Covariance Identities and Variance Bounds for Infinitely Divisible Random Variables and Their Applications

TL;DR

This paper develops covariance identities and variance bounds for infinitely divisible distributions, with applications to various distributions, including CGMY and variance-gamma, and introduces new, simpler Stein-type identities.

Contribution

It introduces a general covariance identity for IDD, derives new variance bounds, and simplifies Stein-type identities for complex distributions like CGMY and VGD.

Findings

Established a covariance identity for IDD.

Derived variance bounds for important distributions.

Presented simplified Stein-type identities for CGMY and VGD.

Abstract

In this article, we establish a general covariance identity for infinitely divisible distributions (IDD). Using this result, we derive Cacoullos type variance bounds for the IDD. Applications to some important distributions are discussed, in addition to the computation of variance bounds for certain posterior distributions. As another application, we derive the Stein-type identity for the IDD, which involves the L'evy measure. This result in turn is used to derive the Stein-type identity for the CGMY distributions and the variance-gamma distributions (VGD). This approach, especially for the VGD is new and simpler, compared to the ones available in the literature. Finally, as another nontrivial application, we apply the covariance identity in deriving known and some new formulas for the weighted premium calculation principles (WPCP) and Gini coefficient for the IDD.