2004/08/23 by Jun Yu · 1 citation
Economics, Econometrics and Finance · Mathematics · #Stochastic processes and financial applications #Monetary Policy and Economic Impact #Financial Risk and Volatility Modeling #Function (biology) #Jump #Monte Carlo method #Likelihood function #Cluster analysis #Jump diffusion #Computer science #Characteristic function (probability theory) #Expression (computer science) #Component (thermodynamics) #Mathematics #Econometrics #Applied mathematics #Mathematical optimization #Estimation theory #Algorithm #Statistics #Probability density function
paper · doi:10.1081/etc-120039605
openalex publication_date 2004/08/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
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.