June 29 & 30, 2006 Ÿ Les 29 et 30 juin 2006 Ottawa, Ontario

1. June 29 & 30, 2006 Ÿ Les 29 et 30 juin 2006 Ottawa, Ontario

2. Sub-Committee of CLIFR formed late in 2004 Members of Sub-Committee: Jacques Boudreau, Ty Faulds, Carl Kruglak, Dale Mathews, Christian-Marc Panneton, Michael Promislow, Anne Vincent Mandate review current ways actuarial judgement is brought in to the GAAP financial reporting process and determine what guidance can be provided to ensure compliance with standards and narrow the range of practice Status: Recommendation to PSC

3. Agenda Background Principles for setting assumptions and margins Considerations for Non Scenario Tested Assumptions Modeling Scenario Testing Segregated Fund Reserves

4. Background CALM is prospective by nature In projecting future experience, understand past Consider trends and changing circumstances A crude application of past experience without judgement is rarely appropriate however any application of judgement should be based on sound grounds

5. Background Section 1750.01 of the General Standards states Unless the actuary reports the inconsistency, the assumptions for a calculation for a periodic report should in the aggregate be consistent with those of the prior calculation Volatility is a financial reporting reality In many circumstances, volatility of results is appropriate when the entity has unhedged or imperfectly hedged exposures to risk

6. Principles for setting assumptions and margins: Assumptions and margins are justified on a prospective basis. Maintaining an assumption/margin subject to same level of scrutiny. The change in policy liabilities does not reflect a change in past experience that the actuary has sufficient reason to believe is temporary.

7. Principles for setting assumptions and margins: The change in expected assumption is supported with data that indicate a need for change. The change in the margin for adverse deviation is supported by a change in the level of risk. The change in assumption is not manipulative. methods to determine assumptions are predetermined and are not subject to irregular or inconsistent application over time

8. Considerations for Non Scenario Tested Assumptions: The best estimate assumptions reflect the actuary’s best estimate of how future experience will emerge. based on past experience, industry experience and other factors such as correlations with other parameters in the valuation.

9. Considerations for Non Scenario Tested Assumptions: Reflect emerging trends in experience, but not random fluctuations in recent past experience. This is sometimes accomplished by using the average of the past three to five years’ of experience as the base from which to determine the best estimate assumption.

10. Considerations for Non Scenario Tested Assumptions: (cont’d) Difficult to determine whether changes in past experience are caused by underlying trends, random fluctuations, or cyclical influences. reflect emerging trends when they have been clearly established. For example, a 4% drop in actual unit expenses might result in a 2% drop in the choice of best estimate expense assumption, with the other 2% drop reflected a year later if the unit expenses stay low.

11. Considerations for Non Scenario Tested Assumptions: (cont’d) Difficult to determine whether changes in past experience are caused by underlying trends, random fluctuations, or cyclical influences. Where emerging trends in different assumptions are offsetting then may delay action on both Where going in the same direction then more reason to proceed with a change

12. Considerations for Non Scenario Tested Assumptions: (cont’d) Incorporates guidance from recent fall letters on setting cyclical assumptions Impact of policyholder pass through features ensuring consistency of pass through features with base assumptions recognizing limitations on ability to pass through

13. Considerations for Non Scenario Tested Assumptions: (cont’d) Correlation of other assumptions with scenario tested assumptions interrelationships often difficult to measure look for relevant experience to aid in reflecting policyholder behavior, anti-selection… sensitivity testing often useful in aiding understanding may be appropriate to assume that policyholders may not react quickly or fully even if to their advantage

14. Modeling Modeling constraints may cause current interest rate environment, mismatch position, asset quality and mix to have an unrealistic impact. actual investment policy, and policy constraints often difficult to model when and how to recognize changes in investment approach Model results tested to ensure they are reasonably consistent with observed experience.

15. Modeling Incorporates guidance from recent fall letters on reinvestment strategies Reviews common approaches in modeling asset investment strategy and things to watch out for In all take care to not assume prior knowledge of future projected rates.

16. Scenario Testing - Principles Reflects control over mismatch position, asset mix and asset quality Recognizes the investment policy as a a model constraint Reflects investment practices i.e. if practice is to invest long to pick up yield, model should so recognize Recognizes current position at balance sheet date Liabilities set to be sufficient without being excessive

17. Scenario Testing - Example Illustrates concepts and not to be taken literally Concepts discussed can be difficult to implement Uses duration mismatch as the variable could have used asset mix or asset quality Assumes you know historical average duration mismatch,current mismatch position,target mismatch position and maximum mismatch position Discusses 4 potential alternatives

18. Scenario Testing Example - Alternative 1 Do the testing and set the liability assuming you remain at the current mismatch position. may be consistent with investment practice quite sensitive to actual mismatch position could result in insufficient liabilities if current position is lower than historical average and/or target position could result in excessive liabilities if current position is significantly higher than historical average and/or target position

19. Scenario Testing Example - Alternative 2 Do the testing and set the liability assuming you move to the maximum mismatch allowed. clearly sufficient likely inconsistent with investment practice implicitly applies a margin to the mismatch position less sensitive to actual mismatch position at valuation date

20. Scenario Testing Example - Alternative 2 Do the testing and set the liability assuming you move to the maximum mismatch allowed. likely results in excessive liabilities, particularly if maximum is sufficiently higher than the average historical and thus generally unacceptable variation could be to restate starting position to maximum Not felt to be appropriate as you can’t adjust the past

21. Scenario Testing Example - Alternative 3 Do the testing and set the liability assuming you move to the target mismatch position. likely consistent with investment practice less sensitive to actual mismatch position at valuation date than alternative 1 if target is the same as or slightly higher than the historical average than would seem appropriate

22. Scenario Testing Example - Alternative 3 Do the testing and set the liability assuming you move to the target mismatch position. if target is significantly higher than historical average then may be excessive unless there is a documented plan to take more mismatch risk if target lower than historical average then may not be sufficient in the absence of a plan to move to the target

23. Scenario Testing Example - Alternative 4 Do the testing and set the liability assuming you move to the average historical mismatch position likely consistent with investment practice less sensitive to actual mismatch position at valuation date than alternative 1 if historical average is the same as or slightly higher than the target than would seem appropriate

24. Scenario Testing Example - Alternative 4 Do the testing and set the liability assuming you move to the average historical mismatch position if historical average is significantly higher than the target then may be excessive if there is a clear plan to take less mismatch risk if historical average lower than target then may not be sufficient if there is a clear plan to take more mismatch risk

25. Scenario Testing Examples - Conclusions Liability should be based on mismatch/asset mix/asset quality in existence at the valuation date Reinvestment strategies that assume return to historical average or target positions are generally acceptable generally consistently applied period to period

26. Scenario Testing Examples - Conclusions The period over which these actions are assumed to occur should reflect past experience Reinvestment strategies that assume move to maximum allowed positions may result in excessive liabilities

27. Segregated Fund Reserves Factors common in these products include: single premium nature makes future revenue dependent on future investment return inherent instability of revenue stream given mixes heavily weighted towards common stock death and surrender guarantees heavily dependent on market performance fixed ‘upfront’ nature of acquisition expenses recovered from unstable revenue stream inverse correlation of guarantees with revenue

28. Segregated Fund Reserves: Incorporates guidance from recent fall letters on the selection of the CTE and changes in CTE levels Level of Aggregation Applied encouraged to review the Aggregation and Allocation of Policy Liabilities education note once level chosen it is normally kept consistent period to period

29. Segregated Fund Reserves: Selection of CTE Level Within the CTE60 and CTE80 corridor Parameter uncertainty impact of parameter uncertainty on CTE level should take into account the risk profile of the business generally less for closer to expiry / in the money Model Risk model risk would not normally change period to period

30. Segregated Fund Reserves: Changes in the CTE Level where designed to simply achieve a measure of stability this is not appropriate where recognizing a change in level of risk it is appropriate Lowering the CTE level consistent with the risk can not result in a decrease in the total liability only can recognize there is more certainty around the amount needed

31. Segregated Fund Reserves: Changes in the CTE Level - Example 1 Look at the Standard Error of CTE0 as a representation of the parameter risk Standard Error has a different shape then CTE In the example setting a PfAD of 2.62 times the standard error with a maximum of CTE80 and minimum of CTE 60 quite complex to implement

32. Standard error of the maturity CTE for a 10-year maturity as a percentage of guaranteed value

33. Segregated Fund Reserves: Changes in the CTE Level - Example 2 More simplistic approach, but similar concept In this example the PfAD is set as 14.2% of the Guarantee value with a maximum of CTE80 and minimum of CTE 60 not as theoretically based but consistent with concepts

34. Maturity PFADs for a 10-year maturity as a percentage of guaranteed value

35. Segregated Fund Reserves: Investment Return Assumptions Valuation can be very sensitive to movements in the market short term fluctuations of common stocks can be considerably greater than fluctuations over longer holding periods product generally priced and designed for the longer term can lead to greater volatility than is theoretically expected

36. Segregated Fund Reserves: Investment Return Assumptions Valuation can be very sensitive to movements in the market however period to period investment performance does directly change the best estimate revenues and costs May be reasonable to dampen impact of short term fluctuations based on the expectation that much of this is transitory

37. Segregated Fund Reserves: Investment Return Assumptions - Examples Looks at 4 different approaches under a given simplified situation (ignores dividends) 50 year historical return is 9.5% current index 1000 previous year index was 900 second previous year was 850

38. Investment Return Assumptions - Examples In setting best estimate assumption Company A uses the long term historical average Company B uses a prudent historical long term average (currently assumed set at 8.5%) Company C also uses a prudent long term average (8.5%) but assumes an initial market correction next years projected level is the average of this years and previous 2 years projected level (result is 7.67% for next year, 8.5% after)

39. Investment Return Assumptions - Examples In setting best estimate assumption Company D also uses a prudent long term average (8.5%) but adjusts its rate for the the first 25 years the 25th year projected level is the average of this years and previous 2 years projected level (result is 8.47% for 25 years , 8.5% after) Resultant projections are

40. Investment Return Assumptions - Examples Following year (T + 1) market return is 15% For Company A revised historical average (51 year now) is 9.61% Company B continues to use 8.5% Company C’s method results in an assumption of 3.61% in year 1, 8.5% thereafter Company D’s method results in an assumption of 8.3% for 25 years 8.5% thereafter

41. Investment Return Assumptions - Examples Resultant projections by Company • Change from Previous Years projections

42. Investment Return Assumptions - Examples Following year (T + 2) market return is a loss of 20% For Company A revised historical average (52 year now) is 8.94% Company B continues to use 8.5% Company C’s method results in an assumption of 31.5% in year 1, 8.5% thereafter Company D’s method results in an assumption of 9.34% for 25 years 8.5% thereafter

43. Investment Return Assumptions - Examples Resultant projections by Company • Change from Previous Years projections

44. Investment Return Assumptions - Examples Upon review the Appointed Actuaries of Companies C and D noticed their initial returns exceeded the long term historical average Adjusted their process to ensure the projected values were not larger Company C’s revised method resulted in a revised assumption of 8.94% for approximately 49 years, 8.5% thereafter Company D’s revised method also results in 8.94% for approximately 49 years, 8.5% thereafter

45. Investment Return Assumptions - Examples Resultant projections by Company • Change from Previous Years projections

46. Segregated Fund Reserves: Criteria for Changes in CTE Levels and Investment Returns Non manipulative Consistent application Produces liabilities within the prescribed range Method is actuarially sound Resultant returns are still the best estimate based on a forward looking assessment