Ch 18: Models 1 Flashcards

1
Q

State the prime objective in building a life insurance company model (3)

A
  1. Enable actuary to give appropriate advice in company…
  2. …so that it can be run in a financially sound way
  3. Models used to provide checks and controls on its business
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2
Q

State the requirements of a good model (14)

A
  1. Valid for purpose
    • deterministic vs stochastic
    • Approx and assumpt must be realistic
  2. Rigorous
    • realistic results under wide range of circumstances
  3. Well documented
    • audit trail, key assumptions/approximation
  4. Reflect risks being modelled
  5. Components used allow for material aspects being modelled
    • structural components:
    • parameters: only include param if results differs for different values of the parameter
  6. Parameter values appropriate
    • for particular business
    • general environment, account for special features of company/economic environ
  7. Sensible joing behaviour of variables eg:
    1. higher expense inflation => higher claims inflation
    2. higher claims rates => higher reinsurance recoveries
    3. higher inflation => higher (nominal) bond yields, equity returns?
  8. Easy to
    1. Understand/appreciate model
    2. Communicate model
  9. Output reasonable able to independent verify reaonableness
    1. Reconcile with supervisory valuation
    2. Reconcile with results from last run
    3. Ratio checks on future results
    4. Back of the envelope model
  10. Output communicable
    1. to those who advice will be given to
    2. mentioning underlying method, critical assumptions
  11. Results displayed clearly
  12. Not overly complex to
    1. understand
    2. explain/communicate
    3. expensive to run
  13. Able to develop/refine over time
  14. Dynamic: assets and liabilities
    1. assumptions used to model assets/liabitlies must be consistent
    2. interactions between assets/liabilities modelled

SCARCER FILES

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3
Q

What are model points? (3)

A
  • Policy that represents most important characteristics of a homogeneous group of policies
  • Polices are grouped so eacch one is expected to produce similar results
  • Single MP is run and result is caled up for all polices in group
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4
Q

Model components (4)
Parameter values (3)

A
  • Model must allow for all features of business being modelled
  • Real world features must be replicated
  • Structural: follow from type of model
  • Parameters: must be included if changes results based on its value. not too complex
  • Values should be appro to business being modelled and consider special features of the insurer.
  • Basis: full set of asusmptions for param values
  • Sensible joint behavoiur bet variables is NB
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5
Q

Communication and verificiation (4)
Complexity vs time and cost (5)

A
  • Output must be indep verifiable
  • Recon results w/prev valuation
  • Ratio checks on projections eg. reserve vs prem inc
  • Simple model for reasonableness check for magnitude errors.
  • Model should not be overly complex where results diff to interpret
  • Runtime and expenses also considered
  • Shortcomings clearly stated
  • Refinable/ able to be developed
  • Actuarial judgement for choice of model
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6
Q

We’ve already covered the basic requirements of a good model.

List the general features of a life insurance company model specifically (5)

A
  1. Model may be used to model different types of business
  2. Model should project all cashflows that may arise
  3. Allow for interactions/corellations between variables (dynamic links; joint sensible behaviour)
  4. Guarantees/options should be propertly allowed for; stochastic model best for this
  5. Projection frequency/time period
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7
Q

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

A
  1. Profit testing model
    • projects expected cash and profit flows on policies from date of issue
    • key for pricing/product design
  2. 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
  3. Existing business model
    • cash & profit flow projection from all existing business company has in force at particular time point
    • important for assessing intrinsic value of existing business (EV’s) and testing solvency of company’s existing business
  4. Full model office
    • sum of new and existing business model
    • of fundamental importance in assessing impact of future management decisions on company’s future financial development
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8
Q

Features of a life insurance model:

  • Projecting cashflows/profit (3)
    Real & notional CFs
A

Projecting cashflows/profit

  1. model must allow for all cashflows that may arise
    • depends on contract’s nature, premium, benefit structure, discretionary benefits
  2. 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 (no physical exchange of money)
      • fund establishment of reserves, by contributing money to reserves from cashflow or initially from company’s free assets
      • this increase in reserves is negative from company’s perspective
      • at maturity/claim, reserves will be released to help pay appropriate benefit => decrease in reserves positive
    • (At+1 - Vt+1) - (At - Vt) = (At+1 - At) - (Vt+1 - Vt)
  3. supervisory solvency capital
    • In addition to supervisory reserve, might be minimum supervisory solvency capital requirement to cover.
    • policy cashflow might also need to fund establishment of solvency margin.
    • required solvency margin included in value of reserves
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9
Q

Features of a life insurance model:

  • allowing for interactions (3,2)
A

Cashflows need to allow for any interactions, particularly where assets and liabilities are being modelled together.

  1. Dynamic 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
  2. 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
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10
Q

Features of a life insurance model:

  • allowance for guarantees/options (3)
A
  • 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.
  1. Allow for effect on supervisory reserves
  2. Allow for stochastic models/simulations
    1. …where appropriate, in order to assess impact of financial guarantees (e.g. minimum maturity guarantees)
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11
Q

Features of a life insurance model:

projection frequency and time period

(3)

(5)

A
  • 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.
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12
Q

What is a deterministic model?
State 5 advantages of deterministic models compared to stochastic models (5)

A
  1. Variable are set as fixed. Sens test to det impact param value will have on result

Advantages
2. Easier to explain (particularly to non-technical audience)
2. Easier to interpret/understand
3. Clearer which (economic) scenarios have been tested
4. Easier to design
5. Easier/shorter to run

Disadvantages
1. Cover smaller amt of scenarios
2. Results depend heavily on param value
3. Less accurate that stoch

A311

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13
Q

Give examples of circumstances in which deterministic models might be appropriate (4)

A
  1. If similar results possible as if stochastic projection were used.
    • Possible outcomes form a symmetric distribution/
    • information only required internally. expectation
    • specific scenario being tested within simple cashflow model
  2. Quick, independent test is required to see that the results of a stochastic projection are reasonable
  3. To provide upper and lower bounds
  4. To avoid nested stochastic model
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14
Q

What is a stochastic model?

For stochastic models compared to deterministic models:

  • State 3 advantages
  • State 2 disadvantages
A
  • A stochastic model is one in which we assign probability distributions to one or more unknown parameters.
  • Multiple runs done, give more accurate impact of param

Advantages

  1. Distribution of outcomes (not just single outcome) because can prob distribution to one/more unknown future parameters
  2. Positive liability can be calculated where deterministic approach might otherwise produce zero liability e.g. costing options and guarantees
  3. Interactions explicitly modelled i.e parameters modelled dynamically

Disadvantages

  1. Time and computing constraints
  2. Possible spurous accuracy i.e. results very senstitive to (deterministically chosen) assumed values of parameter(s) involved

A311

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15
Q

Describe 2 approaches to calibrating stochastic models of economic variables

(5)

(4)

A

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
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16
Q

Example of real world vs market consistent

A

Bond price =100
Equity price =100
Market consistent both =100
Real world Equities > bonds