An efficient approximation for stochastic differential equations on the partition ofsymmetricalirst

Publisher Website
Google Scholar
Abstract
In certain applications of stochastic differential equations a numerical solution must be found corresponding to a particular sample path of the driving process. The order of convergence of approximations based on regular samples of the path is limited, and some approximations are asymptotically efficient in that they minimise the leading coefficient in the expansion of mean-square errors as power series in the sample step size. This paper considers approximations based on irregular samples taken at the passage times of the driving process through a series of thresholds. Such approximations can involve less computation than their regular sample counterparts, particularly for real-time applications. The orders of convergence of the Euler and Milshtein approximations are derived and a new approximation is defined which is asymptotically efficient with respect to the irregular samples. Its asymptotic mean-square error is less than half that of efficient approximations based on regular sample