The key properties of the normal distribution have been presented in the LOS above. As a summary, some of the key properties of this distribution are:
A random variable, Y, follows a lognormal distribution if its natural logarithm, lnY, is normally distributed. You can think of the term lognormal as "the log is normal." For example, suppose X is a normal random variable, and Y = eX. Therefore, LnY = Ln(eX) = X. Because X is normally distributed, Y follows a lognormal distribution.
The reverse is also true; if a random variable Y follows a lognormal distribution, then its natural logarithm, lnY, is normally distributed.
|rufi: this is a good tool for BSOPM|
|bahodir: what is BSOPM?|
|bobert: Black-Scholes Option Pricing Model|
|Seemorr: What kind of variable would be lognormally distributed, but not normally?|
|riouxcf: Some variables which have frequent outliers can be made more normal by taking the log. The normal distribution tends to underestimate extremes.|
|czar: Seemor: stock prices (log) and stock returns (normal) as stock prices lowest value can only be 0 while returns can be negative|