Chp 29-32, 48 Flashcards
1) Requirements of a good model (Acronym – VARIABLE )
Valid Adequately documented Rigorous Inputs to parameter values appropriate Arbitrage free (economic interpretation) Behaviour reasonable Length/expense of run not too long/high Easy to understand
1) Requirements of a good model (Acronym – CRISPS )
Communicable workings and outputs
Reflects risk profile of contracts modelled
Independent verification of outputs
Sensible joint behaviour of variables
Parameters allow for all significant features
Simple (parsimonious) but retain key features
1) Requirements of a good model (Acronym – CARD)
Clear results
A range of implementation methods
Refineable
Developable
2) Cashflows to include in a model
a) Cashflow arising from any supervisory or commercial requirement to hold reserves and to maintain adequate solvency capital
b) Cashflow need to allow for any interactions, particularly where assets and liabilities are being modelled together
c) Potential cashflows from options and take-up rate need to be allowed for
d) More frequently the cashflows are calculated, the more reliable output from the model, less frequently the cashflows are calculated the faster the model can be run and results obtained
3) Approaches to modelling and factors determining their merits - approaches
a) Approaches to modelling
i) Purchase commercial modelling product
ii) Reuse existing model with modification
iii) Develop a new model in house
b) Merit depends on
Cost of each option Level of accuracy required In-house expertise available Number of times use Desired flexibility of the model
a) Deterministic model +/-
i) Pros
(1) More readily explicable to a non-technical audience
(2) Clearer what economic scenarios have been tested
(3) Easier to design and quicker to run
ii) Cons
(1) Requires thought as to the range of economic scenarios that should be tested
b) Stochastic model +/-
i) Pros
(1) Tests a wider range of economic scenarios
(2) Quality of results may be higher than deterministic modelling
ii) Cons
(1) Programming is more complex and run time is longer
(2) Danger of spurious accuracy if number of runs too small
a) Steps in running a deterministic model
Specify the purpose of the investigation
Collect, group, and modify data
Choose the form of the model, identifying its parameters and variables
Ascribe values to the parameters using past experience and appropriate estimation techniques
Construct a model based on the expected cashflows
Check that the goodness of fit is acceptable – run past year and compare model with actual results
Attempt to fit different model if first choice does not fit well
Run model using selected values of variables
Run model using estimates of values of variables in future
Run model several times to assess sensitivity of results to different parameter values
b) Steps in running a stochastic model
Specify the purpose of the investigation
Collect, group, and modify data
Choose a suitable density function for each of the variables to be modelled stochastically
Specify correlation between variables
Construct a model based on the expected cashflows
Check the goodness of fit is acceptable – run past year and compare model with actual results
Attempt to fit a different model if the first model does not fit well
Run the model many times, each time using a random sample from the chosen density function(s)
Produce a summary of the results that shows the distribution of the modelled results after many simulations have been run
6) Ways of assessing the variability of experience (scenario testing and stochastic modelling)
a) Scenario testing – various scenarios can be tested by varying the parameter values in the model and assessing their results to see how the experience varies with varying each parameter
b) Stochastic modelling – This will give a probabilistic distribution of experience, whereby the expected experience and deviation from the expected experience can be analysed
8) Sources of data
Tables – e.g. actuarial mortality tables Regulatory reports and company accounts Abroad – data from overseas contracts Industry data National statistics Experience investigations on the existing contract Reinsurers Similar contracts
9) Problems relating to data quality and how to overcome them
a) Problems relating to data quality
i) Result of poor management control
ii) Poor verification processes
iii) Poor design of data systems
b) How to overcome them
i) Information on proposal form
(1) Questions need to be well designed and unambiguous
(2) Cross check against claims information
10) Data checks
Investment income – consistency between asset data and accounting data
Random spot checks on data Average sum assured or premium for each class of business – should be sensible and consistent with previous investigation Number of members/policies and changes – using previous data and movement data
Shareholdings – at start and end of period adjusted for sales, purchases and bonus issues
Audit of certain assets – e.g. checking title deeds to large real property assets
Assets held by third party –reconcile between beneficial owner’s and custodian’s records
Movement data – check against appropriate accounting data
Benefit amounts and premiums – reconcile total and changes using previous data and movement data
Unusual values – e.g. impossible dates of birth, retirement ages, start dates
Salary-related contributions and in-payment benefit levels – compare membership data and figures in accounts
11) Advantages and disadvantages of industry-wide collection schemes (Acronym - DR DONEQ)
Detail insufficient
Reporting formats differ
Differences in target markets, underwriting, geographical area, sales processes, contract wordings, claim settlement, rating factors
Out of date
Not everyone contributes
Errors
Quality only as good as that of contributors