Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 2015-12-15
Number: 15-132/III
Author-Name: Zdravko Botev
Author-Workplace-Name: The University of New South Wales, Sydney, Australia
Author-Name: Michel Mandjes
Author-Workplace-Name: University of Amsterdam, the Netherlands
Author-Name: Ad Ridder
Author-Workplace-Name: VU University Amsterdam, the Netherlands
Title: Tail Distribution of the Maximum of Correlated Gaussian Random Variables
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.
Classification-JEL: C61, C63
Keywords: Rare event simulation, Correlated Gaussian, Tail probabilities,  Sequential importance sampling
File-Url: https://papers.tinbergen.nl/15132.pdf
File-Format: application/pdf
File-Size: 310601 bytes
Handle: RePEc:tin:wpaper:20150132