Empirical Characteristic Function Estimation and Its Applications
综述了通过经验特征函数进行模型拟合的方法，避免了似然函数计算的困难，并用包含自激跳跃成分的扩散模型和道琼斯指数日收益率数据展示了其优于广义矩方法的有限样本表现。
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.