1. 2007 Annual Meeting ● Assemblée annuelle 2007 Vancouver
Canadian
Institute of
Actuaries
L’Institut canadien
des
actuaires
2007 Annual Meeting ● Assemblée annuelle 2007
Vancouver

2. IP-41 Stochastic Modeling for Insurance Products
Assemblée annuelle 2007
2007 Annual Meeting
IP-41 Stochastic Modeling for Insurance Products

3. That which is static and repetitive is boring
That which is static and repetitive is boring. That which is dynamic and random is confusing. In between lies art.
John A. Locke
Assemblée annuelle 2007
2007 Annual Meeting

4. What is Stochastic Modeling?
A process whereby different scenarios are generated by a random process and used in a model to approximate real world results
Assemblée annuelle 2007
2007 Annual Meeting

5. What is Stochastic Modeling?
A process whereby different scenarios are generated by a random process and used in a model to approximate real world results
Deterministic scenario testing on steroids!!!
Assemblée annuelle 2007
2007 Annual Meeting

6. Why Stochastic Modeling?
It enables the user to see model results under a wide variety of scenarios intended to reflect real life
It shows the distribution of possible outcomes including extreme results.
Analysis of individual scenarios can provide insights into the interaction of the product with real world processes.
It allows analysis into the impact of risk mitigation and diversification strategies.
Assemblée annuelle 2007
2007 Annual Meeting

7. Why Stochastic Modeling?
Financial markets are inherently volatile and do not move in a straight line.
True cost of some product design features can only be determined using stochastic models
Insured’s don’t always lapse and die when they are supposed to.
Assemblée annuelle 2007
2007 Annual Meeting

8. When to Use Stochastic Modeling?
Stochastic modeling is a powerful tool to use when the random variable
Is inherently volatile,and
Has a large range of possible outcomes,
Assemblée annuelle 2007
2007 Annual Meeting

9. When to Use Stochastic Modeling?
Stochastic modeling is a powerful tool to use when the random variable
Is inherently volatile, and
Has a large range of possible outcomes,
But
The random variable must have a material impact on the model results to be worth the effort
Assemblée annuelle 2007
2007 Annual Meeting

10. When to Use Stochastic Modeling?
Stochastic modeling is a powerful tool to use when the random variable
Is inherently volatile, and
Has a large range of possible outcomes,
But
The random variable must have a material impact on the model results to be worth the effort
And
A simpler closed form solution isn’t available
Assemblée annuelle 2007
2007 Annual Meeting

11. Common Uses of Stochastic Modeling in Insurance
Pricing of Financial Instruments
Extensive use in capital markets/seg funds
Limited use in life insurance products
Determination of cost of interest rate guarantees for deterministic pricing
Aggregate Stop Loss Reinsurance
Assemblée annuelle 2007
2007 Annual Meeting

12. Common Uses of Stochastic Modeling in Insurance
CALM Valuation
CLIFR to provide guidance on interest rate calibration in 2007
Economic Capital
Extended beyond economic parameters to include mortality, morbidity and lapse
MCCSR is moving to a economic capital approach for many risks
Assemblée annuelle 2007
2007 Annual Meeting

13. Possible Uses of Stochastic Modeling in Insurance
Impact of Investment Strategies
Equities versus Fixed Income
Cost of Policyholder Guarantees
Long Term Guaranteed Premiums
Interest Rate Floors
Retention Limits
Assemblée annuelle 2007
2007 Annual Meeting

14. Possible Uses of Stochastic Modeling in Insurance
Policyholder Distribution
Demographics
UL Funding Patterns
Policyholder Behaviour
Lapses,
Deaths,
Premium Persistency
Assemblée annuelle 2007
2007 Annual Meeting

15. Challenges Significant Overhead Initial set up can be extensive
Model Research
Model Parameterization
Design
Run times can be long
Don’t want to have to redo
Volumes of Data
Assemblée annuelle 2007
2007 Annual Meeting

16. Challenges Appropriateness of assumptions
Is history a good predictor of future?
TSX returned 9.86% from
Inflation outlook lower than historical average
Dividend yields have dropped
Does answer change depending on projection period?
Is historical frequency of the high and low interest rates correct?
Have macro-economic factors changed?
What time frame is appropriate?
Assemblée annuelle 2007
2007 Annual Meeting

17. Challenges Appropriateness of model
Are scenarios reasonable in relation to history?
Do they produce a distribution of results similar to history?
Do they produce patterns similar to history?
Assemblée annuelle 2007
2007 Annual Meeting

18. Challenges Presentation of Results What information is important?
Mean or Median of results?
Distribution of results?
Percentile or CTE?
Periodic volatility?
Specific scenario results?
Assemblée annuelle 2007
2007 Annual Meeting

19. Conclusion
Stochastic modeling is an activity involving significant investment in education, resources and computer time
The results are only as good as the inputs used
“Art” may be involved
Benefits can be significant
Assemblée annuelle 2007
2007 Annual Meeting