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Bayesian Analysis of Power-Transformed and Threshold GARCH Models: A Griddy-Gibbs Sampler Approach

Author

Listed:
  • Qiang Xia

    (South China Agricultural University)

  • Heung Wong

    (The Hong Kong Polytechnic University)

  • Jinshan Liu

    (South China Agricultural University)

  • Rubing Liang

    (South China Agricultural University)

Abstract

In this paper, we propose a Griddy-Gibbs sampler approach to estimate parameters and forecast volatilities for the power transformed and threshold GARCH (PTTGARCH; Pan et al. in J Econ 142:352–378, 2008) model, which includes the standard GARCH model and many other commonly used models as special cases. Simulation study indicates that the Bayesian scheme performs effectively in estimation and prediction. A real data example is presented to support our proposed Bayesian method.

Suggested Citation

  • Qiang Xia & Heung Wong & Jinshan Liu & Rubing Liang, 2017. "Bayesian Analysis of Power-Transformed and Threshold GARCH Models: A Griddy-Gibbs Sampler Approach," Computational Economics, Springer;Society for Computational Economics, vol. 50(3), pages 353-372, October.
    • Handle: RePEc:kap:compec:v:50:y:2017:i:3:d:10.1007_s10614-016-9588-x
    DOI: 10.1007/s10614-016-9588-x
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    References listed on IDEAS

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    4. Fu, Jin-Yu & Lin, Jin-Guan & Hao, Hong-Xia, 2023. "Volatility analysis for the GARCH–Itô–Jumps model based on high-frequency and low-frequency financial data," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1698-1712.
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