On non-asymptotic optimal stopping criteria in Monte Carlo Simulations

On non-asymptotic optimal stopping criteria in Monte Carlo Simulations

Christian Bayer, Hakon Hoel, Erik Von Schwerin, and Raul Tempone, On non-asymptotic optimal stopping criteria in Monte Carlo Simulations, SIAM Journal on Scientific Computing, Volume 36, Issue 2, 2014
Christian Bayer, Hakon Hoel, Erik Von Schwerin, and Raul Tempone
Monte Carlo methods, optimal stopping, sequential stopping rules, non-asymptotic
2014

We consider the setting of estimating the mean of a random variable by a sequential stopping rule Monte Carlo (MC) method. The performance of a typical second moment based sequential stopping rule MC method is shown to be unreliable in such settings both by numerical examples and through analysis. By analysis and approximations, we construct a higher moment based stopping rule which is shown in numerical examples to perform more reliably and only slightly less efficiently than the second moment based stopping rule.

ISSN (print): 1064-8275