  We introduce a simple method for nearly simultaneous computation of all
moments needed for quasi maximum likelihood estimation of parameters in
discretely observed stochastic differential equations commonly seen in finance.
The method proposed in this papers is not restricted to any particular dynamics
of the differential equation and is virtually insensitive to the sampling
interval. The key contribution of the paper is that computational complexity is
sublinear in the number of observations as we compute all moments through a
single operation. Furthermore, that operation can be done offline. The
simulations show that the method is unbiased for all practical purposes for any
sampling design, including random sampling, and that the computational cost is
comparable (actually faster for moderate and large data sets) to the simple,
often severely biased, Euler-Maruyama approximation.
