几何布朗运动 (GBM)(也叫做指数布朗运动) 是连续时间情况下的随机过程，其中随机变量的对数遵循布朗运动,also

called aWiener

process.几何布朗运动在金融数学中有所应用，用来在布莱克-舒尔斯定价模型中模仿股票价格。

Technical定义

A 随机过程St在满足一下随机微分方程 (SDE)的情况下被认为遵循几何布朗运动：

这里是一个维纳过程,或者说是布朗运动，而('百分比drift')

和('百分比volatility')则是常量。几何布朗运动的特性

给定初始值 S0，根据伊藤积分，上面的

SDE有如下解:

对于任意值 t，这是一个

也就是说St的概率密度函数是:

根据伊藤引理，这个解是正确的。

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. 比如，考虑随机过程 log(St).

这是一个有趣的过程，因为在布莱克-舒尔斯模型中这和股票价格的对数回报率相关。对f(S)

= log(S)应用伊藤引理，得到

于是

.

这个结果还有另一种方法获得：applying the logarithm to the explicit solution of

GBM:

取期望值，获得和上面同样的结果:.在金融中的应用

几何布朗运动在布莱克-舒尔斯定价模型被用来定性股票价格，因而也是最常用的描述股票价格的模型。

使用几何布朗运动来描述股票价格的理由:

The expected returns of几何布朗运动are

independent of the value of the process (stock price), which agrees

with what we would expect in reality.

几何布朗运动process only assumes

positive values, just like real stock prices.

几何布朗运动 process shows the same

kind of 'roughness' in its paths as we see in real stock

prices.

几何布朗运动过程计算相对简单。.

然而，几何布朗运动并不完全现实，尤其存在一下缺陷:

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峰度('fatter

tails'), which means there is a higher chance of large price

changes).几何布朗运动推广

In an attempt to make GBM more realistic as a model for stock

prices, one can drop the assumption that the volatility

(

)

is constant. If we assume that the volatility is adeterministic 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