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

Higham, Desmond and Roj, Mikolaj; Laroque, C. and Himmelspach, J. and Pasupathy, R. and Rose, O. and Uhrmacher, A.M., eds. (2012) Computing mean first exit times for stochastic processes using multi-level Monte Carlo. In: Proceedings of the 2012 Winter Simulation Conference. IEEE. (https://doi.org/10.1109/WSC.2012.6465219)

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Abstract

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.

ORCID iDs

Higham, Desmond ORCID logoORCID: https://orcid.org/0000-0002-6635-3461 and Roj, Mikolaj; Laroque, C., Himmelspach, J., Pasupathy, R., Rose, O. and Uhrmacher, A.M.
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