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引入测量误差和马尔科夫转换机制的高频波动率建模与预测

李红权, 张馨心   

  1. 湖南师范大学商学院, 长沙 410081
  • 收稿日期:2021-10-03 修回日期:2021-11-27 出版日期:2022-05-25 发布日期:2022-07-23
  • 基金资助:
    国家自然科学基金面上项目(71871092),宏观经济大数据挖掘与应用湖南省重点实验室资助课题.

李红权, 张馨心. 引入测量误差和马尔科夫转换机制的高频波动率建模与预测[J]. 系统科学与数学, 2022, 42(5): 1200-1215.

LI Hongquan, ZHANG Xinxin. The Forecasting Performance of the High-Frequency Volatility Models Based on Errors and Markov Regime-Switching[J]. Journal of Systems Science and Mathematical Sciences, 2022, 42(5): 1200-1215.

The Forecasting Performance of the High-Frequency Volatility Models Based on Errors and Markov Regime-Switching

LI Hongquan, ZHANG Xinxin   

  1. School of Business, Hunan Normal University, Changsha 410081
  • Received:2021-10-03 Revised:2021-11-27 Online:2022-05-25 Published:2022-07-23
基于现实金融市场同时存在已实现波动率测量误差问题以及市场结构突变现象,将马尔科夫转换机制引入经测量误差修正的模型,构建了具有时变特征的MRS-HARQ族模型,以期进一步提高HAR框架下波动率模型的预测精度.以沪深300指数2005年4月--2021年7月5分钟高频数据为样本的实证研究证实:相比于HAR和HARQ (F)族模型,结合测量误差和马尔科夫转换机制构建的MRS-HARQ族模型具有更优的拟合效果和预测能力,能够更准确地刻画中国股市真实波动的复杂特征,并发现高波动状态持续的时间都不长;更进一步在文章所探讨的16种高频波动率模型中,结合已实现正负半方差的MRS-HARQ-RS模型和结合符号跳跃的MRS-HARQ-SJ模型拥有最佳的样本外预测能力.研究结论在金融风险建模、资产定价和金融预测领域有较好的应用价值.
Based on the existence of both realized volatility measurement errors and sudden changes in market structure in real financial markets, the Markov regimeswitching is introduced into the model corrected by errors, and the MRS-HARQtype models with time-varying characteristics are constructed to further improve the forecasting accuracy of volatility models under the HAR framework. The empirical study with the sample of 5-minute high-frequency data of the CSI 300 index from April 2005 to July 2021 confirms that, compared with the HAR-type and HARQ(F)-type models, the MRS-HARQ-type models constructed by combining errors and Markov regime-switching have better goodness-of-fit and forecasting performance, and can more accurately portray the complex characteristics of the real volatility of China's stock market, and finds that the high volatility states do not last long; furthermore among the 16 high-frequency volatility models explored in this paper, the MRSHARQ-RS model combining the realized semivariance and the MRS-HARQ-SJ model combining signed jump possess the best out-of-sample forecasting performance. The findings have good applications in the fields of financial risk modeling, asset pricing and financial forecasting.

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