Efficient Simulation of Tail Probabilities of Sums of Dependent Random Variables

Abstract

We study asymptotically optimal simulation algorithms for approximating the tail probability of P(e1 + · · · + ed > u) as u → ∞. The first algorithm proposed is based on Conditional Monte Carlo and assumes that (X1, . . . , Xd) has an elliptical distribution with very mild assumptions on the radial component. This algorithm is applicable to a large class of… (More)

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