Journal Article

Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models

Sangjoon Kim, Neil Shephard and Siddhartha Chib

in The Review of Economic Studies

Published on behalf of Review of Economic Studies Ltd

Volume 65, issue 3, pages 361-393
Published in print July 1998 | ISSN: 0034-6527
Published online July 1998 | e-ISSN: 1467-937X | DOI: http://dx.doi.org/10.1111/1467-937X.00050
Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models

Show Summary Details

Preview

In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models. A highly effective method is developed that samples all the unobserved volatilities at once using an approximating offset mixture model, followed by an importance reweighting procedure. This approach is compared with several alternative methods using real data. The paper also develops simulation-based methods for filtering, likelihood evaluation and model failure diagnostics. The issue of model choice using non-nested likelihood ratios and Bayes factors is also investigated. These methods are used to compare the fit of stochastic volatility and GARCH models. All the procedures are illustrated in detail.

Journal Article.  0 words. 

Subjects: Economics

Full text: subscription required

How to subscribe Recommend to my Librarian

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.