Maximum Likelihood Estimation of Latent Affine Processes
开发了一种基于滤波的最大似然方法，用于估计潜在仿射过程的参数和实现，应用于1953-1996年股票市场日收益率，发现跳跃风险比EMM方法估计的更显著且随时间变化，并考察了对股指期权定价的影响。
This article develops a direct filtration-based maximum likelihood methodology for estimating the parameters and realizations of latent affine processes. Filtration is conducted in the transform space of characteristic functions, using a version of Bayes' rule for recursively updating the joint characteristic function of latent variables and the data conditional upon past data. An application to daily stock market returns over 1953--1996 reveals substantial divergences from estimates based on the Efficient Methods of Moments (EMM) methodology; in particular, more substantial and time-varying jump risk. The implications for pricing stock index options are examined. Copyright 2006, Oxford University Press.