Weak Error for Nested Multilevel Monte Carlo

Daphné Giorgi (), Vincent Lemaire () and Gilles Pagès ()
Additional contact information
Daphné Giorgi: Sorbonne Université, CNRS, Laboratoire de Probabilités, Statistiques et Modélisations (LPSM)
Vincent Lemaire: Sorbonne Université, CNRS, Laboratoire de Probabilités, Statistiques et Modélisations (LPSM)
Gilles Pagès: Sorbonne Université, CNRS, Laboratoire de Probabilités, Statistiques et Modélisations (LPSM)

Methodology and Computing in Applied Probability, 2020, vol. 22, issue 3, 1325-1348

Abstract: Abstract This article discusses MLMC estimators with and without weights, applied to nested expectations of the form Ef(EF(Y,Z)|Y ). More precisely, we are interested on the assumptions needed to comply with the MLMC framework, depending on whether the payoff function f is smooth or not. A new result to our knowledge is given when f is not smooth in the development of the weak error at an order higher than 1, which is needed for a successful use of MLMC estimators with weights.

Keywords: Multilevel Monte Carlo; Weighted multilevel Monte Carlo; Nested Monte Carlo; Weak error expansion; Primary 65C05; Secondary 65C30 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s11009-019-09751-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:metcap:v:22:y:2020:i:3:d:10.1007_s11009-019-09751-3

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/11009

DOI: 10.1007/s11009-019-09751-3

Access Statistics for this article

Methodology and Computing in Applied Probability is currently edited by Joseph Glaz

More articles in Methodology and Computing in Applied Probability from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().