# Geometric Brownian motion

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Two sample paths of Geometric Brownian motion, with different parameters. The blue line has larger drift, the green line has larger variance.

A geometric Brownian motion (GBM) (also known as exponential Brownian motion) is a continuous-time stochastic process in which the logarithm of the randomly varying quantity follows a Brownian motion (also called a Wiener process) with drift.[1] It is an important example of stochastic processes satisfying a stochastic differential equation (SDE); in particular, it is used in mathematical finance to model stock prices in the Black–Scholes model.

## Technical definition: the SDE

A stochastic process St is said to follow a GBM if it satisfies the following stochastic differential equation (SDE):

${\displaystyle dS_{t}=\mu S_{t}\,dt+\sigma S_{t}\,dW_{t}}$

where ${\displaystyle W_{t}}$ is a Wiener process or Brownian motion and ${\displaystyle \mu }$ ('the percentage drift') and ${\displaystyle \sigma }$ ('the percentage volatility') are constants. The latter term is often used to model a set of unpredictable events occurring during this motion, while the former is used to model deterministic trends.

## Solving the SDE

For an arbitrary initial value S0 the above SDE has the analytic solution (under Itō's interpretation):

${\displaystyle S_{t}=S_{0}\exp \left(\left(\mu -{\frac {\sigma ^{2}}{2}}\right)t+\sigma W_{t}\right).}$

To arrive at this formula, let us divide the SDE by ${\displaystyle S_{t}}$, and write it in Itō integral form:

${\displaystyle \int _{0}^{t}{\frac {dS_{t}}{S_{t}}}=\mu \,t+\sigma \,W_{t}\,,\qquad {\text{assuming }}W_{0}=0\,.}$

Of course, ${\displaystyle {\frac {dS_{t}}{S_{t}}}}$ looks like having a lot to do with the derivative of ${\displaystyle \ln S_{t}}$; however, ${\displaystyle S_{t}}$ being an Itō process, we need to use Itō calculus: by Itō's formula, we have

${\displaystyle d(\ln S_{t})={\frac {dS_{t}}{S_{t}}}-{\frac {1}{2}}\,\sigma ^{2}\,dt\,.}$

Plugging back to the equation we got from the SDE, we obtain

${\displaystyle \ln {\frac {S_{t}}{S_{0}}}=\left(\mu -{\frac {1}{2}}\,\sigma ^{2}\right)t+\sigma W_{t}\,.}$

Exponentiating gives the solution claimed above.

## Properties of GBM

The above solution ${\displaystyle S_{t}}$ (for any value of t) is a log-normally distributed random variable with expected value and variance given by[2]

${\displaystyle \mathbb {E} (S_{t})=S_{0}e^{\mu t},}$
${\displaystyle \operatorname {Var} (S_{t})=S_{0}^{2}e^{2\mu t}\left(e^{\sigma ^{2}t}-1\right),}$

that is the probability density function of a St is:

${\displaystyle f_{S_{t}}(s;\mu ,\sigma ,t)={\frac {1}{\sqrt {2\pi }}}\,{\frac {1}{s\sigma {\sqrt {t}}}}\,\exp \left(-{\frac {\left(\ln s-\ln S_{0}-\left(\mu -{\frac {1}{2}}\sigma ^{2}\right)t\right)^{2}}{2\sigma ^{2}t}}\right).}$

When deriving further properties of GBM, use can be made of the SDE of which GBM is the solution, or the explicit solution given above can be used. For example, consider the stochastic process log(St). This is an interesting process, because in the Black–Scholes model it is related to the log return of the stock price. Using Itō's lemma with f(S) = log(S) gives

{\displaystyle {\begin{alignedat}{2}d\log(S)&=f^{\prime }(S)\,dS+{\frac {1}{2}}f^{\prime \prime }(S)S^{2}\sigma ^{2}\,dt\\&={\frac {1}{S}}\left(\sigma S\,dW_{t}+\mu S\,dt\right)-{\frac {1}{2}}\sigma ^{2}\,dt\\&=\sigma \,dW_{t}+(\mu -\sigma ^{2}/2)\,dt.\end{alignedat}}}

This result can also be derived by applying the logarithm to the explicit solution of GBM:

{\displaystyle {\begin{alignedat}{2}\log(S_{t})&=\log \left(S_{0}\exp \left(\left(\mu -{\frac {\sigma ^{2}}{2}}\right)t+\sigma W_{t}\right)\right)\\&=\log(S_{0})+\left(\mu -{\frac {\sigma ^{2}}{2}}\right)t+\sigma W_{t}.\end{alignedat}}}

Taking the expectation yields the same result as above: ${\displaystyle \mathbb {E} \log(S_{t})=\log(S_{0})+(\mu -\sigma ^{2}/2)t}$.

## Multivariate Geometric Brownian motion

GBM can be extended to the cast where there are multiple correlated price paths.

Each price path follows the underlying process

${\displaystyle dS_{t}^{i}=\mu _{i}S_{t}^{i}\,dt+\sigma _{i}S_{t}^{i}\,dW_{t}^{i}}$,

where the Wiener processes are correlated such that ${\displaystyle \mathbb {E} (dW_{t}^{i}dW_{t}^{j})=\rho _{i,j}dt}$ where ${\displaystyle \rho _{i,i}=1}$.

For the multivariate case, this implies that

${\displaystyle \mathrm {Cov} (S_{t}^{i},S_{t}^{j})=e^{(\mu _{i}+\mu _{j})t}\left(e^{\rho _{i,j}\sigma _{i}\sigma _{j}t}-1\right)}$.

## Use of GBM in finance

{{#invoke:main|main}} Geometric Brownian Motion is used to model stock prices in the Black–Scholes model and is the most widely used model of stock price behavior.[3]

Some of the arguments for using GBM to model stock prices are:

• The expected returns of GBM are independent of the value of the process (stock price), which agrees with what we would expect in reality.[3]
• A GBM process only assumes positive values, just like real stock prices.
• A GBM process shows the same kind of 'roughness' in its paths as we see in real stock prices.
• Calculations with GBM processes are relatively easy.

However, GBM is not a completely realistic model, in particular it falls short of reality in the following points:

• In real stock prices, volatility changes over time (possibly stochastically), but in GBM, volatility is assumed constant.
• In real stock prices, returns are usually not normally distributed (real stock returns have higher kurtosis ('fatter tails'), which means there is a higher chance of large price changes).[4]

## Extensions of GBM

In an attempt to make GBM more realistic as a model for stock prices, one can drop the assumption that the volatility (${\displaystyle \sigma }$) is constant. If we assume that the volatility is a deterministic function of the stock price and time, this is called a local volatility model. If instead we assume that the volatility has a randomness of its own—often described by a different equation driven by a different Brownian Motion—the model is called a stochastic volatility model.