Publication Type
Working Paper
Version
publishedVersion
Publication Date
12-2006
Abstract
This paper motivates and introduces a two-stage method of estimating diffusion processes based on discretely sampled observations. In the first stage we make use of the feasible central limit theory for realized volatility, as developed in [Jacod, J., 1994] and [Barndorff-Nielsen, O., Shephard, N., 2002], to provide a regression model for estimating the parameters in the diffusion function. In the second stage, the in-fill likelihood function is derived by means of the Girsanov theorem and then used to estimate the parameters in the drift function. Consistency and asymptotic distribution theory for these estimates are established in various contexts. The finite sample performance of the proposed method is compared with that of the approximate maximum likelihood method of [Aït-Sahalia, Y., 2002].
Keywords
Maximum likelihood, Girsnov theorem, Discrete sampling, Continuous record, Realized volatility
Discipline
Econometrics
Research Areas
Econometrics
Volume
29-2006
First Page
1
Last Page
27
Publisher
SMU Economics and Statistics Working Paper Series, No. 29-2006
City or Country
Singapore
Citation
PHILLIPS, Peter C. B. and YU, Jun.
A Two-Stage Realized Volatility Approach to Estimation of Diffusion Processes with Discrete Data. (2006). 29-2006, 1-27.
Available at: https://ink.library.smu.edu.sg/soe_research/946
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Comments
Published in Journal of Econometrics, 2009, https://doi.org/10.1016/j.jeconom.2008.12.006