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

Efficient Computation of the Quasi Likelihood function for Discretely Observed Diffusion Processes