Stochastic Modeling Could Help Planners Help Clients
The use of life insurance, particularly variable universal life, as a vehicle to generate attractive after-tax retirement income is well known and popular.
To achieve the desired objectives, the policyowner must accumulate premiums–and the higher the premium level the better, as long as this occurs in a non-modified endowment contract (non-MEC.) Then, at retirement, the owner can start taking cash flows from the contract.
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Current approaches to illustrate the technique typically use level gross yield assumptions of, say, 0%, 6%, and 10%. The approach is, concentrate on the 10% yield performance, and solve for the distribution that can be paid out over the desired income period.
Unfortunately, this strategy tells very little, since as readers of this publication know, the likelihood of such a scenario occurring is as great as being bitten by a great white shark and winning Power Ball at the same time.
The time has come to investigate new techniques for helping producers and clients plan retirement programs better, so that the desired outcomes are more likely to be achieved. Stochastic analysis may show the way.
Stochastic analysis is a technique that recognizes the uncertainties underlying investment performance, especially those influenced by the vagaries of consumer demand, shifting consumer tastes, the strength of various national economies, inflation, etc.
Thus, even when an average value is assumed and that outcome has the highest probability, a spectrum of outcomes is possible. For example, while the records of the past 50 years may show that an investment in a certain combination of funds may yield an average of 10% over time, and while there may be reason to believe that such an outcome is likely, random events will prevent such an outcome from being a certainty.
Using the standard illustration technique, suppose an investment yield of 10% results in an accumulation value by retirement age of $400,000. Stochastic analysis may tell us that out of 1,000 investment scenarios, where the average return is 10% but where the annual returns may vary significantly from the average, only 80% of the scenarios will reach the $400,000 goal.