one is often interested in the dynamics of stochastic processes which are functions of : Then can also be described by a solution of a stochastic differential equation from which interesting properties of can be derived as for example the average growth in time .

For a heuristic derivation of the equation for
we assume that is differentiable as many times as
necessary. From a Taylor expansion it follows:

where the dots indicate the terms which can be neglected (for ). Due to equation (5.8) the drift term and the volatility term are the dominant terms since for they are of size and respectively.

In doing this, we use the fact that
and
is of the size of its standard deviation,
We neglect terms which are of smaller size than
Thus, we can express by a simpler term:

We see that the first and the second term are of size and respectively. Therefore, both can be neglected. However, the third term is of size More precisely, it can be shown that

Thanks to this identity, calculus rules for stochastic integrals
can be derived from the rules for deterministic functions (as
Taylor expansions for example). Neglecting terms which are of
smaller size than we obtain from (5.9) the
following version of *Itôs lemma*:

or - dropping the time index and the argument of the function and its derivatives:

Consider the logarithm of the geometric Brownian motion. For we obtain Applying Itôs lemma for the geometric Brownian motion with we get:

The *logarithm of the stock price* is a generalized Wiener process with drift
rate
and variance rate
Since
is
N-distributed is itself
*lognormally distributed* with parameters and

A generalized version of Itôs lemma for functions which are allowed to depend on time is:

is again an Itô process, but this time the drift rate is augmented by an additional term