Ch 21: Assumptions 1 Flashcards
Summary card
- Setting assumptions
- Mortality
- Investment return
- Expenses & expenses inflation
- Persistency
- Product risk
- Risk discount rate
- Profit criteria
- Consistency between assumptions
Background on assumptions
What is the key reason assumptions are used for? (1)
What key risk does setting assumptions introduce? (1)
What kind of risks can be somewhat mitigated by appropriate matching of assets? (3)
- Assumptions used by insurers for variety of reasons, mostly assessng eventual cost of liabilities
- Setting assumptions => parameter risk: want to reduce this
- Not easy finding matched assets protecting from actual experience different to expected, can sometimes reduce following risks from investment matching:
- Investment risk: relates return required meet current liabs for future payouts
- Inflation risk: relates increase in inflation-linked liabs + liabs behaving approximately in line with inflation (eg expenses)
- Marketing risk: ability to satisfy PHs in relation to any investment-linked/discretionary benefits.
Methodology for setting assumptions
- Investigate past experience; make past best estimate parameters; appropriate in context of historical conditions/then-circumstances
- Consider future conditions (including commercial and economic environment ) during period for which assumptions will be used
- Determine future best estimates assumptions, given expected future conditions
- Extent of (a) relying past data vs (b) allowing for other factors, depends on data credibility/relevance + parameter’s predictability
- Adjust best estimates with margin. Size of margin depends on:
- purpose for which model is required
- degree of risk associated with parameter
Best estimate mortality:
Base rates
- Mort rates change over time and have 2 parts
- Base mortality:
- the main demographic assumption for pricing => reflect future experience of lives taking out contracts
- Adj rate from mort table
- Restricted by regulation
- Exp future experience depends on:
- Target mkt and Dbn channel
- Level of underwriting and controls
- Expected changes in experience since last investigation
Best estimate mortality: base rates derivation
Adv of adj std table values 3
What is the adj based on 3
- Base rates usually uses adjusted rates from standard table
- cheaper than own investigation
- less errors eg inappropriate graduation
- Larger samples sizes, esp at extreme ages
- Adj based on own data (similar class of business)
- data must be for relevant period and credible
- conflict bet large # data and creation of heterogeneous groups
- analysis divide data into relevant credible homogenous groups
- data must be for relevant period and credible
Best estimate mortality: base mortality derivation
What data sources can be used to det base rates
- Data sources which can be used for adjustments
- 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
TRAINERS
Best estimate mortality: base mortality derivation data sources
State advantages and disadvantages of these sources.
Industry wide investigations (3)
Population mortality statistics (2)
Reinsurer data (5)
- Industry wide investigations
- useful for contracts where no standard table exists
- good for showing trends, since trends in own data might be due to statistical variation
- not 100% suitable since not based on insurer’s particular PHs
- Population mortality statistics
- useful for showing trends if re-examined at regular intervals in past
- not 100% suitable since not based on insurer’s particular PHs
- Reinsurer data
- Adv
- access to mortality experience of many direct writers
- may be most relevant data available
- Disadv
- relates to large number of different companies
- may have little/no suitable data
- comes with a cost: cost of reinsurance
- Adv
QUERIED/ QRCFD
Best estimate mortality:
Mortality trends
- The mortality trend relates to how the rate of mortality changes over time.
- Estimating future mortality improvements is particular important:
- for policies with longevity risk e.g. annuities
- when rates are guaranteed rather than reviewable
- CHANGE DOG => factors that affect mortality
Different approaches used to project mortality trends (3)
- Different approaches to project mortality trends over time:
-
expectations: uses expert opinion to specify range of future scenarios
- can implicity include all relevant knowledge, incuding quantitative factors
- subjective and subject to bias
-
extrapolation of historical trends
- project historical mortality trends into the future
- some subjectvity: choice of period to determine trends
-
explanatory projection techniques,
- modelling bio-medical processes that cause death
- only effective to extent process understood and mathematically model-able
-
expectations: uses expert opinion to specify range of future scenarios
Best estimate mortality: mortality trends
State how each of the following might be taken into account when making projections of future mortality:
- cohort effect
- the combined effects of multiple factors
- random effects
- 4 main cause: circ diseases, cancer, respiratory, infections
- Cohort effect
- each year of birth cohort is modelled seperately,
- allowing for specific mortality improvemebt rates by cohort
- take into account smoking levels, alcohol consumption etc
- Multi-factor effects
- Use multi-factor predictive modelling techniques (eg. generalised linear models),
- Combine internal data with external factors affecting mortality,
- Allowing for any correlations and interactions between them.
- Random effects
- Use stochastic modelling (e.g. Lee-Carter or P-spline method)
- Hard to calibrate as need understanding of drivers of mort
Morbidity rates: Disability incidence and duration for IP
Describe how these rates may be determined (4)
What factors might affect the transition intensities (5)
Describe how rates are used (2)
Describe issues surrounding estimating these rates (3)
How might we control parameter uncertainty for these rates? (3)
- Benefits for IP can be modelled using a multi-state approach
- needs transition intensities (claim inception, recoveries, death)
- calculated for homogenous groups
- duration: revovery may differ vastly by duration in force
- disability type: recovery may dif vastly by disability type
- may seperate second/subsequent incidences: as more likely to claim in future
- Intensities influenced by
- PH characteristics: identified at underwriting
- prod design features: replace ratio/rehab benefits
- economic morale: low => more likely claim
- government welfare provision
- tax: on premiums (discourage sales), relief on prems (enoucourage sales), way insurer is taxed, tax rates involved changing over time
- Intensities used to calc transitions probabilities
- then construct projected numbers/proportions in each state at future ages.
- can be used to calc claim inception rates/disability annuity values
- Issues surrounding estimating rates
- Data limitations is the main issue
- Published insurance incidence data has limited credibility
- Worldwide stats may not be relevant
- Controlling parameter uncertainty
- Assuming larger risk margins
- Issuing products with reviewable premiums/charges
- Reinsurance
Morbidity rates: Incidence rate for CI
What factors influence claim distribution rates for CI (4)
What kind of factors complicate modelling/setting of assumptions (2)
- May be necessary to estimate significant number of distributions (40+) if each condition modelled seperately, 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)
- influence new and existing business seperately
- new business, can adjust premiums accordingly
- existing business, can only adjust in force prems if revieawable
- Factors which complicate modelling/setting assumptions
- using a disease-based and/or treatment-based claims definiton
- These will need to be modelled separately=> est of rates => param risk
- guaranteed and reviewable alternatives
- lack of data limited past exp build up
- product not around long enough
- using a disease-based and/or treatment-based claims definiton
Morbidity rates: Incidence and amount for LTCI
What key assumptions do we need to estimate for LTCI? (2)
Freq 2
Amt 3
- Estimate distribution of
- Claim frequency
- 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
- Data limited, no stats available
- Claim amount
- No control if indemnity unless cap
- Costs: changing med care may => higher costs e.g. more expensive procedeurs
- inflation: big problem if benefits are indemnity based
- demand (for LTC) vs supply, usually demand is greater, leading to inflation heaveir than economic inflation
- Claim frequency
Morbidity assumptions
Expand briefly on how size of benefit may impact morbidity assumptions (4)
- Impact of benefit size on assumptions
- For IP, CI, and most LTCI, benefit amount fixed, so no assumption needed
- But may be correlation between incidence rates and benefit size
- Large policies, may want to alter assumptions,
- to reflect better claims experience as PH rich
- stricter level of underwriting
- Use benefit amt as rating factor
Investment return:
List 4 factors that affect the value assigned to the investment return assumption when pricing a life insurance contract.
(1,3)
(2,1)
(4,3)
(3,3)
Significance of assumption on profitability depends on:
1. Level of reserves
* Larger reserves => + prop of inv inc in CF’s => + sens inv inc
2. Extent of investment guarantees given under the contract.
* This will affect asset mix
* more onerous the guarantee => more cautious assets selected => cautious investment return assumption
3. Intended investment mix for contract, current return and, where appropriate, likely future returns on this mix
* Analyze past and current yields
* Predict returns from future asset mix
* Asset mix derived from level of free assets to cover NUB, + => mismatch
4. Extent of any reinvestment risk, and extent to which reinvestment risk can be reduced by suitable asset choice
* overall best estimate investment assump will reflect expected balance btwn expected future and current investment yields
* if real cashfow positive in future => requires purchase of future assets. more this happens=>investment assump reflect expected future experience
* mismatching may mean need to buy/sell assets, so future investment yields still important