Computing mean first exit times for stochastic processes using multi-level Monte Carlo

Desmond Higham, Mikolaj Roj, C. Laroque (Editor), J. Himmelspach (Editor), R. Pasupathy (Editor), O. Rose (Editor), A.M. Uhrmacher

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

The multi-level approach developed by Giles (2008) can be used to estimate mean first exit times for stochastic differential equations, which are of interest in finance, physics and chemical kinetics. Multi-level improves the computational expense of standard Monte Carlo in this setting by an order of magnitude. More precisely, for a target accuracy of TOL, so that the root mean square error of the estimator is O(TOL), the O(TOL-4) cost of standard Monte Carlo can be reduced to O(TOL-3|log(TOL)|1/2) with a multi-level scheme. This result was established in Higham, Mao, Roj, Song, and Yin (2013), and illustrated on some scalar examples. Here, we briefly overview the algorithm and present some new computational results in higher dimensions.
Original languageEnglish
Title of host publicationProceedings of the 2012 Winter Simulation Conference (WSC)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)978-1-4673-4779-2
Publication statusPublished - 2012

Keywords / Materials (for Non-textual outputs)

  • complexity theory
  • networking
  • multi-level Monte Carlo method
  • computational efficiency


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