General diffusion processes as the limit of time-space Markov chains

Abstract

We prove the convergence of the law of grid-valued random walks, which can be seen as time-space Markov chains, to the law of a general diffusion process. This includes processes with sticky features, reflecting or absorbing boundaries and skew behavior. We prove that the convergence occurs at any rate strictly inferior to $(1/4)\wedge (1/p)$ in terms of the maximum cell size of the grid, for any $p$-Wasserstein distance. We also show that it is possible to achieve any rate strictly inferior to $(1/2)\wedge (2/p)$ if the grid is adapted to the speed measure of the diffusion, which is optimal for $p\le 4$. This result allows us to set up asymptotically optimal approximation schemes for general diffusion processes. Last, we experiment numerically on diffusions that exhibit various features.

References in corpus (2)

• On the constructions of the skew Brownian motion
• Functional convergence to the local time of a sticky diffusion

Cited by in corpus (1)

• Pricing and hedging for a sticky diffusion