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ERM Glossary: Economic Scenario Generator (ESG)

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The term ‘economic scenario generator’ (ESG) is the name given in insurance or actuarial science to a tool that allows a user to simulate many possible ways in which economic conditions might evolve in the future, and to ascribe a likelihood to these scenarios.

 

Often an ESG uses Monte Carlo simulation. In its most basic form, this simulation approach assumes that each potential evolution provided by the ESG has equal likelihood (i.e. each is an equally likely draw from the relevant distribution of possible future outcomes being modelled by the ESG). More sophisticated simulation techniques can give different assumed probabilities of occurrence to different sampled outcomes, or can select the outcomes in other ways designed to provide a more ‘uniform’ fit to the overall distribution, to speed up computation times.

 

ESGs can target either so-called ‘real-world’ distributions of outcomes or so-called ‘risk-neutral’ distributions of outcomes.

 

Real world distributions aim to characterise the actual likelihoods of different outcomes. They can be useful for identifying the likely actual range of results.

 

Risk-neutral distributions are adjusted so that the probability-weighted average of the modelled values ascribed to a given set of cash flows under each scenario is aligned with the observed market value of that set of cash flows.

 

For example, commonly it is assumed that riskier assets such as equities will on average deliver a higher average return than less risky assets such as cash, albeit with increased risk of significant loss. The valuation of an equity instrument using an ESG targeting a real-world distribution would then include this assumed (average) outperformance. A consequence is that the present ‘value’ it would place on the equity instrument would likely be higher than its actual market value. Risk neutral distributions eliminate this bias and thus provide a better basis if the aim is to estimate the market consistent or fair values to ascribe to different instruments.

 

ESGs (and their use in Solvency II) are explained further in Varnell (2009).

 


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