Volume 7 Number 8 (Aug. 2012)
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JCP 2012 Vol.7(8): 1967-1973 ISSN: 1796-203X
doi: 10.4304/jcp.7.8.1967-1973

Comparison of GARCH Models based on Different Distributions

Yan Gao1, Chengjun Zhang2, Liyan Zhang3
1School of Science, Hebei United University, Xin Hua Street 46, Tangshan, 063009, Hebei, P. R. China; Business school of Jilin University, Changchun 130012, Jilin Province, China
2Market Department, China Mobile Group Hebei Co, Ltd. Tangshan Branch, Xing Yuan road 139, Tangshan, 063004, Hebei, P. R. China
3Business school, Hebei University of Economics and business, Xue Fu road 47, Shijiazhuang, 050061, Hebei, P. R. China


Abstract—Since ARCH and GARCH models are presented, more and more authors are interested in the study of volatilities in financial markets with GARCH models. Method for estimating the coefficients of GARCH models is mainly the maximum likelihood estimation. Now we consider another method—MCMC method to substitute for maximum likelihood estimation method. Then we compare three GARCH models based on it. MCMC method developed based on Markov chain, which is one kind of straggling time and state random process with no offspring imitates. It attracts extensive attention because of its applications in many fields. In this article, we will compare GARCH models based on different distributions with MCMC method. At last we have the conclusion that both in uni-variable case and binary variable case, GED-GARCH is the best model to describe the volatility compared to other two models, and we will provide the application of binary GED-GARCH models in forecasting the volatility in China’s stock markets.

Index Terms—MCMC, China’s stock markets, Gibbs sampling, GED-GARCH.

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Cite: Yan Gao, Chengjun Zhang, Liyan Zhang, "Comparison of GARCH Models based on Different Distributions," Journal of Computers vol. 7, no. 8, pp. 1967-1973, 2012.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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