Higher-order adaptive methods for exit times of Itô diffusions

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. Under some conditions, we show that the expected computational cost of both methods are bounded by $\mathcal{O}(h^{-1} |\log(h)|)$, indicating that both methods are tractable. The theoretical results are supported by numerical examples, and we discuss the potential for extensions that improve the strong convergence rate even further.