# How to calculate future distribution of price using volatility?

I want to create a lognormal distribution of future stock prices. Using a monte carlo simulation I came up with the standard deviation as being $\sqrt{(days/252)}$ $*volatility*mean*$ $\log(mean)$. Is this correct?

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Thanks for the help, I figured it was a simple mistake. Actual formula for stdev is sqrt(days/252)*volatility*mean. The mistake I was making is days doesn't include first day. So if you have 5 days of random walk, days=4 in above formula. – CptanPanic Feb 3 '11 at 1:34

I'm not sure I understand, but if you want to compute the variance of $exp(X)$, where $X$ is normally distributed with mean $\mu$ and variance $\sigma^2$, that variance is (from Wikipedia): $$\left(\exp{(\sigma^2)} - 1\right) \exp{(2\mu + \sigma^2)}$$
I don't think I asked my question correctly. Assuming a random walk, starting at $\mu$ and yearly volatility of $\sigma$ . What is $\sigma$ for the distribution (expected prices) in like 1 month? I must be overthinking this, but it doesn't seem like $\sigma / 12 works either. – CptanPanic Feb 1 '11 at 1:47 The volatility scales as the square root of time. So in one month, you would have$\sigma / \sqrt{12}$, not$\sigma / 12$. This has nothing to do with being log normal, though. – shabbychef Feb 1 '11 at 3:29 The distribution of the log of a stock price in n days is a normal distribution with mean of$\log(current_price)$and standard deviation of$volatility*\sqrt(n/365.2425)$if you're using calendar days, and assuming no dividends and 0% risk-free interest rate. Note that the standard deviation is independent of the current_price: if$\log(current_price)$increases by 0.3 (for example), the stock has increased by 35%, regardless of its current_price. To include dividends and the risk-free interest rate, see: which models future stock prices w/ an eye towards pricing options. - To create a lognormal distribution (that is, to generate values from it), you need to start with normally distributed numbers and then exponentiate them. That is to say, take a sample$z$from the standard normal distribution, and form the lognormally distributed underlying value $$U_T = U_0 \exp\left( (r-q-\sigma^2/2)T + \sigma \sqrt{T} z \right)$$ The probability density function of$U_T$is formed from solving this for$z\$ and then applying the normal PDF.