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基于上证指数高频数据的中国资本市场微观特性研究

唐振鹏 陈尾虹 冉梦

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基于上证指数高频数据的中国资本市场微观特性研究

唐振鹏, 陈尾虹, 冉梦

Microscopic characteristics of Chinese capital market based on the high frequency data of Shanghai composite index

Tang Zhen-Peng, Chen Wei-Hong, Ran Meng
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  • 以上证指数高频数据为研究对象,基于上涨、平缓和下跌三个市场状态分析我国金融市场的微观特性.通过分析上证指数在不同时间间隔下的概率分布、自相关性和多分形三个特性,发现上证指数对数增量序列存在厚尾、列维非高斯分布特征,且随着时间间隔的增大,收益序列愈收敛于正态分布,其中,下降趋势收敛于正态分布的速度更快,拟合于列维分布的效果更好.最为突出的是,在自相关函数分析中,上证指数的收益率无长期记忆性,而波动率则具有较强的记忆性.同时,波动率的自相关性存在明显的周期性特征,即T=240 min,且在下降趋势时其相关性最高.在以时间增量刻画的多重分形结构中,对于不同的时间序列、时间间隔,由于受投资期限和流动性的影响,三种股市状态的收益率波动存在着短期和长期性的差异.上证指数的总体宏观行为与国际成熟股市较为一致,但在微观特性上仍存在显著差异,其所特有的周期性是投资者的惯性反冲所致,而自相关性函数较之成熟股市衰减较慢,则表明投资者的投资行为更多地受历史信息的影响.
    This paper mainly uncovers the typical microscopic characteristics of Chinese capital market in three different stock price stages of rising, steady and falling based on the high frequency data of Shanghai composite index. Firstly, by analyzing the probability distribution of the Shanghai composite index in different time intervals, we clearly find that the logarithmic change of the index presents an obvious heavy tail feature as well as non-Gaussian Levy distribution, and the return series converges to a normal distribution with the increase of the time interval, which becomes more significant especially in the falling stage of stock prices. Secondly, by calculating the autocorrelation function, we observe that unlike the return rate, the fluctuation ratio of Shanghai composite index demonstrates remarkable long memory volatility with a periodicity of about 240 min, and the autocorrelation curve in falling stage is much higher than in rising and steady stages. Thirdly, in the multi-fractal structure, the volatility of return series has significant short-term and long-term differences among three different stages of rising, steady and falling due to the effects of time limitation and liquidity of investment. Finally, the macroscopic behavior of the Shanghai composite index is relatively consistent with that of the international mature stock market, however, the corresponding microscopic characteristics demonstrate significant differences due to the fact that the Chinese capital market is strongly dependent on the macroeconomic policy, investor sentiment, and liquidity levels. It is quite remarkable that the tail distribution of mature stock market is much fatter than that of Chinese stock market because of the special control and limit mechanism of stock prices in China, which finally causes the considerably lower amplitude of price fluctuation. Moreover, it is also found that the attenuation speed of the autocorrelation function in the Chinese capital market is obviously slower than that in the mature stock market, which suggests that the behaviors of investors in Chinese stock market are more likely to be influenced by the historic exchange information. At the same time, the periodicity of autocorrelation function is actually caused by the inertia recoil of investors, which further verifies the information asymmetry of Chinese stock market. Especially, by changing the starting values of the samples, we find that the periodicity of autocorrelation function still remains the same, which indicates that the periodicity characteristic of stock price is not dominated only by the intraday pattern of trading activity. Therefore, the investors should discover the underlying rules of high-frequency data and extract more useful knowledge in order to guide their investment decisions more effectively.
      通信作者: 陈尾虹, tingling69@163.com
    • 基金项目: 国家自然科学基金(批准号:71573042,71171056)和福建省社科基金青年博士项目(批准号:FJ2016C200)资助的课题.
      Corresponding author: Chen Wei-Hong, tingling69@163.com
    • Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 71573042, 71171056) and the Society Science Foundation for Young Ph. D. of Fujian Province, China (Grant No. FJ2016C200).
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    Mantegna R N, Stanley H E 1994 Phys. Rev. Lett. 73 2946

    [16]

    Wang B H, Hui P M 2001 Eur. Phys. J. B 20 573

    [17]

    Issler M, Hller J, Imamoğlu A 2016 Phys. Rev. B 93 081414

    [18]

    Ncheuguim E K, Appiah-Kubi S, Ofori-Dankwa J 2014 Res. Meth. Strateg. Manag. 10 215

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    Mandelbrot B 1974 J. Appl. Probab. 11 437

    [22]

    Zhang T W, Lu W B, Li S 2013 Economist 9 97 (in Chinese) [张腾文, 鲁万波, 李隋 2013 经济学家 9 97]

    [23]

    Wu M C, Huang M C, Yu H C, Chiang T C 2006 Phys. Rev. E 73 019908

    [24]

    Zhou W C, Xu H C, Cai Z Y, Wei J R, Zhu X Y, Wang W, Zhao L, Huang J P 2009 Physica A 388 891

    [25]

    Calvet L E, Fisher A 2008 Multifractal Volatility: Theory, Forecasting, and Pricing (Massachusetts: Acedemic Press) pp23-31

    [26]

    Toth B, Eisler Z, Lillo F, Bouchaud J P, Kockelkoren J, Farmer J D 2012 Quant. Financ. 12 1015

    [27]

    Flimonov V, Sornette D 2012 Phys. Rev. E 85 056108

  • [1]

    Mantegna R N, Stanley H E 1995 Nature 376 46

    [2]

    Chatfield C 2016 The Analysis of Time Series: An Introduction (Boca Raton: CRC Press) pp92-135

    [3]

    Chakraborti A, Toke I M, Patriarca M, Abergel F 2011 Quant. Financ. 11 991

    [4]

    Huang J P 2013 Economy Physics (Beijing: Higher Education Press) pp1-7 (in Chinese) [黄吉平 2013 经济物理学(北京:高等教育出版社)第1-7页]

    [5]

    Zhou R W, Li J C, Dong Z W, Li Y X, Qian Z W 2017 Acta Phys. Sin. 66 040501 (in Chinese) [周若微, 李江城, 董志伟, 李云仙, 钱振伟 2017 物理学报 66 040501]

    [6]

    Parisi D R, Sornette D, Helbing D 2013 Phys. Rev. E 87 012804

    [7]

    Wei Y, Wang P 2008 Physica A 387 1585

    [8]

    Wu P Z, Ma H R 2008 J. Shanghai Jiaotong Univ. 42 147 (in Chinese) [吴斌哲, 马红孺 2008 上海交通大学学报 42 147]

    [9]

    Gao Y C, Zeng Y, Cai S M 2015 J. Stats. Mech. 2015 03017

    [10]

    Qiu T, Zheng B, Ren F, Trimper S 2007 Physica A 378 387

    [11]

    Gao Y C, Cai S M, Wang B H 2012 J. Stats. Mech. 2012 12016

    [12]

    Mandelbrot B B 1963 J. Polit. Econ. 71 421

    [13]

    Sornette D, Knopoff L, Kagan Y, Vanneste C 1996 J. Geophys. Res. 101 13883

    [14]

    Zhou W X 2007 Introduction of Econophysics (Shanghai: Shanghai University of Finance and Economics Press) pp17-33 (in Chinese) [周炜星 2007 金融物理学导论 (上海: 上海财经大学出版社) 第17-33页]

    [15]

    Mantegna R N, Stanley H E 1994 Phys. Rev. Lett. 73 2946

    [16]

    Wang B H, Hui P M 2001 Eur. Phys. J. B 20 573

    [17]

    Issler M, Hller J, Imamoğlu A 2016 Phys. Rev. B 93 081414

    [18]

    Ncheuguim E K, Appiah-Kubi S, Ofori-Dankwa J 2014 Res. Meth. Strateg. Manag. 10 215

    [19]

    Liu S D, Fu Z T, Liu S K 2014 Chin. J. Geophys. 57 2751 (in Chinese) [刘式达, 付遵涛, 刘式适 2014 地球物理学报 57 2751]

    [20]

    Ma F, Wei Y, Huang D, Chen Y 2014 Physica A 405 171

    [21]

    Mandelbrot B 1974 J. Appl. Probab. 11 437

    [22]

    Zhang T W, Lu W B, Li S 2013 Economist 9 97 (in Chinese) [张腾文, 鲁万波, 李隋 2013 经济学家 9 97]

    [23]

    Wu M C, Huang M C, Yu H C, Chiang T C 2006 Phys. Rev. E 73 019908

    [24]

    Zhou W C, Xu H C, Cai Z Y, Wei J R, Zhu X Y, Wang W, Zhao L, Huang J P 2009 Physica A 388 891

    [25]

    Calvet L E, Fisher A 2008 Multifractal Volatility: Theory, Forecasting, and Pricing (Massachusetts: Acedemic Press) pp23-31

    [26]

    Toth B, Eisler Z, Lillo F, Bouchaud J P, Kockelkoren J, Farmer J D 2012 Quant. Financ. 12 1015

    [27]

    Flimonov V, Sornette D 2012 Phys. Rev. E 85 056108

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出版历程
  • 收稿日期:  2017-01-06
  • 修回日期:  2017-04-06
  • 刊出日期:  2017-06-05

基于上证指数高频数据的中国资本市场微观特性研究

  • 1. 福州大学经济与管理学院, 福州 350116;
  • 2. 福建省金融科技创新重点实验室, 福州 350116;
  • 3. 福建省企业发展研究中心, 福州 350116
  • 通信作者: 陈尾虹, tingling69@163.com
    基金项目: 国家自然科学基金(批准号:71573042,71171056)和福建省社科基金青年博士项目(批准号:FJ2016C200)资助的课题.

摘要: 以上证指数高频数据为研究对象,基于上涨、平缓和下跌三个市场状态分析我国金融市场的微观特性.通过分析上证指数在不同时间间隔下的概率分布、自相关性和多分形三个特性,发现上证指数对数增量序列存在厚尾、列维非高斯分布特征,且随着时间间隔的增大,收益序列愈收敛于正态分布,其中,下降趋势收敛于正态分布的速度更快,拟合于列维分布的效果更好.最为突出的是,在自相关函数分析中,上证指数的收益率无长期记忆性,而波动率则具有较强的记忆性.同时,波动率的自相关性存在明显的周期性特征,即T=240 min,且在下降趋势时其相关性最高.在以时间增量刻画的多重分形结构中,对于不同的时间序列、时间间隔,由于受投资期限和流动性的影响,三种股市状态的收益率波动存在着短期和长期性的差异.上证指数的总体宏观行为与国际成熟股市较为一致,但在微观特性上仍存在显著差异,其所特有的周期性是投资者的惯性反冲所致,而自相关性函数较之成熟股市衰减较慢,则表明投资者的投资行为更多地受历史信息的影响.

English Abstract

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