Journal article
Maximum likelihood estimation of latent affine processes
The Review of financial studies, Vol.19(3), pp.909-965
10/01/2006
DOI: 10.1093/rfs/hhj022
Abstract
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.
Details
- Title: Subtitle
- Maximum likelihood estimation of latent affine processes
- Creators
- David S. Bates - University of Iowa
- Resource Type
- Journal article
- Publication Details
- The Review of financial studies, Vol.19(3), pp.909-965
- Publisher
- Oxford Univ Press
- DOI
- 10.1093/rfs/hhj022
- ISSN
- 0893-9454
- eISSN
- 1465-7368
- Number of pages
- 57
- Language
- English
- Date published
- 10/01/2006
- Academic Unit
- Finance
- Record Identifier
- 9984380523002771
Metrics
4 Record Views