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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...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
John M. Olin School of Business, Washington University, USA
Role:
Author
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Institution:
Arizona State University
Role:
Author
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Institution:
University of Oxford
Division:
SSD
Department:
Economics
Research group:
Econometrics
Oxford college:
Nuffield College
Role:
Author
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Funding agency for:
Shephard, N
Grant:
R00023839
Publisher:
Elsevier
Journal:
Journal of econometrics Journal website
Volume:
108
Issue:
2
Pages:
281-316
Publication date:
2002-06-01
DOI:
ISSN:
0304-4076
Language:
English
Keywords:
Subjects:
UUID:
uuid:1be2f753-e393-45e1-ad35-0521971273c8
Local pid:
ora:2251
Deposit date:
2008-08-12

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