I have been away from MHF for quite some time due to how busy my life has been due to work, personal life changes (house, job) and of course, my actuarial exams (in November).

I will not introduce Brownian motion here because I want someone familiar with it to answer a simple question or two for me...

The limit of the sum of infinitely small random movements over a period of time is the accumulation of continuous Random Walks. In other words, that is the bare bones version of Brownian motion. With the rules of Brownian motion set, it can officially be created.

Let there be n number of periods of total length T. The length of a period is then $\displaystyle h=\frac{T}{n}$. Stock price change, the difference of one stock price from a stock price the period before can be defined as: $\displaystyle S_{t+h}-S_t=Y_{t+h}\sqrt{h}$

The Y variable is the Random Walk effect.WHY THE SQUARE ROOT OF THE TIME PERIOD?

Let time start at zero, and instead of analyzing the individual periods (that are infinitely small), analyze the entire time period as a whole. Let’s be practical here; we’re interested in stock price movements over a course of real time, such as a day, week, month, quarter or year, not at a blink of an eye! The sum of these infinitely small periods is simply T, so a series can be developed.

$\displaystyle S_T-S_0=\sum_{k=1}^{n}Y_{hk}\sqrt{h}$

$\displaystyle =\sqrt{h}\sum_{k=1}^{n}Y_{hk}$

$\displaystyle =\sqrt{\frac{T}{n}}\sum_{k=1}^{n}Y_{hk}$

In Brownian motion, we want to find theexpectedstock price change, so some basic statistics have to be applied.

$\displaystyle E[S_T-S_0]=\sqrt{T}*E[\frac{\sum_{k=1}^{n}Y_{hk}}{\sqrt{n}}]$

The expected value of the Y variable in the series is zero as it is equally likely to go up as it is to go down. Therefore, the expected stock price change is zero!

This doesn’t mean that the model predicts no change in the stock. The dynamics of variance must be considered. The variance of the Y variable is simply one unit as the random walk will either go up 1 or go down 1.

$\displaystyle Var[S_T-S_0]=Var[\frac{\sum_{k=1}^{n}Y_{hk}}{\sqrt{n}}]$

$\displaystyle =Var[\frac{\sum_{k=1}^{n}1}{\sqrt{n}}]$

$\displaystyle =Var[\frac{1}{\sqrt{n}}]*n$

$\displaystyle =\frac{n}{n}=1$

This is why the square root of the time period is taken. It lets the sum of independent random variables approach a standard normal distribution! This is the Central Limit Theorem at work. This is the structure of Brownian motion.

These are some of the notes that I have been writing. As you can see, I have on simple question regarding the parameter of time.