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Actuarial Risk Management: CA1 > Test 1: Ch 20, 21 , 29, 30 ,31 > Flashcards

Test 1: Ch 20, 21 , 29, 30 ,31 Flashcards

1
Q

The main factors affecting bond yields - SPROEITI

A 
Short term interest rates
Public sector borrowing: the fiscal deficit
Returns on alternative investments, both domestic and overseas.
Other economic factors (BOP, C, Tax) 
Economic growth
Inflation
The exchange rate
Institutional cashflows
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2
Q

The influences of the level of property in the market -

ODI

A

Occupation market
Development cycles
Investment market

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3
Q

Investors preferences are influenced by- FUME CC

A

Fashion or sentiment altering, sometimes for no real
Uncertainty in political climate
reason at all
Marketing
Investor Education undertaken by the suppliers of a particular asset class
Change in their liabilities
Change in the regulatory or tax regimes

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4
Q

Requirements of a good model- VIVA RAPER DAD

A

Valid – relevant to purpose and economic principles assumed
Implement in various cases
Verify reasonableness of outputs independently
Appropriate inputs and outputs - data and parameters

Rigorous – Realistic under several circumstances
Avoid over complexity - costly, non-flexible
Parameters - allow for significant features in business and economic principles and assets and liabilities
Easily communicate and appreciate workings and results
Reflects risk profile of business modelled

Documented – Key assumptions stated
Adjustable Develop-able, Refine-able and Testable
Dynamic – Interaction between parameters and relevant variables that affect cashflows

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5
Q

Deterministic model development

SCCACCARD

A
  1. Specify purpose
  2. Collect, sort and modify data
  3. Choose the form of the model, identifying parameters and variables
  4. Ascribe the parameter values
  5. Construct the model based on the expected cashflows
  6. Check the goodness of fit is acceptable
  7. Attempt to fit a different model if the model does not fit well
  8. Run model using estimates of the values of future variables
  9. Do sensitivity testing on the parameter values
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6
Q

Stochastic model development

SCCSACCRP

A
  1. Specify the purpose of the investigation
  2. Collect group and modify data
  3. Choose a sensible density function for each of the variables to be modelled
  4. Specify correlation between variables
  5. Ascribe values to the variables not being modelled
  6. Construct a model based on the expected cashflows
  7. Check the goodness of fit
  8. Run the model many times each time using a different sample
  9. Produce a summary of the results that shows the distribution of modelled results
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7
Q

Considerations of modelling options depend on - C U FLIP

A 
Costs
Usage frequency of model
Flexibility desired
Level of accuracy required
In house level of expertise
Purpose of the model
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8
Q

Deterministic vs. Stochastic models differences CUBE PO

A 
Capital intensivity
Understabability
Build and run ease
Economic scenario tested
Parameters
Output
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9
Q

Factors affecting the level of the equity market - ERICA IE POTaR

A 
Equity Risk premium - Investor preception 
Real interest rates 
Inflation 
Currency 
Alternative investments

Institutional cashflows
Economic growth

Political climate
Overseas equity markets
Taxation
Regulation

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10
Q

Sources of models -NEC

A

New model built
Existing model modified and used
Commercial model purchased

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11
Q

Merits of Deterministic model LECE COS

A

Less Capital intensive
Explainable
Clarity on scenario tested
Easy and quick to design

Carefully consider which scenarios will be tested
Only point estimates produced
Some scenarios may be missed

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12
Q

Merits of a stochastic model WAQA SLICAH:

A

Wider range of scenarios tested
Assess financial guarantees/assumptions tested
Quality result
Allows for uncertainty

Spurious accuracy
Longer run time
Interpretation and communication difficulty
Complex programming/ Costly
Additional capital intensity
Higher risk of model and parameter error due to complex nature

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13
Q

Uses of a model PROF P:

A 
Pricing - setting premium of charging structure
Risk management 
Options and guarantees valuation
Set Financing strategies 
Individual Provisions valuation
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14
Q

Using models for pricing consider BAD CoMP DiSCo

A

Business strategy
Assess capital requirements
Discount rate used

Competitiveness
Model point used
Profit requirements

Distribution channel
Contract design
Size of market

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15
Q

Sources of data TRAINERS C

A 
Tables eg actuarial mortality tables
Reinsurers 
Abroad (data from overseas contracts)
Industry data
National statistics
Experience investigations on the existing contract
Regulatory reports and company accounts
Similar contracts
Current economic conditions
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16
Q

Causes of poor data RUS RAD

EDIT—————-

A

Recording or verification
Underwriting Policy / Rating factors
System design

Insufficient:

  • Relevance
  • Amount
  • Detail
17
Q

Uses of data (Company departments)- PREMISES FAAr

A
  • Premium rating, product costing, determining contributions
  • Risk management
  • Experience stats
  • Marketing
  • Investment
  • Statutory returns
  • Experience analytics
  • Setting provisions
  • Financial control, management info
  • Admin
  • Accounting
18
Q

Factors affecting assumptions: DIE FULCN

When setting assumptions it is important to consider:

A

Demographic factors
Investment strategy
Economic factors

Financial significance of assumptions
Use of model/ Use to which the assumptions would be put
Legislation and regulation
Consistency of assumptions
Expert guidance should be allowed for
Needs of client and company in terms or risk appetite

19
Q

Factors that increase the risk of product design:

GLOC U

A 
Guarantees and options
Lack of historical data 
Overheads
Complexity
Untested market
20
Q

For past data consider how to deal with:

CRC CHEAT PRUDT

A

Changes in recording of data
Random fluctuations
Changes in experience over time - Ideas!

Changes in balance of homogenous groups (Business Mix) 
Heterogeneity in groups
Errors in Data
Abnormal fluctuations
Standard Tables:
  -Relevance and adjustments
  -National vs. Industry data 
Changes in:
Product design
Rates changed (Mortality, withdrawal, investment)
Underwriting practices 
Distribution channels 
Target market
21
Q

When considering accuracy and prudence of assumptions BIPACS O

A

Best estimate vs Including uncertainty(Prudence) (overstatement)
Implicit assumptions:
-consistency of population distribution
-closed or open to new business
Purpose of the valuation
Accuracy of assumptions vs. Accuracy of outcome
Correlation between assumptions
Significance of assumption error

Once off cashflows

22
Q

Assumptions made form historic data BIDS

EDIT——–

A

Benefit growth - past inflation
Investment returns
Demographic data
Salary levels and growth

23
Q

Assumptions made form current data REFS

EDIT———-

A 
Regulation and legislation 
Economic factors – central bank policy
Future inflation – index linked bonds interest rates
Scheme sponsor provides info on: 
Future salary increase 
Withdrawals
24
Q

Quality and quantity of data influenced by SEC:

EDIT———–

A

Size of business
Experience in line of business
Characteristics of the product

25
Q

Ways to improve data quality TeCSReFS P

A 
Technological resources to collect relevant real-time data 
Checks on data collected 
Systems to integrate newly collected and historical data
Retain Record new data Robust 
Form improvement:  
-Proposal form  RECTUREQ
-Claim form 
Secure and sufficient storage 
Processing capacity
26
Q

Ways to improve the claim and proposal form RECTURE Q

A 
Relevant + reliable info collected 
Ease of analysis 
Cross-checking with claims form when processing claims
Tick Boxes 
Underwriting info - LI
Rating Factors - GI 
Ease of recording into the system
Quantitative as far as possible
27
Q

Concerns with summarised data MiRC/ RelCanS

A

Miss significant differences between benefits
Reliability reduced
Cannot value client specific options or guarantees

28
Q

Merits of industry wide data CA FLOQ HP

A

Comparison
Assess possible new business opportunities

Formats can differ
Less flexible
Out of date
Quality will vary  
Heterogenity 
Not for provisions
29
Q

Heterogeneity in data industry-wide data caused by difference in
PReCReS SNaP

A 
Practices – Underwriting, i.e. 
Reinsurance
Coding of risk factors
Regulation 
Sales methods

Socio-economic
Nature of data sections
Policies sold

30
Q

Assertions to assess in data TACA /

CATA OPI SpotS

A

That A or L exists or is held
Appropriate value of A and L
Complete data – no unrecorded A or L
Accounting period of events correlate

Opening + movements = closing
Policy data vs Accounting data
Investment data vs Accounting data
Spot Checks for unusual values
Summary statistics
31
Q

Demographic assumptions:

Morty Will Never FallS

A 
Mortality
Withdrawal rates
New business volumes
Future contributions
Salary/Promotional increases
32
Q

Ways to check assertions: Checks to perform SCARFACE MVR
/
Ways to check assertions Reconcile, Consistency, DRaM

A

Spot checks Random check for weird values
Contribution(salary related)/benefit(in payment) consistent with accounts
Asset income consistent with accounts
Reconciliation of asset value between company and third party
Full deed audit on certain assets (like property)
Average sum assured and premium compared to previous investigation
Consistency of shareholdings (start and end of period)
Equate/reconcile member numbers and changes in them
Movement data against appropriate records( accounting data)
Validation of dates
Reconciliation of premiums and benefit amounts and changes in them

Reconcile NBA
Number of member and policies
Benefit amounts and premiums and possible changes
Assets value correctly displayed - market value, and admissible assets

Consistency SACIS
Changes in variable benefits and contributions
SA and Premium in – asset class and previous investigations
Investment income implied vs. accounting data
Shareholding

DRaM
Deed audit
Random spot check
Movement vs accounting data

33
Q

Examples of financially significant economic assumptions

5CIET - Also for monitoring!

A 
Claim amounts
Claim numbers
Investment returns
Expenses such as legal fees in court claims
Commission
Claims inflation
Claims expense inflation
Tail length of business
34
Q

Freehold properties are considered to be more attractive than leasehold properties because of:
BeDGGN

A

Being easier to borrow against
Greater marketability
Greater flexibility
Not suffering from eventual loss off capital
Developing possibilities of the property exists

35
Q

Contract Features Captured in a model point BAGTSAD

A 
Basis 
Age 
Gender 
Term of policy 
Sum Assured
Duration in force / Demographics
36
Q

Risks in data usage FHAG V

A 
Form of data inappropriate 
Historic data not relevant 
Accuracy of data is poor 
Grouping of data may be inefficient
Volume may lack (Especially to model extreme cases)
37
Q

Reasons for historic value to lose its relevance TRUAH

A 
Trend changes 
Random fluctuations 
Not Up to date (with changes in external environment) 
Abnormal past events 
Homogeneous groupings may change
38
Q

The requirements of POPI (APPFIOSD)

A 
Accountability
Processing limitation 
Purpose specification 
Further processing limitation 
Information quality 
Openness 
Security 
Data subject participation
39
Q

Setting the risk discount rate when modelling RASS

A 
Required return of the company 
Average risk faced by business put into a single rate (simplicity) 
Statistical risk (Risk causing variation in cashflows and other model risks) 
Stochastic Discount rate

Actuarial Risk Management: CA1 (13 decks)

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