Bayesian model selection for heteroskedastic models

Cathy W.S. Chen; Richard Gerlach; Mike K.P. So

doi:10.1016/S0731-9053(08)23018-5

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Advances in Econometrics

ISSN: 0731-9053
Series editor(s): Thomas B. Fomby, R. Carter Hill, Ivan Jeliazkov, Juan Carlos Escanciano and Eric Hillebrand

Subject Area: Economics

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Bayesian model selection for heteroskedastic models

Document Information:
Title:	Bayesian model selection for heteroskedastic models
Author(s):	Cathy W.S. Chen, Richard Gerlach, Mike K.P. So
Volume:	23 Editor(s): Siddhartha Chib, William Griffiths, Gary Koop, Dek Terrell ISBN: 978-1-84855-308-8 eISBN: 978-1-84855-309-5
Citation:	Cathy W.S. Chen, Richard Gerlach, Mike K.P. So (2008), Bayesian model selection for heteroskedastic models, in Siddhartha Chib, William Griffiths, Gary Koop, Dek Terrell (ed.) Bayesian Econometrics (Advances in Econometrics, Volume 23), Emerald Group Publishing Limited, pp.567-594
DOI:	10.1016/S0731-9053(08)23018-5 (Permanent URL)
Publisher:	Emerald Group Publishing Limited
Article type:	Chapter Item
Abstract:	It is well known that volatility asymmetry exists in financial markets. This paper reviews and investigates recently developed techniques for Bayesian estimation and model selection applied to a large group of modern asymmetric heteroskedastic models. These include the GJR-GARCH, threshold autoregression with GARCH errors, TGARCH, and double threshold heteroskedastic model with auxiliary threshold variables. Further, we briefly review recent methods for Bayesian model selection, such as, reversible-jump Markov chain Monte Carlo, Monte Carlo estimation via independent sampling from each model, and importance sampling methods. Seven heteroskedastic models are then compared, for three long series of daily Asian market returns, in a model selection study illustrating the preferred model selection method. Major evidence of nonlinearity in mean and volatility is found, with the preferred model having a weighted threshold variable of local and international market news.