Finite Sample Comparison of Alternative Estimators of Itô Diffusion Processes -- A Monte Carlo Study

In this paper, we consider alternative approaches to the estimation of Itˆo diffusion processes from discretely sampled observations. Based on Monte Carlo simulation, we investigate the finite sample properties of various estimators and in particular compare the performance of the nonparametric estimators proposed in Jiang and Knight (1997) 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