Thin tails and the Generalized Pareto Rewriting my question:
On this Mathworks page:
https://www.mathworks.com/help/stats/generalized-pareto-distribution.html
it is said (as many textbooks say) that
"Distributions whose tails decrease exponentially, such as the normal, lead to a generalized Pareto shape parameter of zero."
I don't understand this statement. The tail of an exponential will decrease as exp(-x) while the tail of a normal will decrease as exp(-x^2). 
Can someone explain this?
 A: At https://stats.stackexchange.com/a/86503/919 I have argued that to study the right tail of a distribution $F,$ you should analyze its survival function $1-F$ for large values of the argument.  Because such questions often compare tail behavior to some form of exponential, it is convenient to look at the logarithm of the survival function.
Background and definitions
The Generalized Pareto Distribution is a family of distributions.  Their shapes depend on one real parameter $\xi.$  The other two parameters allow you to select any unit of measurement you like by choosing its origin $\mu$ and scale $\sigma,$ and therefore do not affect asymptotic tail behavior.  Consequently we may set $\mu=0$ and $\sigma=1$ without losing any generality.
For $\xi\ne 0,$ the log survival function is
$$\log(1 - F_\xi(z)) = -\frac{\log(1 + \xi z)}{\xi}\tag{1}$$
on the domain $[0, \infty)$ for positive $\xi$ and the domain $[0, -1/\xi)$ for negative $\xi.$
The case of negative $\xi$ is relatively uninteresting because there is no probability of values exceeding $-1/\xi:$ the right tail ultimately is zero.  Let's focus henceforth on non-negative $\xi.$
The "edge case" is the limit as $\xi\to 0.$  L'Hopital's Rule enables us to find this limit as
$$\lim_{\xi\to 0} \log\left(1-F_\xi(z)\right) = - \lim_{\xi\to 0} \frac{\frac{d}{d\xi}(\log(1+\xi z))}{\frac{d}{d\xi}\xi} = -\lim_{\xi\to 0} \frac{z}{1+\xi z} = -z.$$
Thus, it makes sense to include the distribution with log survival function $-z$ in this family.  That distribution is the Exponential distribution.
Analysis
Now we fix $\xi\ge 0$ and study the distribution for large $z.$  In this case $(1)$ is asymptotically a power tail because the Taylor expansion of the logarithm around $1$ shows
$$\eqalign{-\frac{1}{\xi}\log(1+\xi z) &= -\frac{1}{\xi}\left(\log(\xi) + \log(z) + \log\left(1 + \frac{1}{\xi z}\right)\right) \\
&= -\frac{1}{\xi} \log(z) + C_\xi - \frac{1}{\xi^2}z^{-1} + O(z^{-2})
}$$
where $C_\xi = -(\log(\xi))/\xi$ is just a normalizing constant.  All the terms eventually become arbitrarily small compared to the first, which is a constant times $\log(z):$ when exponentiated, this shows the survival function asymptotically is $z$ to the $-1/\xi$ power, as claimed.
Consequently, the members of the Generalized Pareto Distribution have three kinds of right tail behavior:

*

*The survival function eventually is zero for negative $\xi.$


*The survival function is asymptotic to the $-1/\xi$ power for positive $\xi.$


*The survival function is exponential in the sense that it is exactly proportional to $e^{-z}$ when $\xi=0.$
This does not cover the gamut of possible asymptotic tail behaviors of arbitrary distributions.  For instance, the log survival function of any Normal distribution is asymptotic to $-z^2,$ which decreases faster than any power or the exponential.  Thus, the Generalized Pareto Distribution cannot model just any distribution: it can only model certain distributions with "long" or "heavy" tails.  See Example of heavy-tailed distribution that is not long-tailed for more on these concepts.
By definition, a heavy-tailed distribution $F$ is one with a tail that decays slower than any exponential in the sense that for all positive constants $t,$
$$\int_{\mathbb R} e^{tz} \mathrm{d}F(z) = \infty.$$
When $\xi=0$ and $0\lt t \lt 1,$
$$\int_{\mathbb R} e^{tz} \mathrm{d}F_\xi(z) =\int_0^\infty e^{tz} e^{-z} \mathrm{d}z = \frac{1}{1-t}\ne\infty$$
shows that

The Exponential distribution ($\xi=0$) is not heavy-tailed.  However, all Generalized Pareto Distributions with $\xi\gt 0$ are heavy-tailed.

In this sense the case $\xi=0$ is a boundary between the heavy power tailed Pareto distributions and their limiting non-heavy Exponential distribution.  This is the basis for the intuition that this family, for $\xi\ge 0,$ is a good one for modeling just the heavy-tailed distributions.

Appendix: Technicalities
If these claims about the tails aren't clear, the following is a rigorous demonstration.  Note that for any positive numbers $x,$ $\xi,$ and $t$ for which $x \gt 2(1+t)/(\xi t^2),$ the number $u = \xi t x - 2 = 2/t$ is positive.  Bounding the exponential $e^u$ below by the first three terms of its Taylor series $1+u+u^2/2!+\cdots$ yields
$$\eqalign{
\exp(\xi t x-2) &\gt 1 + \xi t x - 2 + \frac{(\xi t x - 2)^2}{2!} \\
&= 1 + \xi x(-t + \xi t^2 x/2) \\
&\gt 1 + \xi x(-t + (1+t)) \\
&= 1 + \xi x.
}$$
Upon taking logs and dividing by $\xi$ we obtain
$$t x \gt \frac{2}{\xi} + \frac{1}{\xi} \log(1 + \xi x).$$
For notational convenience write $a = \max(0, 2(1-t)/(\xi t^2)).$ Integration by parts, simple bounding arguments, and judicious introduction of the preceding inequality show
$$\eqalign{
\int_{\mathbb R} e^{tz} \mathrm{d}F_\xi(z) &\ge \int_0^\infty e^{tz} \mathrm{d}F_\xi(z) \\
&= \int_\infty^0 e^{tz}\mathrm{d}(1-F_\xi(z)) \\
&= \lim_{z\to\infty}\left[(e^{tz} (1-F_\xi(z)))\left|_z^0\right. + t\int_0^z e^{tx}(1-F_\xi(x))\mathrm{d}x\right] \\
&\ge t \lim_{z\to\infty}\int_0^z e^{tx}(1-F_\xi(x))\mathrm{d}x \\
&=  t \lim_{z\to\infty}\int_0^z \exp\left(tx -\frac{1}{\xi}\log(1+\xi x)\right)\mathrm{d}x \\
&\ge t \lim_{z\to\infty}\int_a^z \exp\left(tx -\frac{1}{\xi}\log(1+\xi x)\right)\mathrm{d}x \\
&\gt  t \lim_{z\to\infty}\int_a^z \exp\left(\frac{2}{\xi}\right)\mathrm{d}x \\
&= t \exp\left(\frac{2}{\xi}\right) \lim_{z\to\infty} \int_a^z \mathrm{d}x,
}$$
which diverges, QED.
A: Maybe the ambiguity is between the tail and the domain of attraction
as related to the Fisher-Tippett Gnedenko theorem.
The theorem defines three Domains of Attraction (DA): Fréchet, Gumbel
and Weibull, and moreover within each of the two DA Weibull and
Fréchet, the tails can be ordered according to a tail index $\xi$
which corresponds to the shape parameter of a Generalized Extreme
Value (GEV) distribution or that of the Generalized Pareto (GP)
distribution. While the domain attraction of a distribution is determined by the tail, the converse is not true: a DA
contains distributions with different "tail thicknesses" in a
reasonable acceptance of this expression.
We can say that two continuous distributions with survival functions
$S_X(x)$ and $S_Y(x)$ are  tail-equivalent if the two
distributions share the same upper end-point $\omega$ (possibly $\infty$) and if moreover
$$
  S_X(x) \underset{x \to \omega}{\sim} a \,S_{Y}(x)
$$
for some finite positive number $a$. If a distribution is
tail-equivalent to the GEV or GP distribution with shape $\xi$ , then
it belongs to the domain of attraction of the GEV with shape
$\xi$. For instance the exponential and Gumbel distributions are
tail-equivalent. However, the converse is not true: distributions
within the same domain of attraction or even with the same tail index
are generally not tail-equivalent.  For example the normal
distribution or the gamma distribution with a shape $\neq 1$ are not
tail-equivalent to the GP distribution with shape $\xi = 0$ i.e. to
the exponential distribution. Yet both of these distributions are in
the domain of attraction of the GEV distribution with $\xi = 0$
i.e. the Gumbel domain of attraction. Within this domain, we find
quite different tails, some being "heavier" than the
exponential/Gumbel - such as a Gamma with shape $< 1$, a Weibull with
shape $>1$ or a log-normal - and some being "lighter" - such as a Gamma
with shape $> 1$.
