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Itô’s Lemma Derives Geometric Brownian Motion from Its Closed Form

Article Quant Q&A · Author: user7348

Summary

The document explains why differentiating the closed-form geometric Brownian motion solution as if it were an ordinary deterministic exponential gives the wrong drift. Because the exponent includes Brownian motion, the appropriate tool is Itô’s lemma, which accounts for the process’s quadratic variation.

Applying the lemma to the exponential produces a second-order term equal to half the volatility squared times the asset value and time increment. This term cancels the negative half-volatility-squared term in the closed-form exponent, leaving the stochastic differential equation with drift μ and diffusion σ. The explanation shows how the two representations are consistent. It is a focused derivation for standard Brownian motion; it does not discuss extensions such as time-varying parameters or other stochastic processes.

Key ideas

  • Brownian motion is stochastic, so ordinary differentiation of the exponential does not capture all terms.
  • Itô’s lemma adds a second-order correction through Brownian motion’s quadratic variation.
  • The correction cancels the negative half-volatility-squared drift in the closed-form solution.
  • The resulting differential form has drift μ and diffusion σ.

Tags

Full text
# How to get Black Scholes' Geometric Brownian Motion differential form form the closed form?


# How to get Black Scholes' Geometric Brownian Motion differential form form the closed form?












My instructor has mostly self contained notes, where our textbook is mostly a reference.

She has it written that:

$$S_t = S_0e^{(\mu - \frac{\sigma^2}{2})t + \sigma W_t} \iff dS_t = S_t(\mu dt + \sigma dW_t)$$

I feel that basic differentiation of the exponential implies that on the right hand side we should have $$dS_t = S_t \left((\mu - \frac{\sigma^2}{2})dt + \sigma dW_t \right)$$.

I'd appreciate understanding why the $\frac{\sigma^2}{2}$ disappears from the differentiation when this is a basic rule about differentiating the exponential.

## Answer by SRKX (score 7, accepted)

https://quant.stackexchange.com/a/15182

You derivation here is flawed because you are deriving with respect to two processes and you do not take into account that the variable $W_t$ is stochastic and hence $S_t$ is as well.

So, to derive $S_t$ from $dS_t$, you have to apply Ito's Lemma, see this question for details. This is the "classic" way you see it.

If you want to do it the other way round, setting $S_t = f(W_t,t) = S_0 \exp \left[ (\mu - \frac{\sigma^2}{2}) t + \sigma W_t \right]$ and applying Ito's Lemma gives you:

$$ df(W_t,t) = \frac{\partial f}{ \partial t } dt + \frac{\partial f}{ \partial W_t } dW_t + \frac{\partial^2 f}{ (\partial W_t)^2 } d \langle W\rangle_t$$ $$ df(W_t,t) = S_t \left(\mu - \frac{\sigma^2}{2} \right) dt + S_t \sigma dW_t + \frac{1}{2} S_t \sigma^2 dt$$ $$ df(W_t,t) = S_t \left[ \left(\mu - \frac{\sigma^2}{2} + \frac{\sigma^2}{2} \right) dt + \sigma dW_t \right]$$ $$ df(W_t,t) = S_t ( \mu dt + \sigma dW_t ) = dS_t$$

So, essentially Ito's Lemma adds a term for the quadratic variation of a stochastic process, $d \langle W\rangle_t$, which is 0 for deterministic processes. This is where the $\frac{\sigma^2}{2}$ appears (or disappears depending how you see it).

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.