65537 (number)

In probability theory, heavy-tailed distributions are probability distributions whose tails are not exponentially bounded: that is, they have heavier tails than the exponential distribution. In many applications it is the right tail of the distribution that is of interest, but a distribution may have a heavy left tail, or both tails may be heavy.

There are three important subclasses of heavy-tailed distributions, the fat-tailed distributions, the long-tailed distributions and the subexponential distributions. In practice, all commonly used heavy-tailed distributions belong to the subexponential class.

There is still some discrepancy over the use of the term heavy-tailed. There are two other definitions in use. Some authors use the term to refer to those distributions which do not have all their power moments finite; and some others to those distributions that do not have a finite variance. The definition given in this article is the most general in use, and includes all distributions encompassed by the alternative definitions, as well as those distributions such as log-normal that possess all their power moments, yet which are generally acknowledged to be heavy-tailed. (Occasionally, heavy-tailed is used for any distribution that has heavier tails than the normal distribution.)

Definition of heavy-tailed distribution

The distribution of a random variable X with distribution function F is said to have a heavy right tail if

$\underset{x\to \mathrm{\infty }}{\lim }{e}^{\lambda x}\mathrm{Pr}[X>x]=\mathrm{\infty }\text{for all }\lambda >0.$

This is also written in terms of the tail distribution function

$\bar{F}(x)\equiv \mathrm{Pr}[X>x]$

as

$\underset{x\to \mathrm{\infty }}{\lim }{e}^{\lambda x}\bar{F}(x)=\mathrm{\infty }\text{for all }\lambda >0.$

This is equivalent to the statement that the moment generating function of F, M_F(t), is infinite for all t > 0.

The definitions of heavy-tailed for left-tailed or two tailed distributions are similar.

Definition of long-tailed distribution

The distribution of a random variable X with distribution function F is said to have a long right tail if for all t > 0,

$\underset{x\to \mathrm{\infty }}{\lim }\mathrm{Pr}[X>x+t|X>x]=1,$

or equivalently

$\bar{F}(x+t)\sim \bar{F}(x)\text{as }x\to \mathrm{\infty }.$

This has the intuitive interpretation for a right-tailed long-tailed distributed quantity that if the long-tailed quantity exceeds some high level, the probability approaches 1 that it will exceed any other higher level: if you know the situation is good, it is probably better than you think.

All long-tailed distributions are heavy-tailed, but the converse is false, and it is possible to construct heavy-tailed distributions that are not long-tailed.

Subexponential distributions

Subexponentiality is defined in terms of convolutions of probability distributions. For two independent, identically distributed random variables $X_1,X_2$ with common distribution function $F$ the convolution of $F$ with itself, $F^{*2}$ is defined, using Lebesgue–Stieltjes integration, by:

$\mathrm{Pr}[X_1+X_2\le x]=F^{*2}(x)=\underset{-\mathrm{\infty }}{\overset{\mathrm{\infty }}{\int }}F(x-y)dF(y).$

The n-fold convolution $F^{*n}$ is defined in the same way. The tail distribution function $\bar{F}$ is defined as $\bar{F}(x)=1-F(x)$.

A distribution $F$ on the positive half-line is subexponential if

$\overline{F^{*2}}(x)\sim 2\bar{F}(x)\text{as }x\to \mathrm{\infty }.$

This implies that, for any $n\ge 1$,

$\overline{F^{*n}}(x)\sim n\bar{F}(x)\text{as }x\to \mathrm{\infty }.$

The probabilistic interpretation of this is that, for a sum of $n$ independent random variables $X_1,\dots ,X_n$ with common distribution $F$,

$\mathrm{Pr}[X_1+\cdots +X_n>x]\sim \mathrm{Pr}[\max (X_1,\dots ,X_n)>x]\text{as }x\to \mathrm{\infty }.$

This is often known as the principle of the single big jump or catastrophe principle.

A distribution $F$ on the whole real line is subexponential if the distribution $FI([0,\mathrm{\infty }))$ is. Here $I([0,\mathrm{\infty }))$ is the indicator function of the positive half-line. Alternatively, a random variable $X$ supported on the real line is subexponential if and only if $X^{+}=\max (0,X)$ is subexponential.

All subexponential distributions are long-tailed, but examples can be constructed of long-tailed distributions that are not subexponential.

Common heavy-tailed distributions

All commonly used heavy-tailed distributions are subexponential.

Those that are one-tailed include:

• the Pareto distribution;
• the Log-normal distribution;
• the Lévy distribution;
• the Weibull distribution with shape parameter less than 1;
• the Burr distribution;
• the log-gamma distribution;
• the log-Cauchy distribution, sometimes described as having a "super-heavy tail" because it exhibits logarithmic decay producing a heavier tail than the Pareto distribution.

Those that are two-tailed include:

• The Cauchy distribution, itself a special case of both the stable distribution and the t-distribution;
• The family of stable distributions, excepting the special case of the normal distribution within that family. Some stable distributions are one-sided (or supported by a half-line), see e.g. Lévy distribution. See also financial models with long-tailed distributions and volatility clustering.
• The t-distribution.
• The skew lognormal cascade distribution.

Relationship to fat-tailed distributions

A fat-tailed distribution is a distribution for which the probability density function, for large x, goes to zero as a power $x^{-a}$. Since such a power is always bounded below by the probability density function of an exponential distribution, fat-tailed distributions are always heavy-tailed. Some distributions however have a tail which goes to zero slower than an exponential function (meaning they are heavy-tailed), but faster than a power (meaning they are not fat-tailed). An example is the log-normal distribution. Many other heavy-tailed distributions such as the log-logistic and Pareto distribution are however also fat-tailed.

Estimating the tail-index

To estimate the tail-index, we could estimate the GEV distribution or Pareto distribution parameters on data using the maximum-likelihood estimation (MLE).

Pickands tail-index

With $(X_n,n\ge 1)$ a random sequence of independent and same density function $F\in D(H(\xi ))$, the Maximum Attraction Domain of the generalized extreme value density $H$, where $\xi \in \mathbb{ℝ}$. If $\underset{n\to \mathrm{\infty }}{\lim }k(n)=\mathrm{\infty }$ and $\underset{n\to \mathrm{\infty }}{\lim }\frac{k(n)}{n}=0$, then the Pickands tail-index estimation is :

${\xi }_{(k(n),n)}^{Pickands}=\frac{1}{\ln 2}\ln \left(\frac{X_{(n-k(n)+1,n)}-X_{(n-2k(n)+1,n)}}{X_{(n-2k(n)+1,n)}-X_{(n-4k(n)+1,n)}}\right)$

where $X_{(n-k(n)+1,n)}=\max (X_{n-k(n)+1},\dots ,X_n)$. This estimator converge in probability to $\xi$.

Hill tail-index

With $(X_n,n\ge 1)$ a random sequence of independent and same density function $F\in D(H(\xi ))$, the Maximum Attraction Domain of the generalized extreme value density $H$, where $\xi \in \mathbb{ℝ}$. If $\underset{n\to \mathrm{\infty }}{\lim }k(n)=\mathrm{\infty }$ and $\underset{n\to \mathrm{\infty }}{\lim }\frac{k(n)}{n}=0$, then the Hill tail-index estimation is :

${\xi }_{(k(n),n)}^{Hill}=\frac{1}{k(n)}\sum _{i=n-k(n)+1}^n\ln (X_{(i,n)})-\ln (X_{(n-k(n)+1,n)})$

where $X_{(n-k(n)+1,n)}=\max (X_{n-k(n)+1},\dots ,X_n)$. This estimator converge in probability to $\xi$.

Software

• aest, C tool for estimating the heavy tail index

See also

• Fat tail
• Leptokurtic
• Outlier
• The Long Tail
• Power law