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On a dynamic mixture GARCH model

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Author Info
Xixin Cheng (Department of Statistics and Actuarial Science, University of Hong Kong)
Philip L. H. Yu (Department of Statistics and Actuarial Science, University of Hong Kong)
W. K. Li (Department of Statistics and Actuarial Science, University of Hong Kong)

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Abstract

This paper proposes a new mixture GARCH model with a dynamic mixture proportion. The mixture Gaussian distribution of the error can vary from time to time. The Bayesian Information Criterion and the EM algorithm are used to estimate the number of parameters as well as the model parameters and their standard errors. The new model is applied to the S&P500 Index and Hang Seng Index and compared with GARCH models with Gaussian error and Student's t error. The result shows that the IGARCH effect in these index returns could be the result of the mixture of one stationary volatility component with another non-stationary volatility component. The VaR based on the new model performs better than traditional GARCH-based VaRs, especially in unstable stock markets. Copyright © 2008 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.1093
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Publisher Info
Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 28 (2009)
Issue (Month): 3 ()
Pages: 247-265
Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Handle: RePEc:jof:jforec:v:28:y:2009:i:3:p:247-265

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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This page was last updated on 2009-12-10.

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