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An Efficient Stochastic Simulation Algorithm for Bayesian Unit Root Testing in Stochastic Volatility Models

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Abstract

In financial times series analysis, unit root test is one of the most important research issues. This paper is aimed to propose a new simple and efficient stochastic simulation algorithm for computing Bayes factor to detect the unit root of stochastic volatility models. The proposed algorithm is based on a classical thermodynamic integration technique named path sampling. Simulation studies show that the test procedure is efficient under moderate sample size. In the end, the performance of the proposed approach is investigated with a Monte Carlo simulation study and illustrated with a time series of S&P500 return data.

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Authors and Affiliations

  1. School of Business, Sun Yat-Sen University, Guangzhou, 510275, China

    Yong Li

  2. School of Economics, Shanghai University, Shanghai, 200444, China

    Zhongxin Ni

  3. Institute of China’s Economic Reform Development, Renmin University of China, Beijing, 100872, China

    Jie Zhang

Authors
  1. Yong Li
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  2. Zhongxin Ni
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  3. Jie Zhang
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Correspondence to Zhongxin Ni.

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Li, Y., Ni, Z. & Zhang, J. An Efficient Stochastic Simulation Algorithm for Bayesian Unit Root Testing in Stochastic Volatility Models. Comput Econ 37, 237–248 (2011). https://doi.org/10.1007/s10614-011-9252-4

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  • DOI: https://doi.org/10.1007/s10614-011-9252-4

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