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...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Publisher copy:
- 10.1016/S0304-4076(01)00137-3
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Authors
Funding
+ Economic and Social Research Council
More from this funder
Funding agency for:
Shephard, N
Grant:
R00023839
Bibliographic Details
- Publisher:
- Elsevier
- Journal:
- Journal of econometrics Journal website
- Volume:
- 108
- Issue:
- 2
- Pages:
- 281-316
- Publication date:
- 2002-06-01
- DOI:
- ISSN:
-
0304-4076
Item Description
- Language:
- English
- Keywords:
- Subjects:
- UUID:
-
uuid:1be2f753-e393-45e1-ad35-0521971273c8
- Local pid:
- ora:2251
- Deposit date:
- 2008-08-12
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Terms of use
- Copyright holder:
- Elsevier Science BV
- Copyright date:
- 2002
- Notes:
- The full-text of this article is not currently available in ORA. Citation: Chib, S., Nardari, F. & Shephard, N. (2002). 'Markov chain Monte Carlo methods for stochastic volatility models', Journal of Econometrics, 108(2), 281-316. [Available at http://www.sciencedirect.com/science/journal/03044076].
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