In probability and statistics, an elliptical distribution is any member of a broad family of probability distributions that generalize the multivariate normal distribution and inherit some of its properties.

## Definition

Elliptical distributions can be defined using characteristic functions. A multivariate distribution is said to be elliptical if its characteristic function is of the form[1]

$e^{it'\mu} \Psi(t' \Sigma t) \,$

for a specified vector $\mu$, positive-definite matrix $\Sigma$, and characteristic function $\Psi$. The function $\Psi$ is known as the characteristic generator of the elliptical distribution.[2]

Elliptical distributions can also be defined in terms of their density functions. When they exist, the density functions f have the structure:

$f(x)= k \cdot g((x-\mu)'\Sigma^{-1}(x-\mu))$

where $k$ is the scale factor, $x$ is an $n$-dimensional random vector with median vector $\mu$ (which is also the mean vector if the latter exists), $\Sigma$ is a positive definite matrix which is proportional to the covariance matrix if the latter exists, and $g$ is a function mapping from the non-negative reals to the non-negative reals giving a finite area under the curve.

## Properties

In the 2-dimensional case where the density exists, each iso-density locus (the set of x1,x2 pairs all giving a particular value of $f(x)$) is an ellipse (hence the name elliptical distribution). More generally, for arbitrary n the iso-density loci are ellipsoids.

The multivariate normal distribution is the special case in which $g(z)=e^{-z/2}$ for quadratic form $z$. While the multivariate normal is unbounded (each element of $x$ can take on arbitrarily large positive or negative values with non-zero probability, because $e^{-z/2}>0$ for all non-negative $z$), in general elliptical distributions can be bounded or unbounded—such a distribution is bounded if $g(z)=0$ for all $z$ greater than some value.

Because the index variable x enters the density function quadratically, all elliptical distributions are symmetric about $\mu.$

## Applications

Elliptical distributions are important in portfolio theory because, if the returns on all assets available for portfolio formation are jointly elliptically distributed, then all portfolios can be characterized completely by their location and scale — that is, any two portfolios with identical location and scale of portfolio return have identical distributions of portfolio return (Chamberlain 1983; Owen and Rabinovitch 1983). For multi-normal distributions, location and scale correspond to mean and standard deviation.

## References

1. ^ Stamatis Cambanis, Steel Huang, and Gordon Simons (1981). "On the Theory of Elliptically Contoured Distributions". Journal of Multivariate Analysis 11: 368–385. Text "." ignored (help)
2. ^ Härdle and Simar (2012), p. 178.
• Wolfgang Karl. Härdle and Lèopold Simar (2012). Applied Multivariate Statistical Analysis (3rd ed.). Springer. Text "." ignored (help)
• Fang, K. Kotz, S. and Ng., K. (1990). Symmetric Multivariate and Related Distributions. London: Chapman & Hall. Text "." ignored (help)
• McNeil, Alexander; Frey, Rüdiger; Embrechts, Paul (2005). Quantitative Risk Management. Princeton University Press. ISBN 0-691-12255-5.
• Chamberlain, G. (1983). "A characterization of the distributions that imply mean-variance utility functions", Journal of Economic Theory 29, 185-201. doi:10.1016/0022-0531(83)90129-1
• Landsman, Zinoviy M.; Valdez, Emiliano A. (2003) Tail Conditional Expectations for Elliptical Distributions (with discussion), The North American Actuarial Journal, 7, 55–123.
• Owen, J., and Rabinovitch, R. (1983). "On the class of elliptical distributions and their applications to the theory of portfolio choice", Journal of Finance 38, 745-752. JSTOR 2328079