Multilevel Monte Carlo method with applications to stochastic partial differential equations
Journal article, 2012
In this work, the approximation of Hilbert-space-valued random variables is combined with the approximation of the expectation by a multilevel Monte Carlo (MLMC) method. The number of samples on the different levels of the multilevel approximation are chosen such that the errors are balanced. The overall work then decreases in the optimal case to O(h2) if h is the error of the approximation. The MLMC method is applied to functions of solutions of parabolic and hyperbolic stochastic partial differential equations as needed, for example, for option pricing. Simulations complete the paper. © 2012 Copyright Taylor and Francis Group, LLC.
stochastic partial differential equations
multilevel Monte Carlo
stochastic finite element methods
stochastic parabolic equation
stochastic hyperbolic equation