Gary,

I couldn't agree more with your statement that "We think it is the probability of permanent capital loss, not volatility, that constitutes the real risk."

However, I would like to provide some details about the math that is driving the results in your simulation experiment. I see this argument used a lot when discussing volatility but the results are actually driven by the mathematical relationship between normal distributions and log normal distributions, and you aren't comparing apples to apples here.

1. First, when does volatility equal risk. Basic economic theory says this occurs when an investor has either a quadratic utility function or negative exponential utility function and returns are normally distributed.

2. For the experiment you ran --- First according to basic economic theory, no rational investor would ever choose scenario 1 over scenario 2 as scenario 2 completely dominates scenario 1. Second, the geometric return, i.e. annualized total return of scenario 2 equal 7.5%/year whereas the geometric return of scenario 1 equals 6.875% / year. So if you invested $100 in scenario 2 and scenario 1, on average you would always have more money at the end with scenario 2 --- That is exactly why it works better in the experiment. This is due entirely to the relationship between a normal distribution and log-normal distribution, the concept called "volatility drag" which is really just a mathematical statement of this relationship:

Geometric Avg. = Arithmetic Avg. - (Variance^2)/2

I hope this explanation helps shed some light on the math behind your conclusions.

Thanks,