考虑投资者情绪的中国股市自激发效应研究

doi:10.16381/j.cnki.issn1003-207x.2018.1001

摘要/Abstract

摘要： 本文在极值理论中引入行为金融学，结合标值自激发点过程（MSEPP）刻画股指收益率极端值序列的集聚性、短期相依性，并将传统的超阈值模型所描述的齐次泊松过程拓展为非齐次泊松过程，探讨投资者情绪对极端收益率的冲击。运用风险偏好指数的方法，基于沪深300指数成份股合成中国投资者情绪指数（EMSI），进一步构建MSEPP-EMSI模型预测沪深300指数、上证综合指数及深圳成分指数的极端风险爆发概率，并对其进行动态ES风险测度。实证结果表明，沪深股市在短期内股指连续暴跌现象时有发生，投资者极度负面情绪会加剧股市的剧烈动荡，当考虑投资者情绪对极端风险的冲击时，MSEPP-EMSI模型能有效的提高对极端风险的概率预测精度及ES预测精度。

关键词: 极值理论, 标值自激发点过程, 股票市场情绪指数, 风险概率预测, ES测度

Abstract: In recent years, extreme events such as continuous soaring and plummeting have occurred frequently in Chinese stock market, and the management of extreme risks in the stock market has been severely challenged. In order to explore the relationship between extreme events and the impact of Investor Sentiment on the extreme returns of the stock market. In this paper, the Process of the Marked Self-exciting Point Process (MSEPP) is used to describe the clustering and short-term dependence of the extreme return series of stock index, and the homogeneous Poisson process with fixed intensity λ described by the traditional Peaks Over Threshold(POT) model is extended to the non-homogeneous Poisson process with time-varying intensity function λ(·). Using the method of risk preference index, the Equity Market Sentiment Index(EMSI) of Chinese stock market is synthesized based on the CSI300 Index Components. Taking EMSI as one of the explanatory variables of the strength function λ(·), the MSE PP-EMSI model is further constructed to predict the extreme risk Outbreak Probability of CSI 300 index, Shanghai composite index and Shenzhen component index during the stock market crash in 2015, and to measure the dynamic Expectd Shortfall(ES) risk of these indexes from June 9, 2017 to March 30, 2018. The empirical results show that the stock indexes of Shanghai and Shenzhen stock markets have plummeted continuously in the short term, and investors' extreme negative emotions will aggravate the violent turbulence of the stock market. When considering the impact of investors' emotions on extreme risks, MSEPP-EMSI model can effectively improve the probability prediction accuracy and ES prediction accuracy of extreme risks. The conclusion of this study reveals the Self-exciting effect of extreme risks in Chinese stock market, and explores the impact of investor sentiment on stock market returns, expanding the research in related behavioral finance fields. At the same time, it can effectively guide the trading behavior of stock market participants, enhance the risk management level of institutional investors in the face of extreme risks, and provide a basis for government regulatory authorities to formulate policies.

Key words: extreme value theory, marked self-exciting point process, stock market sentiment index, risk probability forecast, ES measure

中图分类号:

F830.9

唐振鹏, 吴俊传, 冉梦, 张婷婷. 考虑投资者情绪的中国股市自激发效应研究[J]. 中国管理科学, 2020, 28(7): 1-12.

TANG Zhen-peng, WU Jun-chuan, RAN Meng, ZHANG Ting-ting. Research on The Self-exciting Effect of Chinese Stock Market Considering Investor Sentiment[J]. Chinese Journal of Management Science, 2020, 28(7): 1-12.

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