Higher-order adaptive methods for exit times of It\^o diffusions

TL;DR

This paper introduces higher-order adaptive numerical methods for accurately approximating exit times of Itô SDEs, combining adaptive step-sizing with advanced schemes to improve precision and efficiency.

Contribution

The paper develops two novel higher-order adaptive methods using Milstein and Itô--Taylor schemes, with proven error bounds and computational complexity analysis.

Findings

Strong error bounds of (h^{1-\u03b5}) and (h^{3/2-}) for the two methods

Expected computational cost of (h^{-1}  ((h^{-1})))

Numerical experiments support theoretical convergence rates.

Abstract

We construct a higher-order adaptive method for strong approximations of exit times of It\^o stochastic differential equations (SDE). The method employs a strong It\^o--Taylor scheme for simulating SDE paths, and adaptively decreases the step-size in the numerical integration as the solution approaches the boundary of the domain. These techniques turn out to complement each other nicely: adaptive time-stepping improves the accuracy of the exit time by reducing the magnitude of the overshoot of the numerical solution when it exits the domain, and higher-order schemes improve the approximation of the state of the diffusion process. We present two versions of the higher-order adaptive method. The first one uses the Milstein scheme as numerical integrator and two step-sizes for adaptive time-stepping: $h$ when far away from the boundary and $h^2$ when close to the boundary. The second method is an extension of the first one using the strong It\^o--Taylor scheme of order 1.5 as numerical integrator and three step-sizes for adaptive time-stepping. For any $\xi>0$, we prove that the strong error is bounded by $\mathcal{O}(h^{1-\xi})$ and $\mathcal{O}(h^{3/2-\xi})$ for the first and second method, respectively, and the expected computational cost for both methods is $\mathcal{O}(h^{-1} \log(h^{-1}))$. Theoretical results are supported by numerical examples, and we discuss the potential for extensions that improve the strong convergence rate even further.