Chapter 21 - Setting assumptions Flashcards
What key risk does setting assumptions introduce? (1)
Setting assumptions may => parameter risk: want to reduce this
What is the basic methodology for setting assumptions
- Investigate historical experience and make best estimates of the parameters
- Consider what conditions (including commercial and economic environment) will be like in the future period
- Determine best estimates of your assumptions will be, given the expected future conditions
- The extent you woukd rely on experience data and the extent to which for other factors
- Best estimates may need to adjusted in order to include a margin for prudence
Best estimate mortality:
List 2 separate parts of mortality to be considered when setting best estimate mortality assumptions (2)
- Base mortality: initial rate of mortality, the main demographic assumption for pricing/evaluating life insurance contracts
- Mortality trend: how mortality rate changes over time
Best estimate mortality: base mortality derivation
What factors will influence future mortality experience (3)
Rates should reflect expected future experience of lives to be insured by contract being priced, in terms of
- target market: affected by distribution channel
- underwriting controls:
- expected change in experience since last historical investigation, to point assumptions will apply on average (usually 10 - 15 yrs)
Why are base mortality rates adjusted from standard tables
- save resources
- protect against errors eg, innapropriate graduation
- may be insufficient data from own data
Best estimate mortality: base mortality derivation
What data sources can be used to adjust base mortality rates (6)
- own past experience with that product,
- own past experience with similar product(s),
- reinsurance data
- industry data i.e. standard tables
- international data
- national statistics
State 2 circumstances where the estimation of future mortality improvements is particularly important (2)
- for policies with longevity risk e.g. annuities
- when rates are guaranteed rather than review-able
Annuities could be considered as non-reviewable premium
What are the 3 approaches to determining future rates of mortality improvement and explain them
- expectation approach - involves expert opinion and subjective judgement to specify range of future scenarios
- extrapolation approach - projecting mortality trends in mortality into the future which also required some element of subjective judgement
- explanatory approach - projections attempt to model trends in mortality rates from a bio-medical perspective. Only effective to extent that the processes causing death are understood and modelled
Morbidity assumptions
What factors/kind of rates should we consider when setting morbidity assumptions (4)
Key factors/assumptions
(1) Disability incidence rate and duration for IP
(2) Incidence rate for CI
(3) Incidence and amount for LTCI
(4) Impact of benefit size on assumptions
How are rates for disability incedence and duration for IP derived
- Benefits for IP can be modelled using a multi-state movement hierachy
- estimate transition intensities - which includes claim inception, recoveries and death
- these intensities will need to calculated for homogenous groups, eg by duration of claim of type of disability
What factors affect transition intensities for IP
- PH characteristics
- product design features, eg rehab benefits
- economic morale
- government provision of welfare
- tax
Morbidity rates: Incidence rate for CI
What factors influence claim distribution rates for CI (4)
- May be necessary to estimate significant number of distributions (40+) if each condition modelled separately, plus allowance for future trends
Other influences claim distribution (other than trends), include
- advancement in medical science (cures=> more windfalls)
- diagnosing conditions earlier (more claims)
- simple/more readily available operations (more claims)
Morbidity rates: Incidence rate for CI
What kind of factors complicate modelling/setting of assumptions (2)
- may need to separately model claims definitions which are disease-based and/or treatment-based (eg coronary artery bypass, major organ transplant, heart valve replace)
- guaranteed and review-able alternatives
Morbidity rates: Incidence and amount for LTCI
What key assumptions do we need to estimate for LTCI? (2)
Estimate distribution of
* claim frequency
* claim amount (if funding for care)
Morbidity rates: Incidence and amount for LTCI
What are important factors for LTCI contract assumptions? (5)
Medical advancements
- Transition rates: improved health may reduce inception rates and rates for people moving to higher ben-levels
- Mortality rate: improved health=> people needing benefits for longer
- Costs: changing med care may => higher costs e.g. more expensive procedeurs
Economic factors
- inflation: big problem if benefits are indemnity based
- demand (for LTC) vs supply, usually demand is greater, leading to inflation heavier than economic inflation
Morbidity assumptions
Expand briefly on how size of benefit may impact morbidity assumptions (4)
- For IP, CI, and most LTCI, benefit amount fixed, so no assumption needed for this
- But may be correlation between incidence rates and benefit size
- Only for very large policies, may insurer want to alter assumptions, to relfect better claims experience from
+PH belonging to higher socio-economic class
+stricter level of underwriting
Investment return:
List 4 factors that affect the value assigned to the investment return assumption when pricing a life insurance contract.
- Significance of assumption. This depends on:
+level of reserves (larger reserves => more important)
+extent of investment guarantees - Extent of investment guarantees given under the contract. This will affect asset mix
more onerous the gaurantee => more cautious assets selected => reflected cautious investment return assumption - Extent of any reinvestment risk, and extent to which reinvestment risk can be reduced by suitable asset choice: the less important reinvestment risk the less account needs to be taken of future investment yields
- Intended asset mix for the contract and current and likely future return
Investment return: Market consistency
- For a contract that is priced using a market-consistent approach, how do we set investment return assumption?
- Comment on this process specifically for stochastic modelling
- For market consistent approach
expected investment return should be set as the risk-free rate irrespective of the actual underlying assets held
this is true for both stochastic and deterministic models - If stochastic modelling is used
need additional assumptions for investment return volatility and correlation assumptions
which are dependent on actual underlying assets
Expense inflation:
What will primarily affect the inflation assumption, and why? (2)
What 2 ‘periods’ should be considered when setting the expense inflation assumption? (2)
List 5 factors that will considered when setting the expense inflation assumption for pricing (5)
- Key impact on inflation will likely be earnings inflation, as insurer’s expenses are mostly staff related
- Consider
+inflation between setting assumption, and point from which new policies will be sold
+inflation during term of policy - 5 Factors affecting expense inflation
+Current rates of inflation, both for prices and earnings
+Expected future rates of inflation
+Difference between fixed-interest government bond yields and index-linked government bond yields (may be skewed by any risk +premium implicit in price of government fixed interest bonds)
+Recent actual experience of life insurance company or industry
+Investment assumption being used (be consistent)
Persistency:
What changes to benefits might lead to increased withdrawals? (6)
How might distribution channels impact withdrawals? (5)
Non-linked
- Increase in discontinuance terms
- Decrease in bonuse rates
Unit-linked
* Reduced fund performance
* Increased charges
* Removal/variation of guarantees/options**
Distribution channel
- Who initiates the sale: lower withdrawal if client initiates
- Different sales practice: client pressurised for sale => higher rates
- Sales without gathering proper info: mis-selling
- Financial sophistication: varies by channel=> impacts rates
- Target markets: affected by dist channel, hence PH’s affluence + level of economic wealth
How might we allow for risk in the use of parameters in pricing? (3)
What key factors influence the margins to use? (3)
- through the risk element of the risk discount rate - only applicable to cashflow model
- using stochastic approach - only applicable to cashflow model
+using best estimate for non-stochastic assumptins, using a risk free rate, modelling one/more assumption stochastically - assessing margins to apply to expected values and using a risk free rate to discount
+applicable to either cashflow/formula model
+formula model needs judgement, as doesn’t help actuary determine extent of risk
Use of margins depends on
* degree of risk associated with each parameter used
* financial significance of the risk from each parameter
* purpose for assumptions
+pricing => competitiveness (but also prevent losses)
Profit requirements:
Deciding on a risk discount rate
What methods can be used to determined RDR? (2)
To determine RDR, can use
* CAPM
* Statistical methods
Profit requirements:
Deciding on a risk discount rate using statistical methods
Why can’t we simply use CAPM? (2)
What might affect the riskiness of products/projects undertaken by insurer? (6)
Can’t simply use CAPM as
* assumptions may not hold
- not all projects company undertakes are equally risky (some products have more innovative features, eg)
6 things that might affect riskiness of products/project life company undertakes
* Lack of historical data
* High guarantees
* Policyholder options
* Overhead costs
* Complexity of design
* Untested market
Profit requirements:
Deciding on a risk discount rate using statistical methods
How might we use a statistical approach to asses the insurer’s risks and allow for them in the RDR? (4)
Can assess these risks (and allow for them) by
- analytically, by considering variances of individual parameter values used i.e VaR[Return]
- sensitivity analysis
- using stochastic models
- comparison with any available market data
