Arbeitspapier
Tail Distribution of the Maximum of Correlated Gaussian Random Variables
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Tinbergen Institute Discussion Paper ; No. 15-132/III
- Klassifikation
-
Wirtschaft
Optimization Techniques; Programming Models; Dynamic Analysis
Computational Techniques; Simulation Modeling
- Thema
-
Rare event simulation
Correlated Gaussian
Tail probabilities
Sequential importance sampling
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Botev, Zdravko
Mandjes, Michel
Ridder, Ad
- Ereignis
-
Veröffentlichung
- (wer)
-
Tinbergen Institute
- (wo)
-
Amsterdam and Rotterdam
- (wann)
-
2015
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Botev, Zdravko
- Mandjes, Michel
- Ridder, Ad
- Tinbergen Institute
Entstanden
- 2015
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