我国股市波动信息沿时间尺度流动的非对称性  

Asymmetry of Information Flow between China's Stock Market Volatilities along Time Scales

作　　者：李志强[1] 涂雄苓[2] 

机构地区：[1]江西财经大学 [2]江西财经大学统计学院

出　　处：《统计研究》2015年第8期77-83,共7页Statistical Research

摘　　要：股票市场中广泛具有短线投资者根据长线投资者的交易行为而执行相应交易的跟庄现象,长线和短线交易分别与不同的时间尺度相关联。经典的时间序列分析方法往往在单一时间尺度条件下展开,无法有效地探索不同时间尺度的股票交易行为间的内存关联。由此,本文引入小波域隐马尔可夫模型,以我国股市5分钟的高频交易数据为素材,研究了股市波动信息沿时间尺度流动的统计性质。结果表明波动信息在传导过程中表现出如下显著的非对称性:大尺度的低波动状态以大概率引发小尺度上的低波动状态,但大尺度的高波动状态只以相对小的概率诱发小尺度的高波动状态。最后,对这一结果的政策意义进行了解释。Following banker is generally phenomenon in stock market. That means that short-term trader＇s operation depends on long-term traders＇. Long or short-term transaction is denominated in a certain time scale for the conditions. The traditional time series analysis,focusing on time series at a given scale,lacks the abilities to explore the links between transaction behaviors at different scales. In this paper,we study the statistical properties of the information flow between China＇s stock market volatilities along time scales,using high-frequency data and wavelet domain hidden Markov model.The results show that there is a significant asymmetry during the volatilities conduction. The asymmetry senses that a low volatility state at a long time horizons is most likely followed by low volatility states at shorter time horizons,while a high volatility state at long time horizon does not necessarily imply a high volatility state at a shorter time horizons. Finally,the policy implications derived from the results are explained.

关 键 词：时间尺度 信息流 行为金融 小波域隐马尔可夫模型 

分 类 号：C812[社会学—统计学]