In this ongoing series, we provide our readers with two distinct perspectives on the same topic — one from an academic, the other from a practicing financial advisor. In "The Advisor and The Quant," we will pose one specific question to the advisor and the quant. You will see their responses here on ThinkAdvisor.
If you have a question or two, please send them to us using the form at the end of this article.
Learn more about The Advisor, Joe Elsasser, CFP, and The Quant, Ron Piccinini, Ph.D.
QUESTION: What is fat-tailed modeling and why does it matter?
JOE ELSASSER, CFP, PRESIDENT, COVISUM:
In order to understand "fat tails" we have to start with "tails." The standard bell curve graph that we see used for everything from test scores to potential returns is fat in the middle and very narrow at the edges, which are also known as the "tails."
The bell curve is a representation of how often something happens (or should happen if used to project into the future). Its ranges are based on standard deviations, which is a measure of how often something that is not the mean should happen. One standard deviation is about 68% of the time, two standard deviations is about 95% of the time and three standard deviations is about 99.5% of the time.
The problem with using this model of returns is that in finance, it simply isn't true. Extreme events happen far more often than a normal distribution would predict. Nicholas Nassim Taleb first popularized this idea after the Great Recession with his book "The Black Swan," though it had been written about extensively by other academics prior.
Using the visual above, "fat tails" is simply a recognition that events that should happen very rarely, actually happen with quite a bit more frequency. So, a "fat tailed" model that more effectively represents the reality of financial market returns, looks narrower in the middle, but fatter in the tails like this example of the returns distribution of a 60/40 portfolio: