沪深300股指期货的波动率预测模型研究
发布时间：2019-05-08 08:42
【摘要】：以沪深300股指期货仿真交易的5分钟高频数据为例,运用滚动时间窗的样本外预测和具有Bootstrap特性的SPA检验法,全面对比了基于日收益数据的历史波动率(historical volatility)模型和基于高频数据的已实现波动率(realized volatility)模型对波动率的刻画和预测能力.主要实证结果显示,已实现波动率模型以及加入附加解释变量的扩展随机波动模型是预测精度较高的波动模型,而在学术界和实务界常用的GARCH及其扩展模型对沪深300股指期货的波动率预测能力最弱.
[Abstract]:Taking the 5-minute high-frequency data of Shanghai-Shenzhen 300 stock index futures as an example, this paper uses the out-of-sample prediction of rolling time window and the SPA test method with Bootstrap characteristics. In this paper, historical volatility (historical volatility) model based on daily income data and realized volatility (realized volatility) model based on high frequency data are comprehensively compared to describe and predict volatility. The main empirical results show that the realized volatility model and the extended stochastic fluctuation model with additional explanatory variables are the volatility models with high prediction accuracy. However, GARCH and its extended model, which are commonly used in academic and practical circles, have the weakest ability to predict volatility of Shanghai-Shenzhen 300 stock index futures.
【作者单位】： 西南交通大学经济管理学院;
【基金】：国家自然科学基金资助项目(70501025;70771097;70771095) 教育部新世纪优秀人才支持计划资助项目(NCET-08-0826) 教育部创新团队发展计划资助项目(PCSIRT0860)
【分类号】：F224;F832.51
本文编号：2471766
[Abstract]:Taking the 5-minute high-frequency data of Shanghai-Shenzhen 300 stock index futures as an example, this paper uses the out-of-sample prediction of rolling time window and the SPA test method with Bootstrap characteristics. In this paper, historical volatility (historical volatility) model based on daily income data and realized volatility (realized volatility) model based on high frequency data are comprehensively compared to describe and predict volatility. The main empirical results show that the realized volatility model and the extended stochastic fluctuation model with additional explanatory variables are the volatility models with high prediction accuracy. However, GARCH and its extended model, which are commonly used in academic and practical circles, have the weakest ability to predict volatility of Shanghai-Shenzhen 300 stock index futures.
【作者单位】： 西南交通大学经济管理学院;
【基金】：国家自然科学基金资助项目(70501025;70771097;70771095) 教育部新世纪优秀人才支持计划资助项目(NCET-08-0826) 教育部创新团队发展计划资助项目(PCSIRT0860)
【分类号】：F224;F832.51
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