Publication Type
Journal Article
Version
publishedVersion
Publication Date
5-2019
Abstract
A new posterior odds analysis is developed to test for a unit root in volatilitydynamics in the context of stochastic volatility models. Our analysis extendsthe Bayesian unit root test of So and Li (1999) in two important ways. First,a mixed informative prior distribution with a random weight is introducedfor the Bayesian unit root testing in volatility. Second, a numerically morestable algorithm is introduced to compute Bayes factor, taking into accountthe special structure of the competing models. It can be shown that theapproach introduced overcomes the problem of the diverging “size” in themarginal likelihood approach by So and Li (1999) and improves the “power”of the unit root test. A simulation study is used to investigate the finite sampleperformance of the improved method and an empirical study implements theproposed method and the unit root hypothesis in volatility is rejected.
Keywords
Bayes factor, Markov chain Monte Carlo, Posterior odds ratio, Stochastic volatility models, Unit root testing
Discipline
Econometrics
Research Areas
Econometrics
Publication
Annals of Economics and Finance
Volume
20
Issue
1
First Page
103
Last Page
122
ISSN
1529-7373
Publisher
Peking Univ. Press
Citation
LI, Yong and Jun YU.
An improved Bayesian unit root test in stochastic volatility models. (2019). Annals of Economics and Finance. 20, (1), 103-122.
Available at: https://ink.library.smu.edu.sg/soe_research/2311
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.