Test 1: Ch 20, 21 , 29, 30 ,31 Flashcards by jp swanepoel

The main factors affecting bond yields - SPROEITI

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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The influences of the level of property in the market -

ODI

Occupation market
Development cycles
Investment market

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Investors preferences are influenced by- FUME CC

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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Requirements of a good model- VIVA RAPER DAD

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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Deterministic model development

SCCACCARD

Specify purpose
Collect, sort and modify data
Choose the form of the model, identifying parameters and variables
Ascribe the parameter values
Construct the model based on the expected cashflows
Check the goodness of fit is acceptable
Attempt to fit a different model if the model does not fit well
Run model using estimates of the values of future variables
Do sensitivity testing on the parameter values

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Stochastic model development

SCCSACCRP

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

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Considerations of modelling options depend on - C U FLIP

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

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Deterministic vs. Stochastic models differences CUBE PO

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

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

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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Sources of models -NEC

New model built
Existing model modified and used
Commercial model purchased

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Merits of Deterministic model LECE COS

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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Merits of a stochastic model WAQA SLICAH:

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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Uses of a model PROF P:

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

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Using models for pricing consider BAD CoMP DiSCo

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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Sources of data TRAINERS C

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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Causes of poor data RUS RAD

EDIT—————-

Recording or verification
Underwriting Policy / Rating factors
System design

Insufficient:

Relevance
Amount
Detail

Uses of data (Company departments)- PREMISES FAAr

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

Factors affecting assumptions: DIE FULCN

When setting assumptions it is important to consider:

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

Factors that increase the risk of product design:

GLOC U

Guarantees and options
Lack of historical data 
Overheads
Complexity
Untested market

For past data consider how to deal with:

CRC CHEAT PRUDT

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

When considering accuracy and prudence of assumptions BIPACS O

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

Assumptions made form historic data BIDS

EDIT——–

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

Assumptions made form current data REFS

EDIT———-

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

Quality and quantity of data influenced by SEC:

EDIT———–

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

Ways to improve data quality TeCSReFS P

``` 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 ```

Ways to improve the claim and proposal form RECTURE Q

``` 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 ```

Concerns with summarised data MiRC/ RelCanS

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

Merits of industry wide data CA FLOQ HP

Comparison Assess possible new business opportunities ``` Formats can differ Less flexible Out of date Quality will vary Heterogenity Not for provisions ```

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

``` Practices – Underwriting, i.e. Reinsurance Coding of risk factors Regulation Sales methods ``` Socio-economic Nature of data sections Policies sold

Assertions to assess in data TACA / | CATA OPI SpotS

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 ```

Demographic assumptions: | Morty Will Never FallS

``` Mortality Withdrawal rates New business volumes Future contributions Salary/Promotional increases ```

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

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

Examples of financially significant economic assumptions | 5CIET - Also for monitoring!

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

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

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

Contract Features Captured in a model point BAGTSAD

``` Basis Age Gender Term of policy Sum Assured Duration in force / Demographics ```

Risks in data usage FHAG V

``` 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) ```

Reasons for historic value to lose its relevance TRUAH

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

The requirements of POPI (APPFIOSD)

``` Accountability Processing limitation Purpose specification Further processing limitation Information quality Openness Security Data subject participation ```

Setting the risk discount rate when modelling RASS

``` 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 ```