近年来，由于中美经济联系日趋紧密，中美股票市场大幅波动的互激效应明显增强。本文考虑中美股市时差和法定节假日差异等因素，运用标值Hawkes过程对2006-2017年CSI300和S&P500大幅波动收益率数据进行建模，结果表明：（1）中美股市大幅波动互激效应存在不对称性，美股市场大幅波动对中国股市的互激效应更强；（2）中美股市大幅波动的幅度对互激效应不存在显著影响；（3）中美股票市场对于大幅波动互激效应的消化速度存在差异，中国股票市场消化美股大幅波动互激效应的速度较快。本研究对金融市场监管者和投资者均有一定意义。
In this paper, large volatility mutually exciting effects between the Chinese stock market and the U.S. stock market are discussed under the circumstances that the two markets are linked much more closely than ever before. Over the past ten years, there are more obvious coordinative price movements between the Chinese and the U.S. stock market, especially in large volatility price movements. For example, in October 27, 2008, CSI300 index fell 7.12%. Stimulated by the fall of A shares, the United States stock market S&P 500 index also fell 3.17% after the opening of the stock market. Large volatility mutually exciting effect is that a large price fluctuation in a certain market can be transmitted to other markets through various channels, and then trigger the sharp fluctuation of asset prices in other markets. The theory foundation of large volatility mutually exciting effect is the positive feedback mechanism and the contagion of the financial crisis. There are generally three traditional research methods on the volatility spill-over effect:Multivariate GARCH Model, Copula and VAR. However, there are some defects in these methods. Hawkes process is a special path dependent point stochastic process, which is compatible for modeling large volatility mutually exciting effect, the Hawkes process is use to model the mutually exciting effect of CSI300 and S&P500 large fluctuations from 2006-2017. It is found that (1) The large volatility mutually exciting effect between Chinese stock market and U.S. stock market is asymmetric, and U.S.stock market large volatility exciting effect towards Chinese stock market is stronger;(2) The amplitude of large volatility has no significant influence on the mutually exciting effect;(3) The duration of mutually exciting effect between the stock markets in China and U.S. is different. And the duration of mutually exciting effect from Chinese stock market to U.S. stock market is longer. The result has some implications for financial market regulators and investors.
[1] 李红权, 汪寿阳, 马超群. 股价波动的本质特征是什么?——基于非线性动力学分析视角的研究[J]. 中国管理科学, 2008, 16(5):1-8.
[2] 张一, 刘志东. 异质交易行为主体下的金融传染机制及效应研究[J]. 中国管理科学, 2017,25(9):37-45.
[3] 丁逸俊, 冯芸. 极端下跌事件的正反馈效应与监管限制溢出[J]. 中国管理科学, 2017,25(9):81-96.
[4] 曹栋, 张佳. 基于GARCH-M模型的股指期货对股市波动影响的研究[J]. 中国管理科学, 2017, 25(1):27-34.
[5] 董秀良,曹凤岐. 国内外股市波动溢出效应-基于多元GARCH模型的实证研究[J]. 数理统计与管理,2009,28(6):1091-1099.
[6] 王鹰翔, 张鲁欣. 基于向量GARCH模型的国际证券市场波动溢出研究[J]. 管理评论, 2011, 23(6):49-53.
[7] 雷钦礼,陈晓蒙. 中、印、美股市联动差异性研究[J]. 统计与信息论坛,2017,32(4):41-50.
[8] Grobys K. Have volatility spillover effects of cointegrated European stock markets increased over time?[J]. The Review of Finance and Banking,2010,2(2):83-94.
[9] 过新伟. 沪港股市收益波动性的实证研究[J]. 金融经济, 2009,(4):82-84.
[10] 吕雷,何帆,穆献中. A股与美股、港股联动性的两次比较[J]. 金融发展研究,2016,(12):9-16.
[11] 曾志坚, 徐迪, 左楠. 金融危机对证券市场波动溢出的影响研究[J]. 财经理论与实践, 2011, 32(6):48-52.
[12] 田光, 张瑞锋. 基于Copula的股票市场波动溢出分析[J]. 财经理论与实践, 2011, 32(6):53-58.
[13] Hawkes A G, Oakes D. A cluster process representation of a self-exciting process[J]. Journal of Applied Probability, 1974,11(3):493-503.
[14] Ogata Y, Akaike H. On linear intensity models for mixed doubly stochastic poisson and self-exciting point processes[J]. Journal of the Royal Statistical Society, 1982, 44(1):102-107.
[15] Brillinger D R. Some statistical methods for random process data from seismology and neurophysiology[J]. Annals of Statistics, 1988, 16(1):1-54.
[16] Lewis E, Mohler G, Brantingham P J, et al. Self-exciting point process models ofcivilian deaths in Iraq[J]. Security Journal, 2012, 25(3):244-264.
[17] Hardiman S J, Bouchaud J P. Branching-ratio approximation for the self-exciting Hawkes process[J]. Physical Review E Statistical Nonlinear & Soft Matter Physics, 2014, 90(6):062807.
[18] 吴奔,张波. Hawkes过程分支比估计——一种简单的非参数方法[J]. 统计研究,2015,(3):92-99.
[19] Hamao Y, Masulis R W, Ng V. Correlations in price changes and volatility across International stock markets[J]. Review of Financial Studies, 1990, 3(2):281-307.
[20] Aït-Sahalia Y,Cacho-Diaz J, Roger, J.A. Modeling financial contagion using mutually exciting jump processes[J]. Journal of Financial Economics, 2015, (117):585-605.
[21] 马勇, 张卫国, 闫杜娟. 中国股市暴涨暴跌的交互作用及其预测[J]. 系统管理学报, 2014, 23(5):755-760.
[22] Mcneil A J, Frey R. Estimation of tail-related risk measures for heteroscedastic financial time series:an extreme value approach[J]. Journal of Empirical Finance, 2000, 7(3-4):271-300.
[23] Daley D J, Vere-Jones D.An introduction to the theory of point processes:Volume Ⅱ:General thoery and structure[M]. New York:Springer,2007.
