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Mean square rate of convergence for random walk approximation of forward-backward SDEs

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abstract

Let (Y, Z) denote the solution to a forward-backward SDE. If one constructs a random walk B n from the underlying Brownian motion B by Skorohod embedding, one can show L 2 convergence of the corresponding solutions (Y n , Z n) to (Y, Z). We estimate the rate of convergence in dependence of smoothness properties, especially for a terminal condition function in C 2,$\alpha$. The proof relies on an approximative representation of Z n and uses the concept of discretized Malliavin calculus. Moreover, we use growth and smoothness properties of the PDE associated to the FBSDE as well as of the finite difference equations associated to the approximating stochastic equations. We derive these properties by stochastic methods.

fields

math.PR 1

years

2019 1

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ACCEPT 1

representative citing papers

Donsker-Type Theorem for BSDEs: Rate of Convergence

math.PR · 2019-08-03 · accept · novelty 7.0

The random walk approximation for Markovian BSDEs converges in Wasserstein distance at rate n^{-(α∧ε/2)}, improving n^{-ε/4} and reaching the CLT-optimal n^{-1/2} for Lipschitz data.

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  • Donsker-Type Theorem for BSDEs: Rate of Convergence math.PR · 2019-08-03 · accept · none · ref 9 · internal anchor

    The random walk approximation for Markovian BSDEs converges in Wasserstein distance at rate n^{-(α∧ε/2)}, improving n^{-ε/4} and reaching the CLT-optimal n^{-1/2} for Lipschitz data.