EconPapers    
Economics at your fingertips  
 

Maximum Likelihood Estimation of Latent Affine Processes

David S. Bates

Review of Financial Studies, 2006, vol. 19, issue 3, 909-965

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. Copyright 2006, Oxford University Press.

Date: 2006
References: Add references at CitEc
Citations View citations in EconPapers (41) Track citations by RSS feed

Downloads: (external link)
http://hdl.handle.net/10.1093/rfs/hhj022 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:rfinst:v:19:y:2006:i:3:p:909-965

Ordering information: This journal article can be ordered from
http://www4.oup.co.uk/revfin/subinfo/

Access Statistics for this article

Review of Financial Studies is currently edited by Maureen O'Hara

More articles in Review of Financial Studies from Society for Financial Studies Oxford University Press, Journals Department, 2001 Evans Road, Cary, NC 27513 USA.. Contact information at EDIRC.
Series data maintained by Oxford University Press ().

 
Page updated 2017-11-07
Handle: RePEc:oup:rfinst:v:19:y:2006:i:3:p:909-965