What is a model
A cut down, simplified version of reality that captures essential features of a proble and aims to aid in understanding
Two main uses of models in actuarial work
Sensitivity analysis
Varying individual assumptions and assessing the impact on the results
Scenario analysis
Changing many assumptions in combination to assess the impact on the result
Approaches to modelling
What will be the merits of each approach to modelling depend on
Operational issues in constructing a model
Use by other staff
* The model used should be adequately documented - to understand assumptions and limitations
* Capable of redevelopment and refinement
* The outputs of the model should be capable of independent verification for reasonableness and should be communicable to those to whom advice will be given.
* Must not be overly complex such that:
1. The results are hard to interpret
2. Results are hard to communicate
3. Model becomes too long or expensive to run
Client
* The workings from the model should be easy to communicate and appreciate
Statistics
* The model should exhibit sensible joint behaviour of model variables
* A range of methods of implementation should be available to facilitate testing, parameterisation and focus of results
Computation
* The more frequently the cashflows are calculated, the more reliable the model
* The less frequently the cashflows are calculated, the faster the model can be run and results obtained
Explain the use of model points
How are model points chosen
Exisitng product - the profile of the existing business modified to allow for any expected changes in the future.
New business - the profile of any similar existing business with advice from the company’s marketing department
What will the number of model points depend on ?
Statistical risk
model risk + parameter risk + random fluctuation risk
How can statistical risk be assessed
Merits of a deterministic model
Advantages
* Easily understood by a non-technical audience.
* It is clearer what economic scenarios have been tested
* Usually cheaper, easier to design and quicker to run
* Users can get blinded by science by complex models, assuming they must be working correctly, without veryfying or testing this
Disadvantages
* Requires thought as to the range of economic scenarios which are tested.
* Since these are limited, there is a danger of not testing scenarios which are dentrimental to the company
*
Merits of a stochastic model
Considerations when choosing between a deterministic and stochastic model
Dynamism of the model
The asset and liability parts and all the assumptions are programmed to interact as they would in real life - rules for this would need to be determined
Steps in developing a deterministic model
Steps in developing a stochastic model
Model error
How can it be tested
If the model developed is not appropriate for the financial product, contract, transaction or scheme being modelled
Tests of goodness of fit , taking expected changes in the future experie
Parameter error
How can it be tested
Mis-estimation of paramter values
Sensitivity analysis
Course of action if a model is overly sensitive to a parameter
When assessing premiums for marketibility (competitiveness), what reconsiderations might be made?