文档介绍：: .
6Example 例子
A variable is currently 40
一个变量当期是40
It follows a Markov process
它服从马尔科夫过程
Process is stationary (. the parameters of the process do
not change as we move through time)
该过程是平稳的（即随机过程的参数不随时间变化）
At the end of 1 year the variable will have a normal probability
distribution with mean 40 and standard deviation 10
一年末，该变量服从均值为40，标准差为10的正态分布
7Questions 问题
What is the probability distribution of the
stock price at the end of 2 years?
第二年末该股票的概率分布是什么？
½ years? ½年？
¼ years? ¼年？
Dt years? Dt年？
Taking limits we have defined a continuous
stochastic process
取极限后我们可以定义一个连续的随机过程
8Variances & Standard Deviations
方差和标准差
In Markov processes changes in successive
periods of time are independent
马尔科夫过程里，变量在相邻时间段里的变化是独
立的
This means that variances are additive
这意味着方差是可加的
Standard deviations are not additive
标准差是不可加的
9Variances & Standard Deviations
方差和标准差（续）
In our example it is correct to say that the variance
is 100 per year.
在我们的例子里，方差每年为100。
It is strictly speaking not correct to say that the
standard deviation is 10 per year.
严格来说，标准差每年为10是错误的
10 : .
Chapter 13 第十三章