The
log-normal distribution is often assumed to be the distribution of a
stock price. A distribution is log-normally distributed when the
natural log of the set of the random variables in that distribution is
a normally distributed. In plain English, if you take the natural
log of each of the random numbers from a log-normal distribution, the
new number set will be normally distribution. Like the normal
distribution, log-normal distribtuion is also defined with mean and
standard deviation.
(In Excel, LN( )
is the function that returns the natural log of a number. EXP( ) is the function that
returns e (2.718282) to the power of a given number. EXP(1)=2.718282, LN(2.718282) = 1.)
The following example shows input and output from 3 simulations.
Each has the same mean (50) with different standard deviation, 5, 10,
and 30 respectively. All three simulations have 50,000 iterations
and alpha of 5% (for 1 tail test).
The
output shows the estimate of skewness, mean, stand deviation, maximum
value, minimum value, lower confidence interval, and upper confidence
interval from each of the 3 simulations. The skewness increases
as the standard deviation increases.
