Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75839
Title: Bayesian analysis of power-transformed and threshold GARCH models : a Griddy-Gibbs sampler approach
Authors: Xia, Q
Wong, H 
Liu, JS
Liang, RB
Keywords: Bayesian inference
Griddy-Gibbs sampler
Power transformation
Threshold GARCH
Volatility forecasting
Issue Date: 2017
Publisher: Springer
Source: Computational economics, 2017, v. 50, no. 3, p. 353-372 How to cite?
Journal: Computational economics 
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.
URI: http://hdl.handle.net/10397/75839
ISSN: 0927-7099
EISSN: 1572-9974
DOI: 10.1007/s10614-016-9588-x
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.