An Improved Bayesian Unit Root Test in Stochastic Volatility Models

Yong Li () and Jun Yu
Additional contact information
Yong Li: Hanqing Advanced Institute of Economics and Finance, Renmin University of China

Annals of Economics and Finance, 2019, vol. 20, issue 1, 103-122

Abstract: A new posterior odds analysis is developed to test for a unit root in volatility dynamics in the context of stochastic volatility models. Our analysis extends the Bayesian unit root test of So and Li (1999) in two important ways. First, a mixed informative prior distribution with a random weight is introduced for the Bayesian unit root testing in volatility. Second, a numerically more stable algorithm is introduced to compute Bayes factor, taking into account the special structure of the competing models. It can be shown that the approach introduced overcomes the problem of the diverging "size" in the marginal 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 sample performance of the improved method and an empirical study implements the proposed 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 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://down.aefweb.net/AefArticles/aef200104LiYu.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cuf:journl:y:2019:v:20:i:1:liyu

Access Statistics for this article

Annals of Economics and Finance is currently edited by Heng-fu Zou

More articles in Annals of Economics and Finance from Society for AEF Contact information at EDIRC.
Bibliographic data for series maintained by Qiang Gao ().