重庆理工大学学报(自然科学) ›› 2023, Vol. 37 ›› Issue (1): 291-301.

• 数学·统计学 • 上一篇    下一篇

多重分形视角下的 HARSMFV模型及其预测研究

董 鑫,王 沁,何 婷   

  1. 西南交通大学 数学学院,成都 61175
  • 出版日期:2023-02-16 发布日期:2023-02-16
  • 作者简介:董鑫,男,硕士研究生,主要从事金融风险管理研究,Email:dongxin9812@163.com;通讯作者 王沁,女,博士,副 教授,主要从事金融工程、金融市场及风险管理、时间序列分析研究,Email:qinyuer7311@163.com。

Research on HAR-SMFV model and its prediction from the multi-fractal perspective

  • Online:2023-02-16 Published:2023-02-16

摘要: 考虑日间波动的多重分形性、时变性及异方差性,构建了 HARHV、HARFV、HAR BSMFV和 HARTSMFV模型,评价并比较了这 4种 HAR族模型的拟合优度和预测精度。实证 结果和 MCS检验证实,引入马尔可夫转换多重分形波动的 HARSMFV类模型的预测能力显著 提高且结果具有稳健性,其中 HARTSMFV模型不仅刻画了日间波动的时变性和异方差性,而 且捕捉了 3种多重分形波动之间的转换,表现出最高的预测精度。

关键词: 马尔可夫转换多重分形, HAR模型, 预测, MCS检验

Abstract: Considering the multi-fractal, time-varying and heteroscedasticity of daytime volatility, this paper constructs HAR-HV, HAR-FV, HAR-BSMFV and HAR-TSMFV models and evaluates and compares the goodness of fit and prediction accuracy of these four HAR models. The empirical results and the MCS test confirm that the HAR-SMFV models with Markov-switching multi-fractal volatility have significantly improved predictive abilities and the results are robust. Among them, the HAR-TSMFV model not only describes the time-varying and heteroscedasticity of daytime volatility, but also captures the conversion between the three multi-fractal volatility forms, showing the highest prediction accuracy.

中图分类号: 

  • F830.91