  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.
