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Unit Root Hypothesis in the Presence of Stochastic Volatility, a Bayesian Analysis


  • Jin-Yu Zhang
  • Yong Li


  • Zhu-Ming Chen


This paper investigates the impact of stochastic volatility on the Dickey–Fuller unit root test. Monte Carlo simulations show that the test size is seriously distorted if nonstationary stochastic volatility is ignored. To improve the performance of the test, we propose a Bayesian test for unit root that is robust in the presence of stationary and nonstationary stochastic volatility. The finite sample property of the proposed test statistic is evaluated using Monte Carlo studies. Applying the developed method, we test the policy effect of the Split Share Structure Reform, which is an important milestone for the development of the emerging stock market in China. Copyright Springer Science+Business Media New York 2013

Suggested Citation

  • Jin-Yu Zhang & Yong Li & Zhu-Ming Chen, 2013. "Unit Root Hypothesis in the Presence of Stochastic Volatility, a Bayesian Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 41(1), pages 89-100, January.
  • Handle: RePEc:kap:compec:v:41:y:2013:i:1:p:89-100
    DOI: 10.1007/s10614-012-9319-x

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    References listed on IDEAS

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    8. Renate Meyer & Jun Yu, 2000. "BUGS for a Bayesian analysis of stochastic volatility models," Econometrics Journal, Royal Economic Society, vol. 3(2), pages 198-215.
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    Cited by:

    1. Cathy W. S. Chen & Sangyeol Lee & Shu-Yu Chen, 2016. "Local non-stationarity test in mean for Markov switching GARCH models: an approximate Bayesian approach," Computational Statistics, Springer, vol. 31(1), pages 1-24, March.
    2. Pan, Qi & Li, Yong, 2013. "Testing volatility persistence on Markov switching stochastic volatility models," Economic Modelling, Elsevier, vol. 35(C), pages 45-50.
    3. Thomas Nanfeng Li & Agnès Tourin, 2016. "Optimal pairs trading with time-varying volatility," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 3(03), pages 1-29, September.
    4. Xiao-Bin Liu & Yong Li, 2013. "Bayesian testing volatility persistence in stochastic volatility models with jumps," Quantitative Finance, Taylor & Francis Journals, vol. 14(8), pages 1415-1426, December.

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