中国管理科学 ›› 2022, Vol. 30 ›› Issue (2): 1-13.doi: 10.16381/j.cnki.issn1003-207x.2020.0337
• 论文 • 下一篇
刘志东, 赵致远
收稿日期:
2020-03-03
修回日期:
2020-05-25
出版日期:
2022-02-20
发布日期:
2022-02-20
通讯作者:
February,2022
E-mail:liu_phd@163.com
基金资助:
LIU Zhi-dong, ZHAO Zhi-yuan
Received:
2020-03-03
Revised:
2020-05-25
Online:
2022-02-20
Published:
2022-02-20
Contact:
刘志东(1973-),男(汉族),内蒙古赤峰人,中央财经大学管理科学与工程学院,博士,教授,博士研究生导师,研究方向:金融工程与金融计量,Email:liu_phd@163.com.
E-mail:liu_phd@163.com
Supported by:
摘要: 指令驱动市场中,交易者对委托指令的提交和撤销往往表现出非平稳性和集聚性特征。量化评价限价指令簿事件间的激励关系,是探究限价指令簿动态演化的基础,指导交易者行为决策的重要参照。本文利用我国股票逐单委托数据重建了实时演化的限价指令簿,基于状态依赖Hawkes过程分析了不同市场状态下各类限价指令簿事件的自激励和互激励效应。共采用三种不同设定的状态依赖Hawkes过程模型进行实证分析,并探讨了我国股市中交易者的行为特征。实证结果表明,总体上我国股市中限价指令簿事件的自激励效应强于互激励效应,且不同市场状态下存在显著差异。激进事件所产生的互激励效应更为强烈,通过激进程度划分事件类型更能反映事件冲击和交易者的信息学习行为。
中图分类号:
刘志东, 赵致远. 基于状态依赖Hawkes过程的我国股市限价指令簿事件激励效应研究[J]. 中国管理科学, 2022, 30(2): 1-13.
LIU Zhi-dong, ZHAO Zhi-yuan. Research on Excitation Effect among Limit Order Book Events in Chinese Stock Market Based on State-dependent Hawkes Processes[J]. Chinese Journal of Management Science, 2022, 30(2): 1-13.
[1] Gould M D, Porter M A, Williams S, et al. Limit order books[J]. Quantitative Finance, 2013, 13(11): 1709-1742. [2] 刘祥东,刘澄,刘善存,等.羊群行为加剧股票价格波动吗?[J].系统工程理论与实践, 2014, 34(6): 1361-1368.Liu Xiangdong, Liu Chen, Liu Shancun, et al. Does herd behavior increase stock price volatility?[J]. Systems Engineering—Theory&Practice,2014, 34(6): 1361-1368. [3] 郑丰,赵文耀,张蜀林.基于Agent的羊群行为研究[J].中国管理科学,2015,23(S1):424-429.Zheng Feng, Zhao Wenyao, Zhang Shulin. A study of herd behavior and market volatility based on agent-based modeling[J].ChineseJournalof ManagementScience, 2015,23(S1):424-429. [4] 朱菲菲,李惠璇,徐建国,等. 短期羊群行为的影响因素与价格效应——基于高频数据的实证检验[J]. 金融研究, 2019, 469(7): 191-206.Zhu Feifei, Liu Huixuan, Xu Jianguo, et al. Determinants and Pricing Effects of Short一term Herd Behavior:An Empirical Test Based on High一Frequency Data[J]. JournalofFinancialResearchinChina,2019, 469(7): 191-206. [5] Eisler Z, Bouchaud J P, Kockelkoren J. The price impact of order book events: market orders, limit orders and cancellations[J]. Quantitative Finance, 2012, 12(9): 1395-1419. [6] Van Ness B F, Van Ness R A, Watson E D. Canceling liquidity[J]. Journal of Financial Research, 2015, 38(1): 3-33. [7] Large J. Measuring the resiliency of an electronic limit order book[J]. Journal of Financial Markets, 2007, 10(1): 1-25. [8] Bacry E, Muzy J F. First-and second-order statistics characterization of Hawkes processes and non-parametric estimation[J]. IEEE Transactions on Information Theory, 2016, 62(4): 2184-2202. [9] Lu X, Abergel F. High-dimensional Hawkes processes for limit order books: Modelling, empirical analysis and numerical calibration[J]. Quantitative Finance, 2018, 18(2): 249-264. [10] Muni Toke I, Pomponio F. Modelling trades-through in a limited order book using Hawkesprocesses[J].Economics,2012,6(1):2012 -22. [11] Gonzalez F, Schervish M. Instantaneous order impact and high-frequency strategy optimization in limit order books[J]. Market Microstructure and Liquidity, 2017, 3(02): 1850001. [12] 沈红波,曹军,王雅莉. 中国股票市场限价指令簿的信息含量研究[J]. 投资研究, 2012, 31(9): 113-126.Shen Hongbo, Cao Jun, Wang Yali. Research on the information content of limit order book in Chinese stock market[J]. Review of Investment Studies, 2012, 31(9): 113-126. [13] 刘红忠,叶军. 限价指令簿的价格发现功能[J]. 复旦学报(社会科学版),2012(2):41-48.Liu Hongzhong, Ye Jun. The price discovery of the limit order book[J]. Fudan Journal(Social Sciences),2012(2):41-48. [14] 许启发,蔡超,蒋翠侠.指令不均衡与股票收益关系研究——基于大规模数据分位数回归的实证[J].中国管理科学,2016,24(12):20-29.Xu Qifa, Cai Chao, Jiang Cuixia.An analysis of the relationship between order imbalance and stock returns through quantile regression approach for large-scale data[J].ChineseJournalof ManagementScience,2016,24(12):20-29. [15] Wu P, Rambaldi M, Muzy J F, et al. Queue-reactive Hawkes models for the order flow[J]. Working Paper, UniversitéParis-Dauphine, 2019. [16] Huang W, Lehalle C A, Rosenbaum M. Simulating and analyzing order book data: The queue-reactive model[J]. Journal of the American Statistical Association, 2015, 110(509): 107-122. [17] 周平,马景义,张辛连. 限价指令簿与股票价格:信息扩散与动态反馈机制——来自沪深两市高频数据的证据[J]. 投资研究,2016,35(9):41-54.Zhou Ping, Ma Jingyi, Zhang Xinlian. Limit book and stock price: Information diffusion and dynamic feedback mechanism—evidence from high-frequency data of Shanghai and Shenzhen stock markets[J]. Review of Investment Studies,2016,35(9):41-54. [18] Morariu-Patrichi M, Pakkanen M S. Hybrid marked point processes: Characterisation, existence and uniqueness[R]. Working Paper,Imperial College London, 2018. [19] Morariu-PatrichiM, Pakkanen M S. State-dependent Hawkes processes and their application to limit order book modelling[R]. Working Paper,Imperial College London, 2018. [20] 瞿慧,周慧.引入跳跃与联跳强度的沪深300股指期货套期保值研究[J].中国管理科学, 2016, 24(S1): 454-460.Qu Hui, Zhou Hui.Including jump and co-jump intensities to improve the dynamic hedging of CSI 300 index futures[J]. ChineseJournalof ManagementScience,2016, 24(S1): 454-460. [21] 刘志东,郑雪飞.基于Hawkes因子模型的股价共同跳跃研究[J].中国管理科学,2018,26(7):18-31.Liu Zhidong, Zheng Xuefei. A study of stock price co-jumps with Hawkes factor model[J].Chinese Journal of Management Science,2018,26(7):18-31. [22] 汪冬华,张裕恒.基于Hawkes过程中美股市大幅波动互激效应的研究[J].中国管理科学, 2018, 26(7): 32-39.Wang Donghua, Zhang Yuheng. Research on large volatility mutually exciting effect of Chinese and American stock markets based on Hawkes process[J].ChineseJournalof ManagementScience,2018, 26(7): 32-39. [23] Ozaki T. Maximum likelihood estimation of hawkes’ self-exciting point processes[J]. Annals of the Institute of Statistical Mathematics, 1979, 31(1): 145-155. [24] Daley D J, Vere-Jones D. An introduction to the theory of point processes[M]. Second edition, New York: Springer, 2003. [25] Hendricks D, Gebbie T, Wilcox D. Detecting intraday financial market states using temporal clustering[J]. Quantitative Finance, 2016, 16(11): 1657-1678. [26] Cartea , Jaimungal S. Incorporating order-flow into optimal execution[J]. Mathematics and Financial Economics, 2016, 10(3): 339-36 [27] Hendricks D. An online adaptive learning algorithm for optimal trade execution in high-frequency markets[D]. Johannesburg: University of the Witwatersrand, 2016. |
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