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

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Botev, Zdravko
  • Mandjes, Michel
  • Ridder, Ad
  • Tinbergen Institute

Entstanden

  • 2015

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