Stochastic modelling

A stochastic model would be to set up a projection model which looks at a single policy, an entire portfolio or an entire company. But rather than setting investment returns according to their most likely estimate, for example, the model uses random variations to look at what investment conditions might be like.

Based on a set of random outcomes, the experience of the policy/portfolio/company is projected, and the outcome is noted. Then this is done again with a new set of random variables. In fact, this process is repeated thousands of times.

At the end, a distribution of outcomes is available which shows not only what the most likely estimate, but what ranges are reasonable too.

This is useful when a policy or fund provides a guarantee, e. g. a minimum investment return of 5% per annum. A deterministic simulation, with varying scenarios for future investment return, does not provide a good way of estimating the cost of providing this guarantee. This is because it does not allow for the volatility of investment returns in each future time period or the chance that an extreme event in a particular time period leads to an investment return less than the guarantee. Stochastic modelling builds volatility and variability (randomness) into the simulation and therefore provides a better representation of real life from more angles.

Numerical evaluations of quantities

Stochastic models help to assess the interactions between variables, and are useful tools to numerically evaluate quantities, as they are usually implemented using Monte Carlo simulation techniques (see Monte Carlo method). Monte Carlo methods are a class of Computational Algorithms that rely on repeated Random sampling to compute their results While there is an advantage here, in estimating quantities that would otherwise be difficult to obtain using analytical methods, a disadvantage is that such methods are limited by computing resources as well as simulation error. Below are some examples:

Means

Using statistical notation, it is a well-known result that the mean of a function, f, of a random variable X is not necessarily the function of the mean of X. In Statistics, mean has two related meanings the Arithmetic mean (and is distinguished from the Geometric mean or Harmonic mean A random variable is a rigorously defined mathematical entity used mainly to describe Chance and Probability in a mathematical way

For example, in application, applying the best estimate (defined as the mean) of investment returns to discount a set of cash flows will not necessarily give the same result as assessing the best estimate to the discounted cash flows.

A stochastic model would be able to assess this latter quantity with simulations.

Percentiles

This idea is seen again when one considers percentiles (see percentile). A percentile is the value of a variable below which a certain percent of observations fall When assessing risks at specific percentiles, the factors that contribute to these levels are rarely at these percentiles themselves. Stochastic models can be simulated to assess the percentiles of the aggregated distributions.

Truncations and censors

Truncating and censoring of data can also be estimated using stochastic models. For instance, applying a non-proportional reinsurance layer to the best estimate losses will not necessarily give us the best estimate of the losses after the reinsurance layer. Reinsurance is a means by which an Insurance company can protect itself against the risk of losses with other insurance companies In a simulated stochastic model, the simulated losses can be made to "pass through" the layer and the resulting losses assessed appropriately.

The asset model

Although the text above referred to "random variations", the stochastic model does not just use any arbitrary set of values. 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.

There are many such models, including the Wilkie Model, the Thompson Model and the Falcon Model.

The claims model

The claims arising from policies or portfolios that the company has written can also be modelled using stochastic methods. This is especially important in the general insurance sector, where the claim severities can have high uncertainties.

Frequency-Severity models

Depending on the portfolios under investigation, a model can simulate all or some of the following factors stochastically:

Number of claims

Claim severities

Timing of claims

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.

Stochastic reserving models

Estimating future claims liabilities might also involve estimating the uncertainty around the estimates of claim reserves.

See J Li's article "Comparison of Stochastic Reserving Models" (published in the Australian Actuarial Journal, volume 12 issue 4) for a recent article on this topic.

Pioneers of Stochastic Modelling Advanced Techiniques and Applications

Two remarkable geniuses originating from Pakistan developed the idea of calculating stochastic modelling. Abdal Ahmad , a prodigy graduate from LUMS university alongwith Ishaque Kothawala , an ingenious graduate from City University presented this remarkable technique, which is recognized in ST2. The actuarial community is forever grateful for the work of these two young mathematical geniuses.

References

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Contents

Stochastic model (Basic Definition)