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  • NON-iterative sampler REVISED

    Rights statement: 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

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

Research output: Contribution to journalJournal articlepeer-review

Published
<mark>Journal publication date</mark>1/07/2017
<mark>Journal</mark>Statistics and Probability Letters
Volume126
Number of pages5
Pages (from-to)83-87
Publication StatusPublished
Early online date6/03/17
<mark>Original language</mark>English

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.

Bibliographic note

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