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

Qiang Xia, Heung Wong (), Jinshan Liu and Rubing Liang
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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

Computational Economics, 2017, vol. 50, issue 3, No 1, 353-372

Abstract: 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.

Keywords: Bayesian inference; Griddy-Gibbs sampler; Power transformation; Threshold GARCH; Volatility forecasting (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10614-016-9588-x

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