In a satisfying way, it turns out that there are only so many shapes that probability densities can assume as they head off toward infinity. Extreme value theory makes this notion precise, and gives us some tools to work with them. Important application: understanding the kinds of heavy tailed variables we can observe in nature.

See also densities and intensities, survival analysis.

π

\[\renewcommand{\var}{\operatorname{Var}} \renewcommand{\dd}{\mathrm{d}} \renewcommand{\bb}[1]{\mathbb{#1}} \renewcommand{\vv}[1]{\boldsymbol{#1}} \renewcommand{\rv}[1]{\mathsf{#1}} \renewcommand{\gvn}{\mid} \renewcommand{\Ex}{\mathbb{E}} \renewcommand{\Pr}{\mathbb{P}}\]

## Tail limit theorems

The main result of use to our ends from EVT is the Pickands-Balkema-de Haan theorem (Balkema and de Haan 1974; Pickands III 1975).

This tells us that we can find a function \(\beta(u)\) such that
\[\lim _{u \rightarrow t_{T}} \sup _{0 \leq t<t_{T}-u}\left|T_{u}(t)-G_{\nu, \beta(u),0}(t)\right|=0\]
if (and only if) \(T\) is in the *maximal domain of attraction* of the
extreme value distribution with parameter \(\nu\) for some \(\nu\in\bb{R}\).

This maximal domain of attraction was introduced in the Fisher-Tippett theorem (Fisher and Tippett 1928), and is analysed in the EVT literature (e.g. Embrechts, Kluppelberg, and Mikosch 1997). It is pretty hard to find a distribution that does not fit in the MDA. I should try.

Practically this means that for many purposes, the tails of a random variable ( *may as well* be assumed to be a GPD,
\(\rv{t}\sim G_{\nu,\beta,\mu}(t) := 1-\left(1+\frac{\nu (t-\mu)}{\beta}\right)^{-1/\nu}.\)
Then for \(t> s\geq 0\) and assuming that \(G_{\nu,\beta,\mu}(s)>0,\) the
survival probability over an interval \((s,t]\) is \[\begin{aligned}
\Pr[\rv{t}\geq t\gvn \rv{t}> s]
&=\frac{\Pr[\rv{t}\geq t\cap \rv{t}> s]}{\Pr[\rv{t}> s]}\\
&=\frac{\Pr[\rv{t}\geq t]}{\Pr[\rv{t}> s]}\\
&=\frac{\bar{G}_{\nu,\beta,\mu}(t)}{\bar{G}_{\nu,\beta,\mu}(s)}\\
&=\frac{\left(1+\frac{\nu (t-\mu)}{\beta}\right)^{-1/\nu}}{\left(1+\frac{\nu (s-\mu)}{\beta}\right)^{-1/\nu}}\\
&=\left(\frac{\beta+\nu (s-\mu)}{\beta+\nu (t-\mu)}\right)^{1/\nu}.\label{eq:gpd-survival-prob}
\end{aligned}\]

## Generalized Pareto Distribution

Best intro from Hosking and Wallis (1987):

The generalized Pareto distribution is the distribution of a random variable \(\rv{x}\) defined by \(\rv{x}= \alpha\left(1-e^{-k \rv{y}}\right) / k,\) where \(\rv{y}\) is a random variable with the standard exponential distribution. The generalized Pareto distribution has distribution function

\[ \begin{aligned} F(x) &=1-(1-k x / \alpha)^{1 / k}, & & k \neq 0 \\ &=1-\exp (-x / \alpha), & & k=0 \end{aligned} \] and density function

\[ \begin{aligned} f(x) &=\alpha^{-1}(1-k x / \alpha)^{1 / k-1}, & & k \neq 0 \\ &=\alpha^{-1} \exp (-x / \alpha), & & k=0 \end{aligned} \] the range of \(x\) is \(0 \leq x<\infty\) for \(k \leq 0\) and \(0 \leq x \leq \alpha / k\) for \(k>0 .\) The parameters of the distribution are \(\alpha,\) the scale parameter, and \(k,\) the shape parameter. The special cases \(k=0\) and \(k=1\) yield, respectively, the exponential distribution with mean \(\alpha\) and the uniform distribution on \([0, \alpha] ;\) Pareto distributions are obtained when \(k<0 .\)

and

- The failure rate \(r(x)=f(x) /\{1-F(x)\}\) is given by \(r(x)=1 /(\alpha-k x)\) and is monotonic in \(x,\) decreasing if \(k<0,\) constant if \(k=0,\) and increasing if \(k>0\)
- If the random variable \(X\) has a generalized Pareto distribution, then the conditional distribution of \(X-t\) given \(X \geq t\) is also generalized Pareto, with the same value of \(k\)
- Let \(Z=\max \left(0, X_{1}, \ldots, X_{N}\right),\) where the \(X_{i}\) are independent and identically distributed as (1) and \(N\) has a Poisson distribution. Then \(Z\) has, essentially, a generalized extreme value (GEV) distribution as defined by Jenkinson (1955); that is, there exist quantities \(\beta, \gamma,\) and \(\delta,\) independent of \(z,\) such that
\[ \begin{aligned} F_{Z}(z) &=\operatorname{Pr}(Z \leq z) \\ &=\exp \left[-\{1-\delta(z-\gamma) / \beta\}^{1 / \delta}\right], \quad z \geq 0 \end{aligned} \] furthermore, \(\delta=k ;\) that is, the shape parameters of the GEV and the GPD are equal.

## Generalized Extreme Value distributions

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## Burr distribution

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## References

*The Annals of Probability*2 (5): 792β804.

*PLOS ONE*13 (5): e0196456.

*Journal of the American Statistical Association*92 (440): 1609β20.

*arXiv:1912.11736 [Econ, Stat]*, December.

*Mathematical Geology*21 (8): 829β42.

*Statistical Extremes and Applications*, edited by J. Tiago de Oliveira, 461β82. NATO ASI Series. Dordrecht: Springer Netherlands.

*Extremal Events in Finance and Insurance*. Springer Berlin Heidelberg.

*Modelling Extremal Events*, 21β57. Applications of Mathematics 33. Springer Berlin Heidelberg.

*Mathematical Proceedings of the Cambridge Philosophical Society*24 (2): 180β90.

*Journal of Risk and Financial Management*11 (1): 10.

*Journal of the Royal Statistical Society: Series B (Methodological)*38 (3): 257β64.

*Technometrics*35 (2): 185β91.

*Technometrics*29 (3): 339β49.

*Communications in Statistics - Theory and Methods*40 (14): 2500β2510.

*Communications in Statistics - Theory and Methods*48 (8): 2014β38.

*The Journal of Operational Risk*1 (2): 51β57.

*Computer Networks*40 (3): 459β74.

*ASTIN Bulletin: The Journal of the IAA*27 (1): 117β37.

*Quantitative Risk Management : Concepts, Techniques and Tools*. Princeton: Princeton Univ. Press.

*arXiv:1802.00762 [Math]*, February.

*Annual Review of Statistics and Its Application*8 (1): 217β40.

*The Annals of Statistics*3 (1): 119β31.

*Biometrika*72 (1): 67β90.

*Scandinavian Actuarial Journal*1951 (1-2): 158β75.

*Institute of Mathematical Statistics Lecture Notes - Monograph Series*, 272β83. Beachwood, Ohio, USA: Institute of Mathematical Statistics.

*Mathematics*7 (5): 406.

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