Maximum Likelihood Estimation of Latent Affine Processes 论文

摘要

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, with a version of Bayes’ rule used for recursively updating the joint characteristic function of latent variables and the data conditional upon past data. An application to daily stock returns over 1953-96 reveals substantial divergences from EMM-based estimates; in particular, more substantial and time-varying jump risk. The implications for stock index options ’ prices are discussed.