ISSN 1008-2204
CN 11-3979/C
刘湖, 王莹. 股票市场波动性研究——基于ARMA-TGARCH-M模型的实证分析[J]. 北京航空航天大学学报社会科学版, 2017, 30(4): 56-66. DOI: 10.13766/j.bhsk.1008-2204.2015.0350
引用本文: 刘湖, 王莹. 股票市场波动性研究——基于ARMA-TGARCH-M模型的实证分析[J]. 北京航空航天大学学报社会科学版, 2017, 30(4): 56-66. DOI: 10.13766/j.bhsk.1008-2204.2015.0350
LIU Hu, WANG Ying. Study on Volatility of Stock Market:Empirical Analysis Based on ARMA-TGARCH-M Model[J]. Journal of Beijing University of Aeronautics and Astronautics Social Sciences Edition, 2017, 30(4): 56-66. DOI: 10.13766/j.bhsk.1008-2204.2015.0350
Citation: LIU Hu, WANG Ying. Study on Volatility of Stock Market:Empirical Analysis Based on ARMA-TGARCH-M Model[J]. Journal of Beijing University of Aeronautics and Astronautics Social Sciences Edition, 2017, 30(4): 56-66. DOI: 10.13766/j.bhsk.1008-2204.2015.0350

股票市场波动性研究——基于ARMA-TGARCH-M模型的实证分析

Study on Volatility of Stock Market:Empirical Analysis Based on ARMA-TGARCH-M Model

  • 摘要: 通过构建ARMA-TGARCH-M模型,并同时利用上证综合指数和深圳成份指数的低频日收益率和5分钟高频收益率数据,对中国股票市场的波动性问题进行了实证研究。结果表明:中国股票市场存在着大幅度高频率波动,市场总体风险较大,而且收益率波动也存在着波动集群性、尖峰后尾性和非对称分布等特征,深圳股票市场在各方面的特征也都比上海股票市场突出。此外,低频日收益率序列和5分钟高频收益率序列都存在着显著的平稳性、自相关性和ARCH效应,中国股票市场还存在着较长的外部冲击波动持续期,且杠杆效应显著。GARCH族模型能够很好地拟合中国股票市场的波动性问题。

     

    Abstract: This paper conducts an empirical test of the volatility of the stock market in China, by building the ARMA-TGARCH-M model, and using the data of low-frequency daily yield and high-frequency five minutes yield of Shanghai Composite Index and Shenzhen Component Index simultaneously. The empirical results show that there is a substantial high-frequency fluctuation in China's stock market, and that the overall market risk is high. Moreover, there are characters of volatility clustering, peaky kurtosis and thick trail and asymmetric distribution in the yield volatility of stock market in China; and the stock market in Shenzhen is more prominent in all those characteristics than the stock market in Shanghai. In addition, there are significant stability, self-correlation and ARCH effect, long external shock volatility duration, and the significant leverage effect as well. GARCH Models can be fitted well to the problem of the volatility of the stock market in China.

     

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