Many of us have been searching for ways to enhance the quality and accuracy of the financial plans we create for clients. The use of Monte Carlo simulation (MCS) is one example, but there’s still plenty of room for improvement. In the past few posts, we’ve discussed three different distributions (normal, uniform and triangular) that can be used with MCS in financial planning applications. In this post, we’ll discuss why the lognormal distribution is the best choice for the inflation assumption.
Inflation is clearly one of the more important assumptions in financial planning. However, beyond the consumer price index (CPI), as planners we should also consider using a separate inflation rate for Medicare, other health insurance premiums and college tuition, as these have risen faster than CPI. This should produce a more accurate expense assumption and hence, financial plan. However, we’ll focus our attention on the CPI data from January 1947 to the end of 2013 (66 years).
The annual rate of change in the CPI during this period was approximately 3.7%. Three of the years were negative and the rest were positive. The table below contains the CPI data by tranches.
Here’s how I determined that the lognormal distribution is the best choice. First, I selected the annual CPI data (i.e. percentage change) using a program called Batch Fit which is included in Crystal Ball. (Crystal Ball is a Monte Carlo application which is an add-in for Microsoft Excel). Batch Fit analyzed the data and determined the lognormal distribution was the best fit.