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GARCH-Type Model with Continuous and Jump Variation for Stock Volatility and Its Empirical Study in China

机译:中国股市波动连续跳跃的GARCH型模型及其实证研究

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On the basis of GARCH-RV-type model, we decomposed the realized volatility into continuous sample path variation and discontinuous jump variation, then proposed a new volatility model which we call the GARCH-type model with continuous and jump variation (GARCH-CJ-type model). By using the 5-minute high frequency data of HUSHEN 300 index in China, we estimated parameters of the GARCH-type model, the GARCH-RV-type model, and the GARCH-CJ-type model and compared the three types of models’ predictive power to the future volatility. The results show that the realized volatility and the continuous sample path variation have certain predictive power for future volatility, but the discontinuous jump variation does not have that kind of function. What is more, the GARCH-CJ-type model has a more power to predict the future volatility than the other two types of models. Therefore, the GARCH-CJ-type model is much more useful for the research on the capital assets pricing, the derivative security valuation, and so on.
机译:在GARCH-RV型模型的基础上,我们将实现的波动性分解为连续的样本路径变化和不连续的跳跃变化,然后提出了一个新的波动率模型,我们将其称为具有连续和跳跃变化的GARCH型模型(GARCH-CJ-类型模型)。通过使用中国沪深300指数的5分钟高频数据,我们估算了GARCH型,GARCH-RV型和GARCH-CJ型模型的参数,并比较了这三种模型的对未来波动的预测能力。结果表明,实现的波动率和连续的样本路径变化对未来的波动性具有一定的预测能力,但不连续的跳跃变化不具有这种功能。而且,与其他两种类型的模型相比,GARCH-CJ型模型具有更大的预测未来波动率的能力。因此,GARCH-CJ型模型对于研究资本资产定价,衍生证券估值等更为有用。

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