In this article we consider the efficient estimation of the tail distribution of the maximum of correlated normal random variables. We show that the currently recommended Monte Carlo estimator has difficulties in quantifying its precision, because its sample variance estimator is an inefficient...

It is already known, under certain conditions including stochastic inequalities, the comparison of moments. In this paper, we will study in detail the reverse of this problem, that is, the stochastic orderings implied by moments inequalities. We will limit our study to the absolute value of...

This paper studies the approximation of extreme quantiles of random sums of heavy-tailed random variables, or more specifically, subexponential random variables. A key application of this approximation is the calculation of operational VaR (value at risk) for financial institutions, to determine...