift in interest rate regimes since 2022 has been a useful stress test of which insurers had genuinely robust ESG governance versus those relying on stale calibrations that happened to work during a stable period.

Firms using vendor ESGs should resist the temptation to treat the model as a black box simply because a reputable vendor supplies it. Jonas Adam Mohamed Osman, CEO of Quantica Risk, approaches ESG validation by independently testing generated scenario plausibility against current market conditions before any output feeds into a client's capital or ALM decisions.

Primary sources

economic scenario generatorscapital modellingALMstochastic modelling