A non-iterative (trivial) method for posterior inference in stochastic volatility models

Tsionas, Mike G. (2017) A non-iterative (trivial) method for posterior inference in stochastic volatility models. Statistics and Probability Letters, 126. pp. 83-87. ISSN 0167-7152

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Abstract

We propose a new non-iterative, very simple but accurate, Bayesian inference procedure for the stochastic volatility model. The only requirement of our approach is to solve a large, sparse linear system which we avoid by iteration.

Item Type:

Journal Article

Journal or Publication Title:

Statistics and Probability Letters

Additional Information:

This is the author’s version of a work that was accepted for publication in Statistics and Probability Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Statistics and Probability Letters, 126, 2017 DOI: 10.1016/j.spl.2017.02.035

Uncontrolled Keywords:

/dk/atira/pure/subjectarea/asjc/2600/2613

Subjects:

?? stochastic volatility modelmonte carlo methodsmarkov chain monte carloiterative methodsstatistics and probabilitystatistics, probability and uncertainty ??

Departments:

Lancaster University Management School > Economics

ID Code:

85548

Deposited By:

ep_importer_pure

Deposited On:

22 Mar 2017 09:30

Refereed?:

Yes

Published?:

Published

Last Modified:

18 Sep 2024 00:41

URI:

https://eprints.lancs.ac.uk/id/eprint/85548