ESG acceleration of the production process

Regulatory standards require insurers to evaluate the economic value of their balance sheet in a market consistent way. To that end, insurers use Economic Scenario Generators (ESG) to assess different economic risk factors and project financial returns cashflows.

Recent ESGs represent each risk factor using increasingly complex financial models which induce costly challenges in the production process. This complexity is amplified in a multi-norm framework where insurers are compelled to generate intensely multiple economic scenarios tables using different assumptions.

This article defines an integrated production process to accelerate ESG tables production in multi standard framework.
It identifies conceivable acceleration methodologies according to the sensitivities to be produced. It finally discusses the operational implementation of the acceleration process.
Here is the first part of our Experts’ Paper.

Under Solvency II or IFRS 17 regulatory standards, insurers apply market-consistent approaches to evaluate their balance sheets.

The use of ESGs and Monte-Carlo methods is thus necessary to evaluate the cost of financial options and guarantees embedded in life insurance policies.
Several economic risk factors can be modelled in an ESG: rates, equities, realestate, inflation and credit spreads, using stochastic financial models and following a specific production process: calibration, diffusion and validation.

The first step requires market consistent fitting of stochastic model’s parameters to spot market data when listed or to historical market data (e.g. calibration of real estate models).
With the use of correlation matrix, calibrated parameters are then injected in stochastic differential equations to project multiple scenarios of financial returns. Finally, generated scenarios undergo many statistical and Monte-Carlo tests to insure their martingality and market consistency.

Increasingly complex financial models are used in recent ESGs to satisfy regulatory requirements and generate scenarios consistent with the changing market context. These models engender many challenges to insurers at each step of the production process, and especially at calibration.

In fact, calibration of highly parametrized models is complex as the optimization algorithms estimate at each iteration theoretical semi-closed formulas.
The optimization problem become extremely complicated, and its convergence is time consuming. Furthermore, calibrated parameters are often saturated, i.e. occurrence of inaccurate calibrations where parameters are trapped in interval bounds defined in the optimization algorithm, leading to market consistency problems.

Along with the technical challenges of the complex models used in the ESG, the intensity of production increases drastically in a multi-standard framework, where insurers generate several economic scenarios tables for their quarterly or annual statements production or for their own studies.
These sensitivities include either a movement of the yield curve or the volatility levels, for instance:

• Solvency II closings: ESG tables using rate curves with and without volatility adjustment (VA) and applying standard formula chocs.
• IFRS 17 closing: ESG tables using rate curves with different VA levels according to the portfolio and for AoC.
• ESG tables for ORSA sensitivities.
• MCEV calculations: ESG tables with different interest rates and volatility chocs.

The standard production process becomes operationally intensive and can be accelerated by directly adjusting the economic scenario tables from a central reference table parameters or scenarios.

Furthermore, ESG calibrated parameters and scenarios produced for annual statements could be adjusted to accelerate quarterly closings.

What you will find in the second part:

David MARIUZZA

Partner,
Head of Modeling & Finance

Consultant Modeling & Finance