Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/130486
Authors: 
Botev, Zdravko
Mandjes, Michel
Ridder, Ad
Year of Publication: 
2015
Series/Report no.: 
Tinbergen Institute Discussion Paper 15-132/III
Abstract: 
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 estimator of the true variance. We propose a simple remedy: to still use this estimator, but to rely on an alternative quantification of its precision. In addition to this we also consider a completely new sequential importance sampling estimator of the desired tail probability. Numerical experiments suggest that the sequential importance sampling estimator can be significantly more efficient than its competitor.
Subjects: 
Rare event simulation
Correlated Gaussian
Tail probabilities
Sequential importance sampling
JEL: 
C61
C63
Document Type: 
Working Paper

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