New Considerations in Monte Carlo Testing

September 16, 2010 New Considerations in Monte Carlo Testing By Jack Paul, FSA, MAAA, ChFC, CLU, CASL Monte Carlo testing (also referred to as stochastic testing) is often performed for clients of financial planners as a way to calculate the probability that their clients will be able to meet their financial goals, as well as to validate spending and investment strategies to support those goals. The use of this sophisticated technique is fairly new, being made possible by the advances in computing power and by certain software packages that can be used/leased/purchased to perform the testing. For clients at or nearing retirement, there are some very important considerations that are just starting to be incorporated into this type of Monte Carlo testing, and which have a dramatic impact on the calculation on those probabilities. This article will describe these considerations. Although a client will be assumed to be a single male in this article, the considerations equally apply to couples. Briefly, the type of Monte Carlo testing utilized by financial planners involves calculating hypothetical asset rates of return over the future life of the client (based on the characteristics of his asset portfolio). The client will have an anticipated spending strategy over his lifetime to support the client’s financial goals. The asset returns are combined with the anticipated spending, income and other factors, to determine if the client will be able to successfully meet the goals for the given asset rates of return. This process is repeated many (hundreds or thousands) of times to determine the probability that the client will meet his financial goals. If the probability is too low, the asset portfolio or the spending strategy can be modified to increase the probability of success. One important consideration that has recently become introduced into this type of Monte Carlo testing is the assumed time of death of the client. Often, testing is done assuming the client will live to a fixed age, such as (for a 65 year

old) age 85, 90 or 95. Often the age picked is the assumed life expectancy of the client. Of course death can occur at any age. A second consideration is the client’s future long-term care costs. These costs can vary, over the client’s lifetime, from zero to well over a million dollars. And a third consideration is the client’s future prescription drug costs. These costs can vary from very little to more than a half-million dollars over the client’s lifetime. These considerations have recently been addressed by incorporating the client’s potential long-term care and prescription drug costs into the testing, as well as by varying the time of death (by the use of mortality rates). This expands the testing from what I’ll call “Monte Carlo asset testing” into “comprehensive Monte Carlo testing”. An important feature of comprehensive Monte Carlo testing is that it is being customized to the client’s unique morbidity and mortality profiles. Screener questionnaires are filled out by the client, with the help of the financial planner, in order to produce accurate probabilities of long-term care usage, prescription drug usage as well as the probabilities of living to various ages. This comprehensive Monte Carlo testing incorporates the client’s spending, asset portfolio and investment strategies to go along with the long-term care and prescription drug potential costs, as well as the potential mortality of the client, to give the client a comprehensive picture of major retirement risks. The risks all are combined into one useful, meaningful measure – the probability that the client will meet his goals, including the all-important goal of not outliving his assets. Comprehensive Monte Carlo testing is a very flexible and powerful tool. For example, the financial planner has the option to work with a client to produce a customized level of long-term care (should the need arise) as input to the testing. Would the client want a private room in a nursing home? If the client wants to remain at home and never go into an assisted living facility or nursing home but instead wants a nurse at home twenty-four hours a day, how does that

desire affect the client’s ability to meet his goals? What would a more modest level of care look like? This testing can open the door for a financial planner as it is possible to examine different insurance strategies, such as long-term care insurance policies and/or riders, longevity annuities, prescription drug plans (including Medicare Part D plans) and other products to see the effects on the client’s goals, and to perhaps result in a sale beneficial to both the client and the planner. To arrive at an acceptable result, the financial planner works with the client to run iterations of the testing, examining changes in investment, spending, insurance and other strategies to produce acceptable results for the clients. Here is a brief example to show the difference between Monte Carlo asset testing and the comprehensive Monte Carlo testing as described in this article. The differences are illustrated using a 65 year old single male. The differences displayed are in the assumptions used and the results. Assumptions: Time of death: Monte Carlo asset testing: Age 90. Comprehensive Monte Carlo testing: Varies based on a mortality assessment. Long-term care costs: Monte Carlo asset testing: A two-year stay in a nursing home starting at age 80. Comprehensive Monte Carlo testing: Varies based on a morbidity assessment. Prescription drug costs:

Monte Carlo asset testing: Current drug use assumed to continue throughout life. Comprehensive Monte Carlo testing: Drug use could change based on a morbidity assessment. Results, expressed as the probability that the client will not outlive his assets: Monte Carlo asset testing: 58% Comprehensive Monte Carlo testing: 81% These two probabilities – 81% as opposed to 58% - are quite different for such a critical computation. Using the additional considerations described in this article gives crucial insight into what is often the most important issue in the client’s mind – whether his assets will last for the rest of his life. This additional insight is crucial – if the client’s chance of success is understated, there could be an unnecessary cutback of the client’s lifestyle. And if the client’s chance of success is overstated, that could lead to a false sense of security and could result in a client running out of money because of the overstatement! Therefore, it would benefit financial planners to consider incorporating comprehensive Monte Carlo testing into their practices. Jack Paul can be reached at Jack@JackPaulCASL.com

New Considerations in Monte Carlo Testing

by Jack P Paul Actuary LLC

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