A stochastic model is a tool for estimating probability distributions of potential outcomes by allowing for random variation in one or more inputs over time.
The random variation is usually based on fluctuations observed in historical data for a selected period using standard time-series techniques.
Distributions of potential outcomes are derived from a large number of simulations (stochastic projections) which reflect the random variation in the input(s).
So the valuation of an insurer involves a set of projections, looking at what is expected to happen, and thus coming up with the best estimate for assets and liabilities, and therefore for the company's level of solvency.
Stochastic modelling builds volatility and variability (randomness) into the simulation and therefore provides a better representation of real life from more angles.
The asset model is based on detailed studies of how markets behave, looking at averages, variations, correlations, and more.
The models and underlying parameters are chosen so that they fit historical economic data, and are expected to produce meaningful future projections.
Depending on the portfolios under investigation, a model can simulate all or some of the following factors stochastically: Claims inflations can be applied, based on the inflation simulations that are consistent with the outputs of the asset model, as are dependencies between the losses of different portfolios.
The relative uniqueness of the policy portfolios written by a company in the general insurance sector means that claims models are typically tailor-made.