王 亮. 基于条件自回归极差模型的沪深300 指数波动性分析[J]. 内江师范学院学报, 2015, (4): 9-13. DOI: 10.13603/j.cnki.51-1621/z.2015.04.003
    引用本文: 王 亮. 基于条件自回归极差模型的沪深300 指数波动性分析[J]. 内江师范学院学报, 2015, (4): 9-13. DOI: 10.13603/j.cnki.51-1621/z.2015.04.003
    WANG Liang. Analysis of the Volatility of SCI 300 Based on the Conditional Auto-Regressive Range Model[J]. Journal of Neijiang Normal University, 2015, (4): 9-13. DOI: 10.13603/j.cnki.51-1621/z.2015.04.003
    Citation: WANG Liang. Analysis of the Volatility of SCI 300 Based on the Conditional Auto-Regressive Range Model[J]. Journal of Neijiang Normal University, 2015, (4): 9-13. DOI: 10.13603/j.cnki.51-1621/z.2015.04.003

    基于条件自回归极差模型的沪深300 指数波动性分析

    Analysis of the Volatility of SCI 300 Based on the Conditional Auto-Regressive Range Model

    • 摘要: 通过选取不同的分布对自回归条件极差模型进行了改进,选择GARCH模型对对数收益率的波动率进行了建模, 运用贝叶斯对两种模型中的参数进行分析. 通过用沪深300 指数数据进行实证分析, WinBUGS 软件对两种模型当中的参数进行仿真, 由参数估计值分析得到, 自回归条件极差模型对波动短期效应有更好的捕捉能力, 而GARCH 模型优势在长期效应.

       

      Abstract: In 2005, Chou suggested an approach to conduct the volatility analysis by use of the Auto-regressive Range Model, yet the model was blame for its deficiencies found in the distribution assumptions of logarithmic mean value. By selection of different distributions, improvements were made to the model. By contrast, modeling was made of the volatility rate of the logarithmic return rate by selection of the GARCH Model and by use of Bayesian the parameters of the two models were subjected to a parameter analysis and an empirical analysis was done by taking advantage of the SCI300 index data; and a parameter simulation was also done to the two models by WinBugs. By means of the analysis of the parameter estimates it was concluded that the Auto-Regressive Model is of a better performance in capturing the short-term effects while the GARCH Model is better at capturing the long-term effects.

       

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