Journal of Computational Finance

Finite sample comparison of alternative estimators of Itô diffusion processes: a Monte Carlo study

George J. Jiang, John L. Knight


In this paper, the authors consider alternative approaches to the estimation of Itô diffusion processes from discretely sampled observations. Using Monte Carlo simulation, the finite sample properties of various estimators are investigated, and in particular the performance of the nonparametric estimators proposed in Jiang and Knight (1997) is compared with common parametric estimators, namely the ML, NLS (or OLS), and GMM estimators. The simulation results show that, with certain large samples over a short sampling period, both the nonparametric diffusion and drift estimators perform reasonably well. However, while all the parametric diffusion estimators perform very well, the parametric drift estimators perform very poorly.

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