巢湖学院学报 ›› 2021, Vol. 23 ›› Issue (3): 27-37.doi: 10.12152/j.issn.1672-2868.2021.03.004

• 经济与管理 • 上一篇    下一篇

VIX对股票市场收益波动率的预测能力分析——以港股为例

1.陈亚男,朱睿:巢湖学院 数学与统计学院 2.刘月娟:中山大学附属第七医院   

  1. 1.巢湖学院 数学与统计学院,安徽 巢湖 238024 2. 中山大学附属第七医院,广东 深圳 518000
  • 收稿日期:2021-01-19 出版日期:2021-05-25 发布日期:2021-08-11
  • 作者简介:陈亚男(1991—),女,安徽淮南人,巢湖学院数学与统计学院助教,主要从事金融统计与优化研究。
  • 基金资助:
    巢湖学院校级人文社科研究项目(项目编号:XLY-202006);巢湖学院校级教学团队项目(项目编号:ch20-jxtd02)

Analysis of VIX's Ability to Predict Stock Market Return Volatility:Taking Hong Kong Stocks as an Example

1.CHEN Ya-nan, ZHU Rui:School of Mathematics and Statistics, Chaohu University 2.LIU Yue-juan:The Seventh Affiliated Hospital of Sun Yat-sen University   

  1. 1. School of Mathematics and Statistics, Chaohu University, Chaohu Anhui 238024; 2.The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen Guangdong 518000
  • Received:2021-01-19 Online:2021-05-25 Published:2021-08-11

摘要: 已实现波动率对股票市场未来收益波动率的预测效果不佳,且其所含有的有效可预测信息也不全面。因此,为提高股票收益波动率的可预测性,引入两个重要预测指标(隐含波动率VIX和WTI(BRT)石油价格波动率)并且建立“大杂烩式”预测自回归模型和“凸组合”预测自回归模型。实证结果表明:结合隐含波动率VIX和WTI(BRT)石油价格波动率的“大杂烩式”预测自回归模型具有很好的预测效果。为进一步证明该结论的可靠性,文章还增加经济周期或改变宏观经济和金融变量等条件。结果表明:相对于WTI(BRT)石油价格波动率,隐含波动率VIX对其他宏观经济和金融变量的可预测性更为有效。最后,基于不同滞后长度和不同宏观经济变量的维度,对各种预测回归模型进行鲁棒性检验。检验结果表明:结合隐含波动率VIX和WTI(BRT)石油价格波动率的自回归模型对股票收益波动率的预测效果更加有效更加稳定。

关键词: 隐含波动率VIX, WTI(BRT)石油价格波动率, 自回归模型(AR), 鲁棒性

Abstract: The realized volatility is not effective in predicting the future return volatility of the stock market, and the effective predictable information contained in it is not comprehensive. Therefore, in order to improve the predictability of stock return volatility, this paper introduces two important predictors(implicit volatility VIX and WTI (BRT) oil price volatility), and establishes a "kitchen sink" predictive autoregressive model and "convex combined" predictive autoregressive model. The empirical results show that the "kitchen sink" predictive autoregressive model that combines the implied volatility VIX and WTI(BRT) oil price volatility has a good predictive effect. To further prove the reliability of this conclusion, the paper also adds economic cycles or changes conditions such as macroeconomic and financial variables. The results show that, compared to the WTI(BRT) oil price volatility, the implied volatility VIX is more effective for the predictability of other macroeconomic and financial variables. Finally, based on the different lag lengths and the dimensions of different macroeconomic variables, this article conducts robustness tests on various predictive regression models. The test results show that the autoregressive model combined with implied volatility VIX and WTI(BRT) oil price volatility is more effective and stable in predicting stock return volatility.

Key words: implied volatility(VIX), WTI(BRT) oil price volatility, autoregressive model(AR), robustness

中图分类号: 

  • F832.5