Convolution and convolution-root properties of long-tailed distributions

TL;DR

This paper explores the properties of long-tailed distributions under convolution operations, introduces new classes of heavy-tailed distributions, and demonstrates that certain properties are not preserved under convolution roots, challenging existing conjectures.

Contribution

It introduces two new classes of heavy-tailed distributions that are not long-tailed and analyzes their properties, providing counterexamples to a conjecture about convolution roots.

Findings

Long-tailed and generalized subexponential properties are not preserved under convolution roots.

Examples show infinitely divisible distributions can belong to both classes without their Levy measures being long-tailed.

The paper disproves a conjecture by Embrechts and Goldie regarding these distribution classes.

Abstract

We obtain a number of new general properties, related to the closedness of the class of long-tailed distributions under convolutions, that are of interest themselves and may be applied in many models that deal with "plus" and/or "max" operations on heavy-tailed random variables. We analyse the closedness property under convolution roots for these distributions. Namely, we introduce two classes of heavy-tailed distributions that are not long-tailed and study their properties. These examples help to provide further insights and, in particular, to show that the properties to be both long-tailed and so-called "generalised subexponential" are not preserved under the convolution roots. This leads to a negative answer to a conjecture of Embrechts and Goldie [10, 12] for the class of long-tailed and generalised subexponential distributions. In particular, our examples show that the following is possible: an infinitely divisible distribution belongs to both classes, while its Levy measure is neither long-tailed nor generalised subexponential.