Journal article icon

Journal article

Markov chain Monte Carlo methods for stochastic volatility models

Abstract:

This paper is concerned with simulation-based inference in generalized models of stochastic volatility defined by heavy-tailed Student-t distributions (with unknown degrees of freedom) and exogenous variables in the observation and volatility equations and a jump component in the observation equation. By building on the work of Kim, Shephard and Chib (Rev. Econom. Stud. 65 (1998) 361), we develop efficient Markov chain Monte Carlo algorithms for estimating these models. The paper also discuss...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed

Actions


Access Document


Authors


More by this author
Institution:
John M. Olin School of Business, Washington University, USA
Role:
Author
More by this author
Institution:
Arizona State University
Role:
Author
More by this author
Institution:
University of Oxford
Research group:
Econometrics
Oxford college:
Nuffield College
Department:
Social Sciences Division - Economics
Role:
Author
Publisher:
Elsevier
Journal:
Journal of econometrics Journal website
Volume:
108
Issue:
2
Pages:
281-316
Publication date:
2002-06-05
DOI:
ISSN:
0304-4076
URN:
uuid:1be2f753-e393-45e1-ad35-0521971273c8
Local pid:
ora:2251

Terms of use


Metrics


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP