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An Improved Bayesian Unit Root Test in Stochastic Volatility Models

Author

Listed:
  • Yong Li

    (Hanqing Advanced Institute of Economics and Finance, Renmin University of China)

  • Jun Yu

    (School of Economics and Lee Kong Chian School of Business, Singapore Management University)

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.

Suggested Citation

  • Yong Li & Jun Yu, 2019. "An Improved Bayesian Unit Root Test in Stochastic Volatility Models," Annals of Economics and Finance, Society for AEF, vol. 20(1), pages 103-122, May.
    • Handle: RePEc:cuf:journl:y:2019:v:20:i:1:liyu
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    References listed on IDEAS

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