Principles of actuarial modelling Flashcards
14 Key Steps in a modelling process
Advantages of models
(SPE ISC3)
Models allow us to investigate the future behavior of a process in compressed time
Models allow us to study the stochastic nature of the results e.g using Monte Carlo simulation
Models allow us to consider scenarios that would not be feasible in practice e.g because of cost or other business considerations
Models allow control over experimental conditions so that we can reduce the variance of the results output without affecting the mean values
SPE
Complex systems with stochastic elements cannot be properly described by a mathematical or logical model that is capable of producing results that are easy to interpret. Simulation modelling is a way of studying these complex systems
Different future policies or possible actions can be compared to see which best suits the requirements or constraints of the user
we can get control over the experimental conditions by reducing the variance without upsetting their mean values
Disadvantages
Models can be time-consuming and expensive to set up
Stochastic models require a large number of simulations to be carried out to get accurate results
In general, models are more useful for comparing the results of input variations than optimising outputs
Models can give an impression of greater accuracy and reliability than they actually have and so may create a sense of false security
The results from models are dependent on the data used which may be inaccurate and unreliable
Users of the model may not understand fully how it works and its limitations
Models may not capture sufficiently accurately the real world situation
Models require simplifying assumptions that might turn out wrong e,g may ignore certain features or certain types of events that could occur
Some models may be difficult to explain to clients
Suitability of a model
OV3 PCR CRAE
Turing Test
experts on the real world system are asked to compare several sets of real world and model data without being told which is which
if an expert can differentiate between the real world and model data their technique for doing could be used to improve the model
What to consider when communicating the results
KVVDL2
Deterministic vs Stochastic
A deterministic model uses one set of input parameters and gives the results of the relevant calculations for this single scenario.Special case of the stochastic model
A stochastic model involves at least one input parameter varying according to an assumed pd. As such the output will vary along with input and the model produces distribution of the relevant results for a distribution of scenarios
Often the output from a stochastic model is in the form of many thousands of simulated outcomes of the process. We can study the distribution of these outcomes.
Issues/parameters to consider when modelling a life office
regulations, taxation, cancellation terms, future events affecting returns, inflation, new business, lapses, mortality and expenses
Scenario Based
would take into consideration a particular scenario; that is a series of input parameters based on this scenario. Different scenario would be useful in decision analysis as one can evaluate the impact of a course of action
Proxy
used to replace monte carlo simulations (used to project assests and liabilities)
Example of when you would use scenario based
we could model the financial performance of a company under different future scenarios such recession or economic boom. The value of any input paramters (inflation,interest rate or level of taxation) would be select to be appropriate to the specific scenario under consideration
Sensitivity analysis/testing
involves testing the robustness of the model by making small changes to the input parameters. This should result in small changes to the output from the model that consistent with the real world behaviour of the situation we are modelling
Advantages of stochastic model over the deterministic
It reflects reality as accurately as possible as it imitates the random nature of the variables involved
can provide information about the distribution of the results, not just a single estimate figure
