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A Bayesian threshold nonlinearity test for financial time series

  • Cathy W. S. Chen

    (Feng Chia University, Taiwan)

  • Mike K. P. So

    (Hong Kong University of Science and Technology, Hong Kong)

  • Ming-Tien Chen

    (Feng Chia University, Taiwan)

We propose in this paper a threshold nonlinearity test for financial time series. Our approach adopts reversible-jump Markov chain Monte Carlo methods to calculate the posterior probabilities of two competitive models, namely GARCH and threshold GARCH models. Posterior evidence favouring the threshold GARCH model indicates threshold nonlinearity or volatility asymmetry. Simulation experiments demonstrate that our method works very well in distinguishing GARCH and threshold GARCH models. Sensitivity analysis shows that our method is robust to misspecification in error distribution. In the application to 10 market indexes, clear evidence of threshold nonlinearity is discovered and thus supporting volatility asymmetry. Copyright © 2005 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.939
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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 24 (2005)
Issue (Month): 1 ()
Pages: 61-75

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Handle: RePEc:jof:jforec:v:24:y:2005:i:1:p:61-75
Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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