Ch 18: Models 1 Flashcards
Summary Card
- Prime objective of models
- Types of models
- Requirements of models
- Stochastic & deterministic models
State the prime objective in building a life insurance company model (2)
- Enable actuary to give appropriate advice in company…
- …so that it can be run in a sound way
State the requirements of a good model (14)
-
Valid for purpose
- deterministic vs stochastic
- includes all notable features of what is being modelled
-
Rigorous
- realistic results under wide range of circumstances
-
Well documented
- audit trail, key assumptions/approximation
- Reflect risks being modelled
-
Components used allow for material aspects being modelled
- structural components:
- parameters: only include param if results differs for different values of the parameter
-
Parameter values appropriate
- for particular business
- general environment, account for special features of company/economic environ
-
Sensible joing behaviour of variables eg:
- higher expense inflation => higher claims inflation
- higher claims rates => higher reinsurance recoveries
- higher inflation => higher (nominal) bond yields, equity returns?
-
Easy to
- Understand/appreciate model
- Communicate model
-
Output reasonable able to independent verify reaonableness
- Reconcile with supervisory valuation
- Reconcile with results from last run
- Ratio checks on future results
- Back of the envelope model
-
Output communicable
- to those who advice will be given to
- mentioning underlying method, critical assumptions
- Results displayed clearly
-
Not overly complex to
- understand
- explain/communicate
- expensive to run
- Able to develop/refine over time
-
Dynamic: assets and liabilities
- assumptions used to model assets/liabitlies must be consistent
- interactions between assets/liabilities modelled
We’ve already covered the basic requirements of a good model.
List the general features of a life insurance company model specifically (5)
- Model may be used to model different types of business
- Model should project all cashflows that may arise
- Allow for interactions/corellations between variables (dynamic links; joint sensible behaviour)
- Guarantees/options should be propertly allowed for; stochastic model best for this
- Projection frequency/time period
List the 4 different types of life insurance company models (that differ in the policies that are included in the model).
Briefly descibe what each model does
-
Profit testing model
- projects expected cash and profit flows on policies from date of issue
- key for pricing/product design
- New business model
- projects all expected cash and profit flows arising from future sales of new business
- useful for assessing future capital requirements for new business/overall return on capital achieved from future sales
- Existing business model
- cash & profit flor projection from all existing business company has in force at particular time point
- important for assessing intrinsic value of existing business and testing solvency of company’s existing business
- Full model office
- sum of new and existing business model
- of fundamental importance in assessing impact of future maangement decisions on company’s future financial development
Features of a life insurance model:
- Projecting cashflows/profit (3)
Projecting cashflows/profit
- model must allow for all cashflows that may arise
- depends on contract’s nature, premium, benefit structure, discretionary benefits
- supervisory reserves and solvency requirement (allow for cashflows arising from supervisory need to hold reserves/solvency capital)
- real cashflows
- premiums, investment income, payments to policyholders, commission to agents, expenses, tax
- notional cashflows
- fund establishment of reserves, by contributing money to reserves from cashflow or initially from company’s free assets
- this increase in reserves is negarive from company’s perspective
- at maturity/claim, reserves will be released to help pay appropriate benefit => decrease in reserves positive
- supervisory solvency capital
- In addition to supervisory reserve, might be minimum supervisory solvency capital requirement to cover.
- policy cashlfo might also need to fund establishment of solvency margin.
- required solvency margin included in value of reserves
Features of a life insurance model:
- allowing for interactions (2,5)
Cashflows need to allow for any interactions, particularly where assets and liabilities are being modelled together.
- Dymaic model (asset/liability parts programmed to interact as in real life
- investment return and bonus rates
- supervisory reserves & projected investment conditions
- investment strategy response to changing conditions
- Links are important
1. for all models, but
2. particularly for stochastic models, as variables are changing yearly and ongoing response need to occur automatically as each simulation is run
- Links are important
Features of a life insurance model:
projection frequency and time period
(3)
(5)
-
Frequency
- more frequent cashflows calculation => more reliable output
- less frequent cashflow calculation faster model is run
- usually monthly
- Period
- Whole company models
- projection period chosen normally 3…5 years
- anything more expose to doubt, especially regarding level and mix of new business,
- but may usefully indicate significant trends, especially regarding solvency.
- Individual product cashflows for profit testing purposes the projection period used will be the policy term.
Features of a life insurance model:
- allowance for guarantees/options (3)
- Where health options exist (e.g. option to effect a new term assurance contract without providing further evidence of health), the potential cashflows from such options need to be allowed for.
- Allow for effect on supervisory reserves
-
Allow for stochastic models/simulations
- …where appropriate, in order to assess impact of financial guarantees (e.g. minimum maturity guarantees)
State 5 advantages of deterministic models compared to stochastic models (5)
- Easier to explain (particularly to non-technical audience)
- Easier to interpret/understand
- Clearer which (economic) scenarios have been tested
- Easier to design
- Easier/shorter to run
Give examples of circumstances in which deterministic models might be appropriate (4)
-
If similar results possible as if stochastic projection were used.
- Possible outcomes form a symmetric distribution/information and information only required on the expectation, or
- specific scenario being tested within simple cashflow model
- Quick, independent test is required to see that the results of a stochastic projection are reasonable
- To provide upper and lower bounds
- To avoid nested stochastic model
What is a stochastic model?
For stochastic models compared to deterministic models:
- State 3 advantages
- State 2 disadvantages
- A stochastic model is one in which we assign probability distributions to one or more unknown parameters.
Advantages
- Distribution of outcomes (not just single outcome) because can prob distribution to one/more unknown future parameters
- Positive liability can be calculated where deterministic approach might otherwise produce zero liability e.g. costing options and guarantees
- Interactions explicitly modelled i.e parameters may be assumed to vary together
Disadvantages
- Time and computing constraints
- Possible spurous accuracy i.e. results very senstitive to (deterministically chosen) assumed values of parameter(s) involved
Describe 2 approaches to calibrating stochastic models of economic variables
(5)
(4)
Risk neutral/market-consistent
- market-consistent: typically used for valuation purposes, particularly where there are options/guarantees
- focus: attempt to replicate market prices of financial instruments as closely as possible using risk neutral probability measure
- choose number of financial instruments for which price is known
- build model than can project cashflows from these instruments in a range of scenarios
- parameters are chosen such that average PV of cashflows from modelled simulations is sufficiently close to known market price
Real world calibrations
- typically used for projecting in future e.g. for calculating appropriate level of capital to hold to ensure solvency under extreme adverse future scenarios at a given confidence level
- focus: use assumptions according to realistic ‘long-term’ expectations and which consequently also reflect onbservable real world probabilities/outcomes
- determine model parameters using expectations of future
- assumptions used to project the values of assets/liabilities under each stochastic scenario