Revisiting Grüss’s Inequality : Covariance Bounds, QDE but not QD Copulas, and Central Moments

Since the pioneering work of Gerhard Grüss dating back to 1935, Grüss’s inequality and, more generally, Grüss-type bounds for covariances have fascinated researchers and found numerous applications in areas such as economics, insurance, reliability, and, more generally, decision making under uncertainly. Grüss-type bounds for covariances have been established mainly under most general dependence structures, meaning no restrictions on the dependence structure between the two underlying random variables. Recent work in the area has revealed a potential for improving Grüss-type bounds, including the original Grüss’s bound, assuming dependence structures such as quadrant dependence (QD). In this paper we demonstrate that the relatively little explored notion of ‘quadrant dependence in expectation’ (QDE) is ideally suited in the context of bounding covariances, especially those that appear in the aforementioned areas of application. We explore this research avenue in detail, establish general Grüss-type bounds, and illustrate them with newly constructed examples of bivariate distributions, which are not QD but, nevertheless, are QDE. The examples rely on specially devised copulas. We supplement the examples with results concerning general copulas and their convex combinations. In the process of deriving Grüss-type bounds, we also establish new bounds for central moments, whose optimality is demonstrated