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.
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Acknowledgments
The authors thank the editor, and the referees for their constructive suggestions and comments that led to a significant improvement of an early version of the manuscript. The research of Qiang Xia was supported by National Science Foundation of Guangdong Province of China (No. 2016A030313414) and National statistical plan for scientific research project of China (No. 2015LZ48). The research of Heung Wong was supported by a grant from the research committee of The Hong Kong Polytechnic University. The research of Rubing Liang was supported by National Science Foundation of Guangdong Province of China (No. 2015A030310365).
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Xia, Q., Wong, H., Liu, J. et al. Bayesian Analysis of Power-Transformed and Threshold GARCH Models: A Griddy-Gibbs Sampler Approach. Comput Econ 50, 353–372 (2017). https://doi.org/10.1007/s10614-016-9588-x
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DOI: https://doi.org/10.1007/s10614-016-9588-x