不同矩属性波动模型对中国股市波动率的预测精度分析
发布时间：2018-11-14 14:56
【摘要】：金融时间序列的波动性建模经历了从一阶矩到二阶矩直到高阶矩(包含三阶矩和四阶矩)的过程,而对于高阶矩波动模型是否有助于对未来市场的波动率预测这一问题,国内外学术界尚无文献讨论。以上证综指长达7年的每5分钟高频数据样本为例,通过构建具有不同矩属性的波动模型,计算了中国股票市场波动率的预测值,并利用具有bootstrap特性的SPA检验法,实证检验了不同矩属性波动模型的波动率预测精度差异。实证结果显示:就中国股市而言,四阶矩波动模型能够取得比二阶矩波动模型更优的波动率预测精度,而三阶矩波动模型并未表现出比二阶矩波动模型更强的预测能力;在高阶矩波动模型中包含杠杆效应项并不能提高模型的预测精度。最后提出了在金融风险管理、衍生产品定价等领域引入四阶矩波动模型的研究思路。
[Abstract]:The volatility modeling of financial time series has experienced the process from first moment to second moment to high order moment (including third moment and fourth moment), but whether the high order moment volatility model is helpful to predict the volatility of the future market. There is no literature discussion at home and abroad. Taking the high frequency data samples of Shanghai Composite Index for 7 years as an example, a volatility model with different moment attributes is constructed to calculate the forecast value of volatility in Chinese stock market, and the SPA test method with bootstrap characteristics is used. The volatility prediction accuracy of different moment attribute volatility models is tested empirically. The empirical results show that the fourth-order moment volatility model can achieve better volatility prediction accuracy than the second-order moment volatility model, but the third-order moment volatility model does not show better prediction ability than the second-order moment volatility model. The prediction accuracy of the model can not be improved by including the lever effect in the higher-order moment wave model. Finally, the fourth moment volatility model is introduced in the fields of financial risk management and derivative pricing.
【作者单位】： 西南交通大学经济管理学院;
【基金】：国家自然科学基金(70501025;70771097)
【分类号】：F224.0;F832.51
，
本文编号：2331495
[Abstract]:The volatility modeling of financial time series has experienced the process from first moment to second moment to high order moment (including third moment and fourth moment), but whether the high order moment volatility model is helpful to predict the volatility of the future market. There is no literature discussion at home and abroad. Taking the high frequency data samples of Shanghai Composite Index for 7 years as an example, a volatility model with different moment attributes is constructed to calculate the forecast value of volatility in Chinese stock market, and the SPA test method with bootstrap characteristics is used. The volatility prediction accuracy of different moment attribute volatility models is tested empirically. The empirical results show that the fourth-order moment volatility model can achieve better volatility prediction accuracy than the second-order moment volatility model, but the third-order moment volatility model does not show better prediction ability than the second-order moment volatility model. The prediction accuracy of the model can not be improved by including the lever effect in the higher-order moment wave model. Finally, the fourth moment volatility model is introduced in the fields of financial risk management and derivative pricing.
【作者单位】： 西南交通大学经济管理学院;
【基金】：国家自然科学基金(70501025;70771097)
【分类号】：F224.0;F832.51
，
本文编号：2331495
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