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中国管理科学 ›› 2020, Vol. 28 ›› Issue (9): 12-22.doi: 10.16381/j.cnki.issn1003-207x.2018.1363

• 论文 • 上一篇    下一篇

带有市场交易信息和随机微观噪声下的杠杆效应研究

苑慧玲1, 徐路2,3, 周勇4   

  1. 1. 香港城市大学数据科学学院, 香港 999077;
    2. 申万宏源证券有限公司博士后科研工作站, 上海 200031;
    3. 复旦大学理论经济学博士后流动站, 上海 200433;
    4. 统计与数据科学前沿理论及应用教育部重点实验室, 华东师范大学统计交叉科学研究院和统计学院, 上海 200062
  • 收稿日期:2018-09-22 修回日期:2019-01-23 出版日期:2020-09-20 发布日期:2020-09-25
  • 通讯作者: 周勇(1964-),男(汉族),广西人,华东师范大学统计交叉科学研究院,教授,院长,研究方向:金融计量、风险管理,E-mail:yzhou@amss.ac.cn. E-mail:yzhou@amss.ac.cn
  • 基金资助:
    国家自然科学基金资助项目(71931004,91546202)

Leverage Effect Combining Trading Information with Stochastic Microstructure Noise

Yuan Hui-ling1, XU Lu2,3, Zhou Yong4   

  1. 1. School of Data Science, City University of Hongkong, Hongkong 999077, China;
    2. Postdoctoral Research Station, Shanghai Hongyuan Securities Co., LTD, Shanghai 200031, China;
    3. Theoretical Economics Postdoctoral Programs at Fudan University, Shanghai 200433, China;
    4. Key Laboratory of Advanced Theory and Application in Statistics and Data Science, Ministry of Education, Institute of Statistics and Interdisciplinary Sciences and School of Statistics, East China Normal University, Shanghai 200062, China
  • Received:2018-09-22 Revised:2019-01-23 Online:2020-09-20 Published:2020-09-25

摘要: 在风险管理中杠杆效应的现象广泛存在,也是金融计量学中的重要议题。高频金融市场中蕴含着丰富的交易信息,而这些信息并不能都看作随机噪声,因此探讨利用市场交易信息并在带有随机噪声模型下研究杠杆效应具有重要意义。本文在带有市场交易信息和随机微观噪声相结合的模型下研究了杠杆效应,提出了新的杠杆效应估计,该估计具有n1/8的收敛速度,同时给出了估计的方差和相关的定理。通过模拟分析得出利用广泛的市场微观信息可以更有效和更精确地对杠杆效应进行估计,模拟的结果表明本文提出的杠杆效应估计具有更好的渐近正态性和更小的偏差。最后将提出的估计应用到实证分析中,发现杠杆效应对未来一天波动率的预测具有显著性影响。

关键词: 杠杆效应, 高频数据, 市场微观信息, 二次变差, 波动率

Abstract: Leverage effect is a widespread existence in risk management, which is also an important topic in financial econometrics. On the other hand, it is valuable for us to study leverage effect based on the market trade information and the stochastic noise model, since that rich information is available and not viewed as the stochastic noise in high-frequency financial market. In this paper, the leverage effect combining trading information with stochastic microstructure noise model is studied, and the new estimator of leverage effect is proposed, which has the convergence rate n1/8. The variance and the relative theorems are also provided. A simulation is conducted to study that leverage effect could be estimated more efficiently and more accurately via the general market microstructure information. Simulation results show the leverage effect estimator performs better asymptotic normal property and smaller biases. An empirical study has been carried out to demonstrate that leverage effect is significant in forecasting volatility, which provides a useful theoretical basis for relevant financial departments to conduct financial guidance.

Key words: leverage effect, high frequency data, market microstructure information, quadratic covariation, volatility

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