How to Get Bounds for Distribution Convolutions? A Simulation Study and an Application to Risk Management
14 Pages Posted: 26 Nov 2007 Last revised: 3 Apr 2009
Date Written: September 10, 2000
Abstract
In this paper, we consider the problem of bounds for distribution convolutions and we present some applications to risk management. We show that the upper Frechet bound is not always the more risky dependence structure. It is in contradiction with the belief in finance that maximal risk corresponds to the case where the random variables are comonotonic.
Keywords: Triangle functions, dependency bounds, infimal, supremal and sigma-convolutions, Makarov inequalities, Value-at-Risk, square root rule, Dall'aglio problem, Kantorovich distance
JEL Classification: G00
Suggested Citation: Suggested Citation
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