Maximum Likelihood Estimations of SDE Dynamics Based on Discrete Time Data How well does the Euler Method Perform?
AbstractThis paper employs maximum likelihood (ML) estimations to obtain parameters for stochastic differential equations (SDE). Three discretization methods for approximating SDE solutions are applied in the maximum likelihood estimations: the Euler method, the Milstein method and the Ozaki method. A ML estimation based on continuous time data serves as benchmark model for the theoretical treatment of the SDE parameter estimation. It can be approximated by the ML estimation using the Euler method as the observation steps become finer and finer. The performances of the ML estimations using the three discretization methods are compared and evaluated by using the example of \ a SDE model for the short-term-interest-rate. As an evaluation criterion we take the errors of the one-step-ahead predictions. We show that the predictions of the Euler method and of the Ozaki method are equivalent in estimating the parameters of the SDE process of the short-time-interest-rate. Numerically the magnitude of the prediction errors of the Euler method and the Milstein method are quite similarly. As it turns out the Euler method is not inferior to the other two methods for our chosen performance criterion.
Download InfoTo our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
Bibliographic InfoPaper provided by Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance in its series CeNDEF Workshop Papers, January 2001 with number 3A.3.
Date of creation: 04 Jan 2001
Date of revision:
Contact details of provider:
Postal: Dept. of Economics and Econometrics, Universiteit van Amsterdam, Roetersstraat 11, NL - 1018 WB Amsterdam, The Netherlands
Phone: + 31 20 525 52 58
Fax: + 31 20 525 52 83
Web page: http://www.fee.uva.nl/cendef/
More information through EDIRC
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum).
If references are entirely missing, you can add them using this form.