Approximation of SDE solutions using local asymptotic expansions

We develop an asymptotic expansion for small time of the density of the solution of a non-degenerate system of stochastic differential equations with smooth coefficients, and apply this to the stepwise approximation of solutions. The asymptotic expansion, which takes the form of a multivariate Edgeworth-type expansion, is obtained from the Kolmogorov forward equation using some standard PDE results. To generate one step of the approximation to the solution, we use a Cornish–Fisher type expansion derived from the Edgeworth expansion. To interpret the approximation generated in this way as a strong approximation we use couplings between the (normal) random variables used and the Brownian path driving the SDE. These couplings are constructed using techniques from optimal transport and Vaserstein metrics. The type of approximation so obtained may be regarded as intermediate between a conventional strong approximation and a weak approximation. In principle approximations of any order can be obtained, though for higher orders the algebra becomes very heavy. In order 1/2 the method gives the usual Euler approximation; in order 1 it gives a variant of the Milstein method, but which needs only normal variables to be generated. However the method is somewhat limited by the non-degeneracy requirement.

Asymptotic Expansion of Quantiles Computed from Mixed Samples 1

This article describes an Edgeworth type expansion as n→∞ for the distribution of a quantile computed from a mixed sample of size nk, representing a finite number k of populations, a sample of size n being drawn from the jth population (l⩽ j⩽ k). If the sth ( s ≧ 3) derivative of each of the underlying k distribution functions exists in the neighbourhood of a suitably defined population quantile, then the error of this asymptotic expansion is of order smaller than n-(8-2)|2.