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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