Monte Carlo simulation is widely used in retirement modelling to explore uncertainty in investment returns and inflation. It helps analyse a range of possible futures rather than relying on a single forecast. In EvolveMyRetirement®, Monte Carlo results form the basis of the utility calculation used during optimisation. This page explains how Monte Carlo applies to retirement planning and how it is used within EvolveMyRetirement.
Retirement planning can include periods both before and after retirement. These can span many decades, during which investment returns, inflation, and personal circumstances may vary significantly from year to year. A single projection cannot capture this uncertainty. Monte Carlo simulation generates many possible future paths, each reflecting different combinations of market behaviour and inflation. This provides a distribution of outcomes that helps analyse how variable long‑term financial results can be.
Another uncertainty is longevity. EvolveMyRetirement does not assume a fixed life expectancy, but instead uses mortality data from the Office for National Statistics. Each simulation uses a randomised lifespan derived from this data.
Historical market returns cover a little over a century, which is a relatively small statistical sample for modelling outcomes that may extend over several decades. Using such limited data directly can lead to a high margin of error.
Instead, historical data is used to estimate statistical characteristics such as the mean and standard deviation of returns. These estimates are applied to a lognormal distribution within the Monte Carlo simulation. This approach allows the model to generate a wide range of possible futures without being constrained by the limited number of historical paths.
EvolveMyRetirement uses Monte Carlo simulation as part of the utility function during optimisation. Each candidate strategy generated by the Genetic Algorithm is evaluated using many simulations, producing a calculated utility value for that strategy. This utility depends both on the simulated outcomes and on the user’s modelled preferences and attitudes.
The resulting utility values are fed into the optimisation process, which explores different combinations of strategy settings. This helps identify settings that balance sustainability, volatility, and spending stability across many possible future scenarios, in line with the user's modelled preferences and attitudes.
Whenever a user updates any part of their plan, assumptions, or strategy, Monte Carlo simulation is rerun as soon as they navigate to the Results page. This ensures that the outcomes shown always reflect the latest inputs. The results include probability bands, percentiles, and sustainability indicators derived from the distribution of simulated outcomes.
If centrally maintained assumptions change, such as tax rules, annuity rates, or other modelling parameters, EvolveMyRetirement automatically reruns Monte Carlo simulation. This ensures that results remain consistent with the latest rules and modelling framework without requiring any action from the user.
When a user requests a sensitivity analysis, EvolveMyRetirement runs additional Monte Carlo simulations. Typically four simulations are performed: two above and two below the current setting. Some variables use a fixed set of values, each with its own simulation. The sensitivity analysis analyses how changes in a single assumption affect the distribution of outcomes.
Monte Carlo simulation illustrates uncertainty; it does not predict specific outcomes. Percentiles show ranges of possible futures, and sustainability indicators summarise the distribution of results. Outcomes depend on the assumptions entered, and real‑world results may differ from those explored in the model.
