Article2015

Unbiased Estimation with Square Root Convergence for SDE Models

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Stochastic processes and financial applications Markov Chains and Monte Carlo Methods Mathematical Approximation and Integration

Abstract

In many settings in which Monte Carlo methods are applied, there may be no known algorithm for exactly generating the random object for which an expectation is to be computed. Frequently, however, one can generate arbitrarily close approximations to the random object. We introduce a simple randomization idea for creating unbiased estimators in such a setting based on a sequence of approximations. Applying this idea to computing expectations of path functionals associated with stochastic differential equations (SDEs), we construct finite variance unbiased estimators with a “square root convergence rate” for a general class of multidimensional SDEs. We then identify the optimal randomization distribution. Numerical experiments with various path functionals of continuous-time processes that often arise in finance illustrate the effectiveness of our new approach.

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References

2016Numerical solution of stochastic differential equations 2008Multilevel Monte Carlo Path Simulation 1992The Asymptotic Efficiency of Simulation Estimators 2007Improved Multilevel Monte Carlo Convergence using the Milstein Scheme 2014A continuation multilevel Monte Carlo algorithm

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