CHP 31 Flashcards
When setting assumptions it is important to:
- Consider the use to which the assumptions will be put to, e.g. new product, reserves etc.
- Take particular care of the assumptions that will have the greatest financial impact.
- Consistency between assumptions
- Consider any legislative or regulatory constraints
- Consider the needs of the client
1.2. Different types of basis
In order of increasing strength the bases will be:
Optimistic -> best estimate -> prudent -> cautious
2.1. Historical data
This is likely to be the primary source for determining assumptions about future experience.
Examples of when past experience is useful as a starting point:
• Determining assumptions for future investment returns. E.g. dividend yields and returns on other asset classes. Where dividends are linked to an inflation index, past data on that index will be useful.
• Past data on salary levels in a particular country, industry or company
• History of inflation index for future benefit growth linked to either fully or partially to that index.
• Choosing demographic assumptions
2.2. Current data and forecasts
The relationship between current yields for fixed-interest and index-linked bonds may provide some indication of the market’s view of future levels of the inflation index to which the bonds are related.
Policy statements by government or controlling banks may also be useful when making assumptions about economic factors.
A scheme sponsor may be able to provide information on planned future salary increases or likely future rates of withdrawal.
In some cases, assumptions may be defined in regulation or legislation.
3.1. Relevance and credibility of past data
It is unlikely to be sensible to take an average past rate and use this to project for future experience. Past data only provides information that can be considered when determining the most likely future experience.
Social and economic conditions are likely to have changed over any period in history. Consider the conditions that will apply in the future period to which the projections will relate and how those conditions will lead to a difference from the past data being used.
The relevance of past data to future projections must be balanced against the need for sufficient data for its analysis to be statistically credible.
In making a judgment about future experience this conflict between credibility and relevance must be managed.
When using past data it is necessary to consider how to deal with:
- Abnormal fluctuations
- Changes in the experience with time
- Random fluctuations
- Changes in the way in which the data was recorded
- Potential errors in the data
- Changes in the balance of any homogenous groups underlying the data
- Heterogeneity with the group to which the assumptions are to relate.
Changes affecting economic data
Economic data fluctuates with changes in economic and fiscal policy as well as general economic cycle.
Past data relating to investment returns, salary levels, dividend yields etc. fluctuates significantly over extended periods of time. Because of this, it could be thought that past economic data is only useful after the last significant change. This will however increase the random fluctuation and decrease the credibility of the data.
Therefore it is needed to use the earlier data and try strip out the fluctuations that relate to economic and fiscal conditions that differ from those that exist currently.
Price inflation
Past levels of inflation index usually fluctuates significantly and are often a useful indicator of the economic conditions that existed.
Thus they are unlikely to be useful in determining an assumption of future levels of inflation. Current levels of inflation index may be a better indication of the future levels. E.g. government projections and the “risk-free” real returns indicated by the current yields on long-term index-linked bonds could be used.
Use of real values
Past data for price inflation can be very useful in determining other economic assumptions. Conversion of past economic data into real terms will often remove much of the fluctuation.
Other adjustments
Making further adjustments to economic or fiscal changes is difficult to do other than subjectively.
Dividend levels could be adjusted to allow for changes in taxation applying to those dividends.
An explicit adjustment may be spurious – there may be tax changes to tax that have a much greater effect.
Demographic changes
Much of the demographic data will also be affected by economic changes. Explicit adjustment is difficult and so judgment and analysis of fluctuations and trends will be important.
Mortality data is mainly affected by medical advances. Past data can be considered with this in mind.
This will probably result in emphasis of the most recent data with consideration of trends of past data. Also the underlying reasons for trends being important in determining the extent of future change.
Changes in statistics recorded
Over time, stats produced by state or data recorded by companies may change. Such changes distort past data and could lead to inappropriate assumptions unless these changes are recognized.
Errors in data recorded
These cause distortions and will not be as easy to pick up. Generally the verification of data has greatly improved due to increased computing power. Thus, older data carries the greater risk of data error, perhaps to the extent that it outweighs the usefulness of having more data.
Changes in the constituents of the population
Past data may give false results due to changes in balance between homogenous groups over time. E.g. past levels of salary growth may reflect a change in the overall composition of the workforce rather than real salary level changes for individuals.
Splitting the population into homogenous groups
Split the population into groups that fit a set of assumptions. In practice, the information required to split the data reliably will not be available. In addition, splitting the data may result in a significant reduction of credibility. Thus past data will usually need to be adjusted in a subjective manner to allow for differences in the characteristics of the individuals concerned.