Empirical Characteristic Function Estimation and its Applications

39 Pages Posted: 31 May 2004

See all articles by Jun Yu

Jun Yu

Singapore Management University - School of Economics; Singapore Management University - Lee Kong Chian School of Business

Date Written: June 2003

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

JEL Classification: C13, C15, C22, G10

Suggested Citation

Yu, Jun, Empirical Characteristic Function Estimation and its Applications (June 2003). Available at SSRN: https://ssrn.com/abstract=553701 or http://dx.doi.org/10.2139/ssrn.553701

Jun Yu (Contact Author)

Singapore Management University - School of Economics ( email )

90 Stamford Road
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Singapore
+6568280858 (Phone)
+6568280833 (Fax)

HOME PAGE: http://www.mysmu.edu/faculty/yujun/

Singapore Management University - Lee Kong Chian School of Business ( email )

469 Bukit Timah Road
Singapore 912409
Singapore

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