Extreme Value Theory - Show: Normal to Gumbel The Maximum of $X_1,\dots,X_n. \sim$ i.i.d. Standardnormals converges to the Standard Gumbel Distribution according to Extreme Value Theory.
How can we show that?
We have 
$$P(\max X_i \leq x)  = P(X_1 \leq x, \dots, X_n \leq x) 
 = P(X_1 \leq x) \cdots P(X_n \leq x) = F(x)^n
$$
We need to find/choose $a_n>0,b_n\in\mathbb{R}$ sequences of constants such that: $$F\left(a_n x+b_n\right)^n\rightarrow^{n\rightarrow\infty}  G(x) = e^{-\exp(-x)}$$
Can you solve it or find it in literature?
There are some examples pg.6/71, but not for the Normal case:
$$\Phi\left(a_n x+b_n\right)^n=\left(\frac{1}{\sqrt{2\pi}}\int_{-\infty}^{a_n x+b_n} e^{-\frac{y^2}{2}}dy\right)^n\rightarrow e^{-\exp(-x)}$$
 A: An indirect way, is as follows:
For absolutely continuous distributions, Richard von Mises (in a 1936 paper "La distribution de la plus grande de n valeurs", which appears to have been reproduced -in English?- in a 1964 edition with selected papers of his), has provided the following sufficient condition for the maximum of a sample to converge to the standard Gumbel, $G(x)$:
Let $F(x)$ be the common distribution function of $n$ i.i.d. random variables, and $f(x)$ their common density. Then, if
$$\lim_{x\rightarrow F^{-1}(1)}\left (\frac d{dx}\frac {(1-F(x))}{f(x)}\right) =0 \Rightarrow X_{(n)} \xrightarrow{d} G(x)$$
Using the usual notation for the standard normal and calculating the derivative, we have
$$\frac d{dx}\frac {(1-\Phi(x))}{\phi(x)} = \frac {-\phi(x)^2-\phi'(x)(1-\Phi(x))}{\phi(x)^2} = \frac {-\phi'(x)}{\phi(x)}\frac {(1-\Phi(x))}{\phi(x)}-1$$
Note that $\frac {-\phi'(x)}{\phi(x)} =x$. Also, for the normal distribution, $F^{-1}(1) = \infty$. So we have to evaluate the limit
$$\lim_{x\rightarrow \infty}\left (x\frac {(1-\Phi(x))}{\phi(x)}-1\right) $$
But $\frac {(1-\Phi(x))}{\phi(x)}$ is Mill's ratio, and we know that the Mill's ratio for the standard normal tends to $1/x$ as $x$ grows.
So
$$\lim_{x\rightarrow \infty}\left (x\frac {(1-\Phi(x))}{\phi(x)}-1\right) = x\frac {1}{x}-1= 0$$
and the sufficient condition is satisfied.  
The associated series are given as
$$a_n = \frac 1{n\phi(b_n)},\;\;\; b_n = \Phi^{-1}(1-1/n)$$
ADDENDUM 
This is from ch. 10.5 of the book H.A. David & H.N. Nagaraja (2003), "Order Statistics" (3d edition).
$\xi_a = F^{-1}(a)$. Also, the reference to de Haan is "Haan, L. D. (1976). Sample extremes: an elementary introduction. Statistica Neerlandica, 30(4), 161-172."
But beware because some of the notation has different content in de Haan -for example in the book $f(t)$ is the probability density function, while in de Haan $f(t)$ means the function $w(t)$ of the book (i.e. Mill's ratio). Also, de Haan examines the sufficient condition already differentiated.

A: The question asks two things: (1) how to show that the maximum $X_{(n)}$ converges, in the sense that $(X_{(n)}-b_n)/a_n$ converges (in distribution) for suitably chosen sequences $(a_n)$ and $(b_n)$, to the Standard Gumbel distribution and (2) how to find such sequences.
The first is well-known and documented in the original papers on the Fisher-Tippett-Gnedenko theorem (FTG).  The second appears to be more difficult; that is the issue addressed here.
Please note, to clarify some assertions appearing elsewhere in this thread, that

*

*The maximum does not converge to anything: it diverges (albeit extremely slowly).


*There appear to be different conventions concerning the Gumbel distribution.  I will adopt the convention that the CDF of a reversed Gumbel distribution is, up to scale and location, given by $1-\exp(-\exp(x))$.  A suitably standardized maximum of iid Normal variates converges to a reversed Gumbel distribution.

Intuition
When the $X_i$ are iid with common distribution function $F$, the distribution of the maximum $X_{(n)}$ is
$$F_n(x) = \Pr(X_{(n)}\le x) = \Pr(X_1 \le x)\Pr(X_2 \le x) \cdots \Pr(X_n \le x) = F^n(x).$$
When the support of $F$ has no upper bound, as with a Normal distribution, the sequence of functions $F^n$ marches forever to the right without limit:

Partial graphs of $F_n$ for $n=1,2,2^2, 2^4, 2^8, 2^{16}$ are shown.
To study the shapes of these distributions, we can shift each one back to the left by some amount $b_n$ and rescale it by $a_n$ to make them comparable.

Each of the previous graphs has been shifted to place its median at $0$ and to make its interquartile range of unit length.
FTG asserts that sequences $(a_n)$ and $(b_n)$ can be chosen so that these distribution functions converge pointwise at every $x$ to some extreme value distribution, up to scale and location.  When $F$ is a Normal distribution, the particular limiting extreme value distribution is a reversed Gumbel, up to location and scale.

Solution
It is tempting to emulate the Central Limit Theorem by standardizing $F_n$ to have unit mean and unit variance.  This is inappropriate, though, in part because FTG applies even to (continuous) distributions that have no first or second moments.  Instead, use a percentile (such as the median) to determine the location and a difference of percentiles (such as the IQR) to determine the spread.  (This general approach should succeed in finding $a_n$ and $b_n$ for any continuous distribution.)
For the standard Normal distribution, this turns out to be easy!  Let $0 \lt q \lt 1$.  A quantile of $F_n$ corresponding to $q$ is any value $x_q$ for which $F_n(x_q) = q$.  Recalling the definition of $F_n(x) = F^n(x)$, the solution is
$$x_{q;n} = F^{-1}(q^{1/n}).$$
Therefore we may set
$$b_n = x_{1/2;n},\ a_n = x_{3/4;n} - x_{1/4;n};\ G_n(x) = F_n(a_n x + b_n).$$
Because, by construction, the median of $G_n$ is $0$ and its IQR is $1$, the median of the limiting value of $G_n$ (which is some version of a reversed Gumbel) must be $0$ and its IQR must be $1$.  Let the scale parameter be $\beta$ and the location parameter be $\alpha$.  Since the median is $\alpha + \beta \log\log(2)$ and the IQR is readily found to be $\beta(\log\log(4) - \log\log(4/3))$, the parameters must be
$$\alpha = \frac{\log\log 2}{\log\log(4/3) - \log\log(4)};\ \beta = \frac{1}{\log\log(4) - \log\log(4/3)}.$$
It is not necessary for $a_n$ and $b_n$ to be exactly these values: they need only approximate them, provided the limit of $G_n$ is still this reversed Gumbel distribution.  Straightforward (but tedious) analysis for a standard normal $F$ indicates that the approximations
$$a_n^\prime = \frac{\log \left(\left(4 \log^2(2)\right)/\left(\log^2\left(\frac{4}{3}\right)\right)\right)}{2\sqrt{2\log (n)}},\ b_n^\prime = \sqrt{2\log (n)}-\frac{\log (\log (n))+\log \left(4 \pi  \log ^2(2)\right)}{2  \sqrt{2\log (n)}}$$
will work fine (and are as simple as possible).

The light blue curves are partial graphs of $G_n$ for $n=2, 2^6, 2^{11}, 2^{16}$ using the approximate sequences $a_n^\prime$ and $b_n^\prime$.  The dark red line graphs the reversed Gumbel distribution with parameters $\alpha$ and $\beta$.  The convergence is clear (although the rate of convergence for negative $x$ is noticeably slower).

References
B. V. Gnedenko, On The Limiting Distribution of the Maximum Term in a Random Series.  In Kotz and Johnson, Breakthroughs in Statistics Volume I: Foundations and Basic Theory, Springer, 1992.  Translated by Norman Johnson.
A: Here is a "direct" approach.  Let $a_n > 0$, $b_n$ to be determined so that $a_nx+b_n \rightarrow +\infty$ for all $x$.
From L'Hospital's rule,
$$
\underset{A \rightarrow \infty }{lim} \frac{\int_A^{+\infty} e^{-u^2/2} du}{A^p e^{-A^2/2}} = 1 
$$
when $p=-1$, so we have:
$$
F(a_n x + b_n) = 1 - \frac{1}{\sqrt{2\pi}}\, \frac{e^{-(a_nx+b_n)^2/2}}{(a_nx+b_n)}(1+o(1))
$$
where $o(1) \rightarrow 0$ under the running assumption on $a_n, b_n$.
Then
$$
\begin{align*}
ln\!\left(\ F(a_n x + b_n)^n \right) &= 
n\, ln\!\left(  1 - \frac{1}{\sqrt{2\pi}}\, \frac{e^{-(a_nx+b_n)^2/2}}{(a_nx+b_n)}(1+o(1))\right) \\
& =  - \frac{n}{\sqrt{2\pi}}\, \frac{e^{-(a_nx+b_n)^2/2}}{(a_nx+b_n)}(1+o(1)) \\
& = - \frac{n}{\sqrt{2\pi}}\, \frac{e^{-a_n^2x^2/2-b_n^2/2 - a_nb_nx}}{(a_nx+b_n)}(1+o(1)) 
\end{align*} 
$$
If we take $a_n = 1/b_n \rightarrow 0^+$, the required assumption $a_nx+b_n \rightarrow +\infty$ for all $x$ is satisfied, and:
$$
\begin{align*}
ln\!\left(\ F(a_n x + b_n)^n \right) &= 
 - \frac{n}{\sqrt{2\pi}\,  b_n \,e^{b_n^2/2} }\, e^{-x}(1+o(1)) 
\end{align*} 
$$
Now all that remains to do to get the result is to show one can choose $b_n$ so that
$$
b_n \,e^{b_n^2/2} ~=~ \frac{n}{\sqrt{2\pi}}(1+o(1)) \ \ ,
$$
which is clearly feasible.
Solving this equation "asymptotically" is amusing. Obviously the big factor on the left is $e^{b_n^2/2}$, which suggests $b_n \approx \sqrt{2\,ln(n)}$. After some trial and error, one possible explicit solution is:
$$
b_n = \sqrt{ ln\!\left( \frac{n^2}{4\pi\, ln(n)} \right) } = \sqrt{2 ln(n) \left( 1 - \frac{ln(4\pi\,ln(n))}{2\, ln(n)} \right)}
$$
