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Volatility forecasting with double Markov switching GARCH models

  • Cathy W. S. Chen

    (Feng Chia University, Taiwan)

  • Mike K. P. So

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

  • Edward M. H. Lin

    (Feng Chia University, Taiwan)

This paper investigates inference and volatility forecasting using a Markov switching heteroscedastic model with a fat-tailed error distribution to analyze asymmetric effects on both the conditional mean and conditional volatility of financial time series. The motivation for extending the Markov switching GARCH model, previously developed to capture mean asymmetry, is that the switching variable, assumed to be a first-order Markov process, is unobserved. The proposed model extends this work to incorporate Markov switching in the mean and variance simultaneously. Parameter estimation and inference are performed in a Bayesian framework via a Markov chain Monte Carlo scheme. We compare competing models using Bayesian forecasting in a comparative value-at-risk study. The proposed methods are illustrated using both simulations and eight international stock market return series. The results generally favor the proposed double Markov switching GARCH model with an exogenous variable. Copyright © 2008 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.1119
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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 28 (2009)
Issue (Month): 8 ()
Pages: 681-697

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Handle: RePEc:jof:jforec:v:28:y:2009:i:8:p:681-697
DOI: 10.1002/for.1119
Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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  2. So, Mike K.P. & Yu, Philip L.H., 2006. "Empirical analysis of GARCH models in value at risk estimation," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 16(2), pages 180-197, April.
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  17. Chen, Cathy W. S. & Chiang, Thomas C. & So, Mike K. P., 2003. "Asymmetrical reaction to US stock-return news: evidence from major stock markets based on a double-threshold model," Journal of Economics and Business, Elsevier, vol. 55(5-6), pages 487-502.
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