Introduction

Many risks that are measured develop over time. As such, it is important that the ways in which these risks develop are correctly modelled. This means that a good understanding of time series analysis is needed.

Deterministic modelling

There are two broad types of model: deterministic and stochastic. At its most basic, deterministic modelling involves agreeing a single assumption for each variable for projection. The single assumption might even be limited to the data history, for example the average of the previous monthly observations over the last twenty years.

With deterministic approaches, prudence can be added only through margins in the assumptions used, or through changing the assumptions. A first stage might be to consider changing each underlying assumption in turn and noting the effect. This is known as sensitivity analysis. It is helpful in that it gives an idea of the sensitivity of a set of results to changes in each underlying factor, thus allowing significant exposures to particular risks to be recognised. However, variables rarely change individually in the real world. An approach that considers changes in all assumptions is therefore needed.

This leads us to scenario analysis. This is an extension of the deterministic approach where a small number of scenarios are evaluated using different pre-specified assumptions. The scenarios used might be based on previous situations, but it is important that they are not restricted to past experience – a range of possible futures is considered.