MLMC techniques for discontinuous functions

Conference item

Abstract:: The Multilevel Monte Carlo (MLMC) approach usually works well when estimating the expected value of a quantity which is a Lipschitz function of intermediate quantities, but if it is a discontinuous function it can lead to a much slower decay in the variance of the MLMC correction. This article reviews the literature on techniques which can be used to overcome this challenge in a variety of different contexts, and discusses recent developments using either a branching diffusion or adaptive sampling.

Files:: Giles_et_al_2024_MLMC_techniques_for.pdf

(Preview, Accepted manuscript, pdf, 222.9KB, Terms of use)

Publisher:: Springer
Host title:: Monte Carlo and Quasi-Monte Carlo Methods: MCQMC 2022, Linz, Austria, July 17–22
Pages:: 33-47
Series:: Springer Proceedings in Mathematics & Statistics
Series number:: 460
Place of publication:: Cham, Switzerland
Publication date:: 2024-07-13
Acceptance date:: 2023-02-15
Event title:: 15th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing (MCQMC 2022)
Event location:: Linz
Event website:: https://www.ricam.oeaw.ac.at/events/conferences/mcqmc2022/
Event start date:: 2022-07-17
Event end date:: 2022-07-22
DOI:: 10.1007/978-3-031-59762-6_2
EISSN:: 2194-1017
ISSN:: 2194-1009
EISBN:: 9783031597626
ISBN:: 9783031597619

Language:: English
Keywords:: discontinuous payoffs

nested expectation

multilevel Monte Carlo
Pubs id:: 1329057
Local pid:: pubs:1329057
Deposit date:: 2023-02-19

Copyright holder:: Michael Giles
Notes:: This is the accepted manuscript version of the article. The final version is available online from Springer at https://dx.doi.org/10.1007/978-3-031-59762-6_2

Licence:: Terms and Conditions of Use for Oxford University Research Archive

If you are the owner of this record, you can report an update to it here: Report update to this record