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Abstract
En 中文
We show that multigrid ideas can be used to reduce the computational complexity of estimating an expected value arising from a stochastic differential equation using Monte Carlo path simulations. In the simplest case of a Lipschitz payoff and a Euler discretisation, the computational cost to achieve an accuracy of O(ϵ) is reduced from O(ϵ−3) to O(ϵ−2 (log ϵ)2). The analysis is supported by numerical results showing significant computational savings.
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