Chapter 18 - Modelling Flashcards
Operational Considerations when Building a Model
The model being used should be adequately documented.
Model workings should be easy to appreciate and communicate.
Results should be displayed clearly.
Model should exhibit sensible joint behaviour of model variables. eg. premium increases as sum insured increases
Outputs from the model should be capable of independent verification for reasonableness and should be communicable to those to whom advice will be given.
Model must not be overly complex so that either the results become difficult to interpret and communicate or the model becomes too long or expensive to run, unless this is required by the purpose of the model.
The model should be capable of development and refinement – nothing complex can be successfully designed and built in a single attempt.
A range of methods of implementation should be available to facilitate testing, parameterisation and focus of results.
Deterministic vs Stochastic Modelling
The advantages of a deterministic model are:
- more readily explicable to a non-technical audience, since the concept of variables as probability distributions is not easy to understand.
- clearer as to what economic scenarios have been tested.
- cheaper and easier to design, and quicker to run.
- Users can get ‘blinded by science’ by complex models, assuming they must be working correctly, but without verifying or testing this.
The disadvantage is that it requires thought as to the range of economic scenarios that should be tested.
A stochastic model tests a wider range of economic scenarios. The programming is more complex and the run time longer, but the benefit is in the quality of the result. It does depend on the parameters that are used in any standard investment model.
Stochastic models are particularly important in assessing the impact of financial guarantees or to allow for investment mismatching risks.
Sensitivity Analysis
Model and parameter error can be assessed using sensitivity analysis.
