Title

Empirical Characteristic Function Estimation and Its Applications

Publication Type

Journal Article

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; Jump clustering

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

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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