Central Limit Theorem and Sum of Random Variables Let each $X_i$ be i.i.d and $Y_i=\ln(X_i)$ is exponentially distributed with mean $\alpha^{-1}$ and variance $\alpha^{-2}$.  Now let
$$S_n=\left[ \prod_{i=1}^{n}X_i \right ]^{n^{-1}}.$$ 
Use the central limit theorem to find as $n \rightarrow \infty$ the limiting distribution of:
$$\sqrt{n}(\ln(S_n)-E(\ln(X_1)))=\frac{\sum_{i-1}^n{\ln(X_i)}-n\alpha^{-1}}{\sqrt{n}}.$$
The way that I understand the central limit theorem, if the standard deviation appeared in the denominator, then this distribution would converge to $N(0,1)$, but since it does not appear, can I assume that it instead converges to $N(0,\alpha ^{-2})$?  (The second parameter is the variance).  I don't really understand how this part works.
Finally, if $N(0,\alpha^{-2})$ is correct, does it give me any information about the underlying $S_n$?  Can I use that normal distribution to approximate, for example, $P(S_n>1.12)$?
I'm just really confused on how to properly apply the central limit theorem to situations like these.
 A: Observe that
$$
\log S_n=\frac{\log X_{1}+\dotsb+\log X_n}{n}=\frac{Y_{1}+\dotsb+Y_{n}}{n}
$$
where the $Y_i$ are i.i.d since the $X_i$ are. Observe that
$$
E(\log S_n)=\frac{1}{n}\times nEY_{1}=\alpha^{-1}=EY_{1};\quad \text{Var}(\log S_n)=\frac{1}{n^2}\times n\text{Var}(Y_{1})=\alpha^{-2}/n. 
$$
The central limit theorem implies that
$$
\alpha\sqrt n(\log S_n-\alpha^{-1})\stackrel{D}{\rightarrow} N(0,1).
$$
so that 
$$
\sqrt n(\log S_n-\alpha^{-1})\stackrel{D}{\rightarrow} N(0,\alpha^{-2})
$$
where the second parameter is variance. 
To see this suppose that $Z_{n}$ is a sequence of random variables such that $Z_{n}\stackrel{D}{\rightarrow}Z\sim N(0,1)$. We claim that $Z_{n}/\alpha\stackrel{D}{\rightarrow}Z/\alpha\sim N(0,\alpha^{-2})$ (Here $\alpha >0)$. Indeed,
$$
P(Z_{n}/\alpha\leq x)=P(Z_{n}\leq\alpha x)\stackrel{n\to \infty}{\rightarrow}
P(Z\leq \alpha x)=P(Z/\alpha\leq x).
$$
Using similar reasoning we can conclude that $\log S_n\stackrel{D}{\rightarrow} N(\alpha^{-1}, \alpha^{-2}/n)$. Then $S_n\stackrel{D}{\rightarrow} \text{Lognormal}(\alpha^{-1}, \alpha^{-2}/n)$. For more on the lognormal distribution see here.
