Publication Type
Journal Article
Version
submittedVersion
Publication Date
2004
Abstract
This paper reviews the method of model-fitting via the empirical characteristic function. The advantage of using this procedure is that one can avoid difficulties inherent in calculating or maximizing the likelihood function. Thus it is a desirable estimation method when the maximum likelihood approach encounters difficulties but the characteristic function has a tractable expression. The basic idea of the empirical characteristic function method is to match the characteristic function derived from the model and the empirical characteristic function obtained from data. Ideas are illustrated by using the methodology to estimate a diffusion model that includes a self-exciting jump component. A Monte Carlo study shows that the finite sample performance of the proposed procedure offers an improvement over a GMM procedure. An application using over 72 years of DJIA daily returns reveals evidence of jump clustering.
Keywords
Diffusion process; Poisson jump; Self-exciting; GMM; Jumpclustering
Discipline
Econometrics
Research Areas
Econometrics
Publication
Econometric Reviews
Volume
23
Issue
2
First Page
93
Last Page
123
ISSN
0747-4938
Publisher
Taylor and Francis
Citation
YU, Jun.
Empirical Characteristic Function Estimation and Its Applications. (2004). Econometric Reviews. 23, (2), 93-123.
Available at: https://ink.library.smu.edu.sg/soe_research/358
Creative Commons License
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