A recent article entitled “When Monte Carlo analysis meets a black swan” in Investment News addresses the criticisms Monte Carlo simulation has received for “missing the meltdown.” The author, Moshe A. Milevsky , notes that people typically seek a single number “answer” from a Monte Carlo simulation, such as the probability of meeting a single retirement planning goal. Milevsky points out that many Monte Carlo software packages do not include sensitivity or scenario analyses to drill down and determine which variables are really driving the risk inherent in the results. He proposes what amounts to a stress test – simulating what could happen under likely scenarios, and simulating again under 1-in-100 chance “black swan” disastrous scenarios. Milevsky wraps up by saying, “Instead of condemning Monte Carlo analyses for missing the meltdown, let’s properly harness the full power of stochastic methods to give us tools that provide clear utility.”
I believe Milevsky makes a great point, focusing on the modeling practices rather than the tools themselves in this case. Monte Carlo simulation tools are very important for applications like retirement planning, but even the best hammer can’t help an unskilled carpenter. Tools like @RISK include sensitivity and scenario analysis, enabling easy implementation of tests under different scenarios for portfolio value, inflation, longevity, or any combination of these.