Mean square rate of convergence for random walk approximation of forward-backward SDEs
Geiss, C., Labart, C., & Luoto, A. (2020). Mean square rate of convergence for random walk approximation of forward-backward SDEs. Advances in Applied Probability, 52(3), 735-771. https://doi.org/10.1017/apr.2020.17
Published inAdvances in Applied Probability
© Applied Probability Trust 2020
Let (Y, Z) denote the solution to a forward-backward stochastic differential equation (FBSDE). If one constructs a random walk from the underlying Brownian motion B by Skorokhod embedding, one can show -convergence of the corresponding solutions to We estimate the rate of convergence based on smoothness properties, especially for a terminal condition function in . The proof relies on an approximative representation of and uses the concept of discretized Malliavin calculus. Moreover, we use growth and smoothness properties of the partial differential equation associated to the FBSDE, as well as of the finite difference equations associated to the approximating stochastic equations. We derive these properties by probabilistic methods.
PublisherCambridge University Press (CUP)
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