期现货市场订单流动性层面的“遛狗效应”——基于交易量刻度的高频交易数据研究

doi:10.16381/j.cnki.issn1003-207x.2016.04.003

中国管理科学 ›› 2016, Vol. 24 ›› Issue (4): 19-26.doi: 10.16381/j.cnki.issn1003-207x.2016.04.003

期现货市场订单流动性层面的“遛狗效应”——基于交易量刻度的高频交易数据研究

刘睿智^1,2, 周勇^2,3

1. 上海国际信托有限公司, 上海 200002;
2. 上海财经大学统计与管理学院, 上海 200433;
3. 中国科学院数学与系统科学研究院, 北京 100190

收稿日期:2014-11-26 修回日期:2015-12-04 出版日期:2016-04-20 发布日期:2016-04-29
通讯作者: 刘睿智(1989-),男(汉族),山东泰安人,上海国际信托有限公司,研究员,经济学博士,研究方向:金融市场流动性,E-mail:liuruizhi1989@163.com. E-mail:liuruizhi1989@163.com
基金资助:
国家自然科学基金委重点项目(71331006);自然科学基金委项目(71271128);国家自然科学基金委创新研究群体科学基金(11021161);国家数学与交叉科学中心;上海市重点学科项目;银兴经济研究基金;上海财经大学研究生科研创新基金(CXJJ-2013-473)

“Walking-the-Dog”Effect under Order Liquidity in Futures and Spots Market——Based on High-Frequency Trading Data with Volume-Clock

LIU Rui-zhi^1,2, ZHOU Yong^2,3

1. Shanghai International Trust Co., Ltd, Shanghai 200002, China;
2. Shanghai University of Finance and Economics, Shanghai 200433, China;
3. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China

Received:2014-11-26 Revised:2015-12-04 Online:2016-04-20 Published:2016-04-29

摘要/Abstract

摘要： 在多层次资本市场的大发展趋势下,建立有效的衍生品-现货互补对冲机制是完善金融市场的基本要求。期货-现货体系为投资者提供套期保值风险对冲功能对期货与现货合约的紧密联系程度提出非常高的要求,这不仅应体现在价格上,更应微观的体现在交易过程的订单流动性中。若在任何情形下,订单流动性的趋同能够立刻反应在两类金融证券中,那么异常的基差风险就很难发生,股指期货与现货之间将存在健康的"遛狗效应"。本文以期货现货合约的高频交易数据为基础,构建期货和现货合约的订单流动性,并通过期现货订单流动性传染互动模型的合理性检验期现货合约之间是否在微观订单流动性层面在平常交易日存在紧密的"遛狗效应"。在高频数据模型构建中,使用成交量刻度的衡量方法,并说明了其较时间刻度的优势。在实证研究中,本文使用股指期货和沪深300指数现货的高频交易数据,证明了我国股指期货和现货之间在平常交易日中存在紧密的"遛狗效应"。

关键词: 期、现货市场, 订单流动性, 遛狗效应, 交易量刻度, 高频数据

Abstract: Under the requirement of constructing multi-level capital market, it is the effective derivatives-spots hedging mechanism that contributes to the well-developed financial market in our country. The function of providing risk-hedging for investors under futures-spots system requests more than price and volatility synchronously between them, but at the microscopic level, that is, on the level of order liquidity. The investors such as arbitragers and hedgers under high-frequency circumstances build trading records of stock index future market and spot market together. And whether there is a an arbitrage opportunity, the order liquidity of these two types of assets will change earlier than prices. If in any circumstance, the convergence of order liquidity will be reflected in securities of these two categories, the abnormal basis risk can hardly occur. This makes a contribution to the establishment of healthy "walk-the-dog" effect between stock index and stock index futures. Based on high-frequency trading data of futures and spots, order liquidity has been constructed and an effective method has been provided to test whether there is "walking-the-dog" effect to keep close relation between futures and spots under order liquidity level in usual days. Theorem 1 provides the theoretical foundation of test method in this paper. Volume-clock method is used in high-frequency trading data and dominants to chronic-clock is depicted in this paper. In empirical study, using high-frequency trading data of SS300 index futures and spot index, it's proved that there is closing "walking-dog" relation between index futures and spot index in our market under order liquidity level. It is not easy to achieve a small quantity of profits from high-frequency futures-spot arbitrage by digging high-frequency trading orders. Secondly, the adjustments on changes of stocks' prices imposed by traders' information can be reflected by return measured by trading volume, which is closer to normal distribution than return measured by time.

Key words: futures-spots market, order liquidity, walking-dog effect, volume clock, high frequency data

中图分类号:

F830.9

刘睿智, 周勇. 期现货市场订单流动性层面的“遛狗效应”——基于交易量刻度的高频交易数据研究[J]. 中国管理科学, 2016, 24(4): 19-26.

LIU Rui-zhi, ZHOU Yong. “Walking-the-Dog”Effect under Order Liquidity in Futures and Spots Market——Based on High-Frequency Trading Data with Volume-Clock[J]. Chinese Journal of Management Science, 2016, 24(4): 19-26.

参考文献

[1] Stoll H R,Whaley R E. Program trading and expiration-day effects[J]. Financial Analysts Journal,1987,43(2):16-28.

[2] Stoll H R,Whaley R E. Expiration-day effects of the all ordinaries share price index futures: Empirical evidence and alterbative settlement procedures[J]. Australin Journal of Management, 1997,22(2):139-174.

[3] Robert A J. Derivative security markets, market manipulation, and option pricing theory[J].Journal of Financial and Quantitative Analysis, 1994, 29(2): 241-261.

[4] Chow K C, Yung H H M, Zhang Hua. Expiration day effects: The case of Hong Kong[J]. Journal of Futures Markets, 2003,23(1):67-86.

[5] 姚亚伟,廖士光. 股指期货与股票现货市场竞争关系研究_来自中国的经验证据[J].证券市场导报,2011,(9),43-50.

[6] 方匡南,蔡振忠. 我国股指期货价格发现功能研究[J].统计研究,2012,29(5):73-78.

[7] 周伍阳,李攀艺. 我国股指期货市场期现联动型操纵行为研究[J].财会月刊,2013,(16):21-23.

[8] Hong Yongmiao, Liu Yanhui,Wang Shouyang. Granger causality in risk and detection of extreme risk spillover between financial markets[J]. Journal of Econometrics, 2009,150(2):271-287.

[9] Hong Yongmiao. A test for volatility spillover with applications to exchange rates[J].Journal of Econometrics, 2001, 103(1):183-224.

[10] 张兵,范致镇,李心丹.中美股票市场的联动性研究[J].经济研究,2010,(11):141-151.

[11] 李志辉,王颖.中国金融市场间风险传染效应分析[J].现代财经,2012,(7):20-36.

[12] Easley D, de Prado M L, O'Hara M. The volume clock: Insights into the high frequency paradigm[J].Journal of Portfolio Management, 2012a,39(1):19-29.

[13] Easley D, de Prado M L, O'Hara M. Flow toxicity and liquidity in a high frequent World[J]. Review of Financial Studies, 2012b, 25(5):1457-1493.

[14] Easley D,Engle R F, O'Hara M,et al. Time-varying arrival rates of informed and uninformed traders[J]. Journal of Financial Econometrics, 2008, 6(2):171-207.

[15] Easley D, de Prado M L, O'Hara M.Bulk volume classification.Working Paper, 2012c.

[16] Easley D, de Prado M L, O'Hara M. The microstructure of the flash crash: Flow Toxicity, liquidity crashes and the probability of informed trading[J].Journal of Portfolio Management,2011,37(2):118-128.

[17] 刘文文,张合金.测量高频交易领域中的指令流毒性—基于我国沪深300指数期货的实证研究[J].中国经济问题,2013,(1):81-90.

期现货市场订单流动性层面的“遛狗效应”——基于交易量刻度的高频交易数据研究

“Walking-the-Dog”Effect under Order Liquidity in Futures and Spots Market——Based on High-Frequency Trading Data with Volume-Clock

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	梁超, 魏宇, 马锋, 李霞飞. 我国黄金期货价格波动率预测研究：来自模型缩减方法的新证据[J]. 中国管理科学, 2022, 30(4): 30-41.
[2]	吴鑫育, 李心丹, 马超群. 基于期权与高频数据信息的VaR度量研究[J]. 中国管理科学, 2021, 29(8): 13-23.
[3]	苑慧玲, 徐路, 周勇. 带有市场交易信息和随机微观噪声下的杠杆效应研究[J]. 中国管理科学, 2020, 28(9): 12-22.
[4]	瞿慧, 张壹. 基于波动择时绩效的高维波动率估计量与预测模型研究[J]. 中国管理科学, 2020, 28(5): 62-70.
[5]	马超群, 徐光鲁, 刘伟, 贾钰, 赵新伟. 询价制度改革、知情交易者概率与IPO溢价[J]. 中国管理科学, 2018, 26(8): 1-12.
[6]	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.
[7]	朱学红, 张宏伟, 钟美瑞, 刘海波. 基于高频数据的中国有色金属期货市场量价关系研究[J]. 中国管理科学, 2018, 26(6): 8-16.
[8]	罗嘉雯, 陈浪南. 多国股票市场的高频波动相关性研究[J]. 中国管理科学, 2018, 26(2): 116-125.
[9]	徐光鲁, 马超群, 刘伟, 贾钰. 信息披露与IPO首日回报率[J]. 中国管理科学, 2018, 26(10): 10-19.
[10]	宋亚琼, 王新军. 基于动态估计误差的中国股市波动率建模与预测[J]. 中国管理科学, 2017, 25(9): 19-27.
[11]	吴鑫育, 李心丹, 马超群. 考虑微观结构噪声的非仿射期权定价研究——基于上证50ETF期权高频数据的实证分析[J]. 中国管理科学, 2017, 25(12): 99-108.
[12]	刘志东, 严冠. 基于半鞅过程的中国股市随机波动、跳跃和微观结构噪声统计特征研究[J]. 中国管理科学, 2016, 24(5): 18-30.
[13]	刘志东, 杨竞一. 基于非参数日内跳跃检验和高频数据的公司信息披露对股市价格波动影响研究[J]. 中国管理科学, 2016, 24(10): 22-34.
[14]	唐勇, 林欣. 考虑共同跳跃的波动建模:基于高频数据视角[J]. 中国管理科学, 2015, 23(8): 46-53.
[15]	赵军力, 梁循. 基于TrTS取样的股票收益率RV测度的改进[J]. 中国管理科学, 2015, 23(7): 26-34.