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A New Bayesian Unit Root Test in Stochastic Volatility Models

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  • Yong Li

    (Sun Yat-Sen University)

  • Jun Yu

    () (Sim Kee Boon Institute for Financial Economics, School of Economics and Lee Kong Chian School of Business)

Abstract

A new posterior odds analysis is proposed 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, Journal of Business and Economic Statistics) in the two important ways. First, a numerically more stable algorithm is introduced to compute Bayes factors, taking into account the special structure of the competing models. Owing to its numerical stability, the algorithm overcomes the problem of the diverging “size” in the marginal likelihood approach. Second, to improve the “power” of the unit root test, a mixed prior specification with random weights is employed. It is shown that the posterior odds ratio is the by-product of Bayesian estimation and can be easily computed by MCMC methods. A simulation study examines the “size” and “power” performances of the new method. An empirical study, based on time series data covering the subprime crisis, reveals some interesting results.

Suggested Citation

  • Yong Li & Jun Yu, 2012. "A New Bayesian Unit Root Test in Stochastic Volatility Models," Working Papers 14-2012, Singapore Management University, School of Economics.
    • Handle: RePEc:siu:wpaper:14-2012
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Cathy Chen & Shu-Yu Chen & Sangyeol Lee, 2013. "Bayesian Unit Root Test in Double Threshold Heteroskedastic Models," Computational Economics, Springer;Society for Computational Economics, vol. 42(4), pages 471-490, December.

    More about this item

    Keywords

    Bayes factor; Mixed Prior; Markov Chain Monte Carlo; Posterior odds ratio; Stochastic volatility models; Unit root testing.;

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