More recently, in an article called "Nailing Down Risk," James Xiong says that a log-TLF (Truncated Lévy Flight ) model might offer results that more accurately match historical experience. This research looks promising, but I suspect that many advisors will have trouble understanding concepts like log-TLF models, let alone explaining them to clients.

The Money Tree Fat Tail Method
Money Tree Software has come up with an interesting alternative approach that allows advisors to run Monte Carlo simulations approximating the historical distributions of returns. The Money Tree method has at least one major advantage over the log-TLF method: Any advisor who has a basic understanding of Monte Carlo simulations should be able to grasp it in about five minutes.

Money Tree's program uses a two-stage process to show more likelihood of a major disaster-the fatter tail on the standard deviation chart. In step one, the program follows a standard Monte Carlo simulation approach. The program generates random returns using a normal distribution. The user defines the mean return. The standard deviation of returns can be defined by the user, or the program can select one. Ten thousand simulations are then run for the plan. Since this step is already familiar to most advisors, there is nothing new to learn here.

But then, in step two, the program resamples the lowest 2% of returns generated during step one and makes them even worse. What's the point? In a normal distribution, almost all of the worst returns will fall within the negative 2.5 to negative 3.0 standard deviation range. Through resampling, the program distributes those returns in a range of negative 2.5 to negative 5.0. The net effect is that you end up with many more negative returns exceeding three standard deviations.

How many more? According to Mark Snodgrass, president of Money Tree Software, those events exceeding three standard deviations using the Money Tree fat tail method are about 12.5 times more prevalent than those in the "standard model"-and about 1.5 times more prevalent than they are in Kaplan's monthly historical observations of the S&P 500.

Does the fact that Money Tree's fat tail model offers more extreme outcomes limit its validity and usefulness? Not necessarily. Remember, these are simulations, not facts. The whole idea is to try and model what might happen under a given set of circumstances. If we are trying to model an unknowable future, might it not make sense to err on the side of too many bad years as opposed to too few?

Although it's up to individual firms to decide whether the Money Tree fat tail method works for them, I find the method appealing for a number of reasons. First, as stated earlier, if you have a basic understanding of Monte Carlo simulations, you should be able to easily understand this methodology-and it's something that you should be able to explain to your clients easily as well. All you really have to tell the client is that standard Monte Carlo simulation understates the probability of a serious market downturn, and that's why you are using this new tool in addition to adjust for the somewhat higher probability that a bad market event will occur. If you or your client requires a detailed explanation of how the actual calculations are computed, Money Tree will be happy to supply you with documentation of all the particulars.

The other thing I really like about this fat tail option is that it is, in fact, simply that-an option. You don't have to use it if you do not want to. Money Tree makes the fat tail option available in both its Silver Planner and its more comprehensive TOTAL Planning Suite. If you fail to check the fat tails checkbox, Money Tree will run its standard Monte Carlo simulation routine. If you do check the box, it will run the fat tail calculation instead.

What is the impact of choosing the fat tail method as opposed to the standard method? Obviously, the fat tail method, with a more extreme number of bad events, will lower the plan's overall probability of success. The magnitude of the difference between the standard method and the fat tail method will depend on a number of factors, including the length of the plan. To give you some frame of reference, Snodgrass says that a 65-year-old retiring today with a 30- to 40-year life expectancy and a probability of success of 90% using the standard method might see that probability drop to 82% or so using the fat tail method.

By offering the fat tail option, Money Tree gives advisors the option of easily increasing the probability of some bad market events over time if the advisor deems it appropriate. In addition, Money Tree Silver Online, the application I used to test the fat tail method, offers advisors options to soften the impact that market declines will have with adjustments. For example, with the push of a button, you can view an alternative scenario that pushes retirement out a year or two. Or you can look at a scenario that marginally reduces variable expenses on the fly.