Series Representation of Jointly S$\alpha$S Distribution via Symmetric Covariations

TL;DR

This paper introduces symmetric covariation as a new dependence measure for symmetric alpha-stable distributions, enabling a series representation of their joint characteristic functions that generalizes Gaussian cases.

Contribution

It defines symmetric covariation for all alpha in (0,2], derives its properties, and provides a convergent series representation of the joint characteristic function.

Findings

Symmetric covariation is well-defined for all alpha in (0,2].

Properties of symmetric covariation extend or generalize covariance properties.

Series representation of the joint characteristic function generalizes the Gaussian case.

Abstract

We introduce the notion of symmetric covariation, which is a new measure of dependence between two components of a symmetric $\alpha$-stable random vector, where the stability parameter $\alpha$ measures the heavy-tailedness of its distribution. Unlike covariation that exists only when $\alpha\in(1,2]$, symmetric covariation is well defined for all $\alpha\in(0,2]$. We show that symmetric covariation can be defined using the proposed generalized fractional derivative, which has broader usages than those involved in this work. Several properties of symmetric covariation have been derived. These are either similar to or more general than those of the covariance functions in the Gaussian case. The main contribution of this framework is the representation of the characteristic function of bivariate symmetric $\alpha$-stable distribution via convergent series based on a sequence of symmetric covariations. This series representation extends the one of bivariate Gaussian.