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

Journal of Computational Finance, 1999 (2), Nov. 3, 1-34

41 Pages Posted: 2 Oct 2012

See all articles by George J. Jiang

George J. Jiang

Washington State University

John Knight

University of Western Ontario, Faculty of Social Science, Deparment of Economics (Deceased)

Date Written: Novermber 2, 1999

Abstract

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.

Keywords: Diffusion Process, Parametric Estimation, Nonparametric Estimation, Monte Carlo Simulation

JEL Classification: C14, C15, C22

Suggested Citation

Jiang, George and Knight, John L., Finite Sample Comparison of Alternative Estimators of Itô Diffusion Processes -- A Monte Carlo Study (Novermber 2, 1999). Journal of Computational Finance, 1999 (2), Nov. 3, 1-34, Available at SSRN: https://ssrn.com/abstract=2155952

George Jiang (Contact Author)

Washington State University ( email )

Department of Finance and Management Science
Carson College of Business
Pullman, WA 99-4746164
United States
509-3354474 (Phone)

HOME PAGE: http://directory.business.wsu.edu/bio.html?username=george.jiang

John L. Knight

University of Western Ontario, Faculty of Social Science, Deparment of Economics (Deceased)

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